Executive takeaways

Supply chain leaders are right to automate repetitive planning, procurement, transportation, and inventory work. The danger starts when companies assume that because AI can perform more junior tasks, they need fewer junior people.

The latest labor-market evidence suggests that this substitution is already reshaping early careers.

Stanford Digital Economy Lab researchers report that the employment shortfall for workers ages 22 to 25 in highly AI-exposed occupations widened from 15% in the July 2025 data vintage to 19% by June 2026. The widening is driven primarily by reduced hiring of young workers.

Supply chains should be especially cautious about that trend because much of the field’s value comes from handling exceptions. Forecasts are easy until demand changes suddenly. Procurement is straightforward until a supplier misses a commitment. Transportation planning works until weather, labor, capacity, customs, or a customer requirement disrupts the plan.

SCMR recently argued that AI is changing supply chain skills and talent and that companies need to redesign early-career development paths. That should become an operating requirement, not merely an HR recommendation.

Automate the task, not the entry-level job

A junior planner should no longer spend hours assembling a routine forecast that AI can generate faster. But that planner should still exist. Give the employee the forecast and ask what assumption would break it. Have the employee investigate where the system’s historical pattern no longer matches current business conditions.

A junior buyer should not manually compare every supplier quote. Let AI normalize the proposals. Then have the buyer analyze supplier reliability, switching costs, operational dependencies, capacity risk, and the consequences of a seemingly cheaper choice.

A transportation analyst should not spend all day producing standard route plans. Let the system optimize the baseline. Then give the employee the disrupted network and require a decision that balances service, cost, inventory, customer priority, and operational reality.

Those are stronger entry-level jobs than the ones automation removes.

They also address a basic supply chain truth: resilience comes from judgment under imperfect information. AI can process more data than a human. It does not eliminate the need for people who know when the data is incomplete, when a supplier’s promise is unrealistic, when a customer constraint matters more than the mathematical optimum, or when a small operational problem is about to become a large one.

SCMR’s recent coverage of AI-enabled supply chain operations makes the same broader point: technology only works when organizations strengthen coordination, decision-making, visibility, and workforce capabilities.

Make independent competence the new talent metric

The missing metric is time to independent competence.

How quickly can a new planner explain why the AI forecast is wrong? How soon can a buyer make a credible sourcing recommendation when cost, resilience, and supplier performance point in different directions? When can an analyst manage a disruption without a senior employee rewriting the plan?

If AI shortens those learning curves, it is solving two problems at once: productivity and talent development.

If AI instead reduces junior hiring, supply chain organizations may save money while weakening the bench that produces future category managers, planning leaders, logistics directors, and chief supply chain officers.

That risk compounds across the industry. One company can decide to hire only experienced people. The entire industry cannot. Someone has to create the experienced people everyone later wants.

The widening 15% to 19% early-career gap says the collective pipeline is already under pressure.

Turn every AI deployment into an apprenticeship

Supply chain leaders should therefore treat every AI deployment as both a technology project and an apprenticeship redesign. Automate the routine work. Capture expert overrides. Turn disruptions into teaching cases. Require junior employees to explain disagreements with the system. Rotate them across planning, procurement, logistics, and operations so they understand how one decision creates consequences elsewhere.

The future of AI adoption at work in supply chains should not be fewer chances to enter the profession. It should be a faster path from beginner to capable decision-maker.

About the author

Gleb Tsipursky, PhD, is a behavioral scientist, CEO of Disaster Avoidance Experts, and author of The Psychology of AI Adoption at Work: From Resistance to Results (Georgetown University Press, 2026).

Executive takeaways

Economists are finding it increasingly difficult to predict the direction of the economy. One reason is the growing importance of the so-called K-shaped economy, a concept popularized during the COVID-19 pandemic.

Initially, the K shape illustrated how industries experienced sharply different recoveries. Some sectors rebounded quickly and expanded, forming the upper arm of the K, while others struggled and recovered much more slowly, forming the lower arm. Today, the concept increasingly describes a structural divide in consumer income and spending.

The upper part of the K represents higher-income consumers, who are generally better positioned to absorb inflation and maintain discretionary spending. The lower part represents lower-wage consumers, who are more exposed to rising costs for housing, food, energy, and other basic needs. While affluent households may continue spending, lower-income households may reduce purchases, trade down to less expensive products, or eliminate nonessential expenses altogether.

This divergence creates a significant challenge for traditional economic models. Many models rely on averages for income, spending, inflation, and unemployment. However, an economy that appears healthy on average may conceal substantial weakness within particular income groups, product categories, or geographic markets.

Why the K shape complicates forecasting

A K-shaped economy makes forecasting more difficult because different consumer groups respond differently to the same economic conditions. Interest rates, inflation, wage growth, and changes in employment may have a limited effect on affluent consumers while creating considerable pressure on lower-income households.

Aggregate results can therefore be misleading. Growth in premium products may offset declining demand in value or mid-tier categories, hiding important changes inside the overall numbers. Premium and value products may even grow simultaneously while mid-priced products decline. Demand can also vary significantly by region, depending on employment patterns, local industries, household income, and living costs.

Companies can no longer assume that a single macroeconomic indicator will affect every customer or product in the same way. The economy has become more heterogeneous, and supply chain planning must reflect that complexity.

Resilience begins with segmentation

This environment is developing alongside persistent supply chain disruptions. The pandemic, material shortages, supplier shutdowns, transportation constraints, energy-market volatility, geopolitical tensions, and other events have demonstrated that stability cannot be taken for granted.

The starting point for resilience is segmentation. Companies need to understand where their products, customers, and markets sit within the K-shaped economy. They must identify which categories are benefiting from stronger spending, which are under pressure, and which could move rapidly as economic conditions change.

Organizations must also acknowledge that they cannot make every part of the business equally flexible or resilient. Attempting to protect every product, material, and supplier relationship would be prohibitively expensive. Some exposure is unavoidable. The objective is to protect the areas that are most critical to profitability, customer relationships, and operational continuity.

Segmentation should consider factors such as margin contribution, demand volatility, customer importance, substitution options, supply risk, lead time, and geographic exposure. This creates a stronger foundation for deciding where resilience investments will generate the greatest value.

Forecasting at the segment level

In the past, companies could often apply broad economic indicators across the business or use historical trends to estimate future growth or decline. In a polarized economy, those methods may no longer provide sufficient detail.

Forecasting models should be refined by product, customer, income group, channel, and geography whenever the available data supports that level of analysis. A national consumer-spending forecast, for example, may have limited value if premium demand is rising in one region while value-oriented demand is weakening somewhere else.

Forecast accuracy should also become an organization-wide priority. It is not solely the responsibility of demand planning or supply chain teams. Poor forecasts can create excess inventory, product shortages, manufacturing inefficiencies, wasted materials, inaccurate financial projections, and higher transportation costs. The effects can spread across supply chain, finance, sales, procurement, and manufacturing.

Companies should continuously measure forecast accuracy, identify recurring sources of error, and refine their assumptions. Planning for continued economic polarization can be a useful baseline, but it should not become a fixed conclusion. Forecasts must remain flexible enough to recognize when consumer behavior begins to change.

Connecting segmentation with scenario planning

Segmentation and scenario planning must work together. Companies should develop a manageable number of credible scenarios that reflect how the K-shaped economy could evolve. These might include continued premium growth, increased trading down among middle-income consumers, regional demand deterioration, higher input costs, or constraints involving critical suppliers and materials.

Each scenario should answer practical questions. What inventory levels would be required? Which supplier or material constraints could emerge? How would the cost to serve change? Which products deserve stronger protection? Where could inventory be reduced and additional risk accepted? Which materials require multiple sources to create flexibility?

Every scenario should also include an actionable playbook. A common planning failure is to create numerous scenarios without defining what the organization will do if any of them occur. Effective playbooks establish decision triggers, responsibilities, response options, and expected financial consequences. They convert scenario planning from an analytical exercise into an operational capability.

Improving long-term decisions

Many supply chain decisions are costly, difficult to reverse, and dependent on long lead times. Adding production capacity, qualifying a new supplier, relocating sourcing, increasing strategic inventory, or redesigning a distribution network may require commitments based on uncertain demand projections.

For that reason, organizations should evaluate the confidence level associated with each forecast and scenario. Leaders need to understand not only the expected outcome but also the range of possible outcomes and the risks attached to each one.

Scenario planning allows companies to evaluate trade-offs before conditions force an immediate response. Higher inventory may improve service and reduce stockout risk but increase working capital and obsolescence. Local suppliers may be more expensive but provide shorter lead times and greater reliability. Multiple sourcing may improve flexibility but add complexity and reduce purchasing leverage. These decisions should be assessed by segment rather than applied uniformly across the business.

Combining AI with human experience

Artificial intelligence can perform much of the analytical heavy lifting. It can process large datasets, identify patterns across demand segments, evaluate economic indicators, and generate potential scenarios more quickly than traditional methods.

However, AI-generated outputs still require interpretation. Experienced supply chain professionals understand operational constraints, supplier behavior, customer priorities, and risks that may not be fully represented in the data. Their judgment is essential when converting analysis into long-term decisions.

Resilience in a K-shaped economy ultimately depends on combining detailed segmentation, accurate forecasting, disciplined scenario planning, and experienced human judgment. Companies that develop these capabilities will be better equipped to protect profitability, allocate resources intelligently, and respond when different parts of the market move in opposite directions.

About the author

Vinicius Giarola is the Executive Director of Consumer Supply Chain, Logistics & Supply Chain at Bridgestone Americas, Inc.

Executive takeaways

A supply chain control tower can show hundreds of late orders, constrained parts and shifting arrival dates before breakfast. The dashboard may be accurate. The predictions may be sophisticated. Yet the planner still faces the same question: Which problem should I work first?

That question is where many AI business cases become fragile. Organizations invest in better forecasts and faster alerts, but the operating model around exceptions remains manual. Every warning enters the same queue. Teams investigate the same facts in different systems. The loudest customer or the most senior escalation often determines priority.

Supply chain AI earns its return when it changes that daily decision. The real product is not another prediction. It is an exception queue that helps people understand impact, choose the next action and learn from the outcome.

An alert is not an exception

An alert says that a threshold was crossed. An exception says that a business commitment is at risk and requires a decision.

The distinction matters. A shipment that is two days late may have no customer impact because inventory is available at the destination. A four-hour delay may stop a production line. Treating both events equally creates noise and trains planners to ignore the system.

A useful exception record connects the signal to the business context: affected order, customer promise, inventory position, production dependency, financial exposure and recovery window. Without that context, AI only accelerates awareness. It does not accelerate action.

Rank by consequence and time to recover

Most teams naturally rank exceptions by severity. They should add a second dimension: time to recover.

Some problems are expensive but can wait. Others look small but will become irreversible within hours. A part shortage may be manageable before the production sequence is frozen. A carrier delay may be recoverable before a cutoff time. A supplier quality issue may require immediate containment even when no customer order is late yet.

The queue should therefore answer two questions for every exception: What happens if no one acts, and how long does the team have before the best recovery option disappears? That combination turns a list of risks into a work plan.

Show the evidence behind the recommendation

Planners will not trust a recommended action if they cannot see why the system proposed it. The explanation does not need to expose every model calculation. It does need to show the operational evidence.

If the recommendation is to expedite, the planner should see the customer commitment, available inventory, alternate lanes, cutoff time and cost difference. If the recommendation is to hold, the planner should see the buffer that makes delay safe.

This is especially important when data conflicts. The system may see one arrival date in transportation, another in the supplier portal and a third in the ERP. A confident recommendation built on unresolved conflict is more dangerous than a cautious request for review.

Design the queue around decisions

A practical exception queue should separate four states. The first is observe: the risk is visible, but no action is required. The second is investigate: evidence is incomplete or inconsistent. The third is decide: viable recovery options exist and an owner must choose. The fourth is execute: the action is approved and needs to move across systems and partners.

These states prevent the queue from mixing information work with decision work. They also make ownership visible. A data steward may resolve conflicting attributes. A planner may select a recovery option. Procurement may negotiate with a supplier. Logistics may execute the change.

When every exception has a state, owner and next decision, leaders can see where work is stuck instead of merely seeing how many alerts exist.

Capture the recovery outcome

Many systems close an exception when the alert condition disappears. That is not enough. The organization needs to know what action was taken and whether it worked.

Did expediting protect the customer promise? Did a substitute part create a quality issue later? Did the planner reject the recommendation because the inventory data was stale? Did the team discover that the alert arrived after the recovery window had closed?

Those outcomes are training data for the operating model, not only for the algorithm. They reveal broken master data, unrealistic thresholds, unclear approval rights and supplier processes that create recurring failure.

Start with one expensive exception family

Organizations do not need to redesign every planning process at once. Start with an exception family that is frequent, costly and recoverable: supplier commit changes, late inbound shipments, inventory allocation conflicts or production constraints.

Map the evidence planners gather, the decisions they make, the approvals they need and the actions that follow. Then measure whether the new queue reduces time to decision, repeated investigation, missed recovery windows and unnecessary premium cost.

This approach keeps the AI program close to operational value. It also builds confidence because planners can see the system improving a decision they already understand.

The operating system for disruption

Supply chains will never eliminate exceptions. Volatility, data gaps and competing priorities are part of the work. The advantage comes from seeing the important exception sooner and responding while options still exist.

A forecast tells the organization what may happen. An exception queue tells people what deserves attention now, why it matters and what can still be done. That is the point where AI moves from an analytical feature to an operating capability—and where the return begins to show.

About the author

Hemang Upadhyay is a senior product and AI leader with more than 16 years of experience across enterprise AI strategy, digital commerce, product data governance, customer experience and scalable platform transformation. His writing focuses on the operating controls that help organizations move AI from promising demonstrations into reliable business workflows. LinkedIn: https://www.linkedin.com/in/hemang-up/ Website: https://www.hemangai.com

Those cabs and related components are then moved from Kings Mountain into Volvo Group’s production network in Virginia and Pennsylvania, where they have to arrive on time and in the right sequence to keep production on schedule. Any delay in that flow can affect build plans, customer commitments, and the broader truck market that Volvo Group serves.

Before Volvo Group acquired the Kings Mountain operation, the site was part of the company’s supplier network. Volvo initially set out to help the supplier improve the flow of materials and get cab production back on track. Starting with an assessment of the logistics operation, Volvo looked at the constraints around the site and what it would take to support better movement in and out of the facility.

“We were looking at it as a supplier issue at first,” says Dave Yancey, project manager, production logistics, at Volvo Group North America. “They were behind on our cab production, and it was affecting our markets. We sat down to assess the operation and figure out how to fix the logistics flow, including the warehouse capacity we needed to support the site.”

A familiar partner for a fast-moving problem

Volvo needed a logistics partner that could step in quickly, understand its production environment, and help stabilize the warehouse side of the operation. The company had options, but this project didn’t come with months to plan from scratch. Material was already coming in, cab production needed support, and the Kings Mountain site was still changing.

A. Duie Pyle (ADP) already had a track record with Volvo Group, having provided regional less-than-truckload (LTL) service and value-added logistics support. It also handled cab storage for Volvo Group’s Mack Trucks operation in Macungie, Pa., during an earlier production challenge.

“When I was in Macungie, we had a major success story with the ADP team,” Yancey says. “They were storing our cabs at the time and helped us through some production issues. I liked the system they used and thought this would be a perfect model.”

A nine-day sprint

What started out as a supplier-support effort became part of a larger transition when Volvo Group acquired the Kings Mountain operation. ADP already had agreements in place with the supplier, so the acquisition added contract complexity at the same time the warehouse had to keep receiving, organizing and moving material.

“Once we got the agreements straightened out, it was full speed ahead,” Yancey says. “The attitude from the ADP team was: ‘Tell us what we need to do and we’ll make it happen.’”

And with that, Volvo took over a 180,000-square-foot warehouse that still needed the basics before it could fully support the operation. Material had to be received, staged, and stored while utilities, equipment, and warehouse processes were still being put in place.

“It started as just a warehouse with no electricity,” says Yancey. “We used pallet jacks to unload inbound material, and then we started getting power, forklifts, racking, shelving, and scanning. It went from being an empty shell to a functioning warehouse.”

Working together, Volvo and ADP moved the initial material into the warehouse over roughly nine days, even as new inbound shipments continued to arrive. Once electricity, forklifts, and other equipment were in place, the operation started to function more like a true warehouse. By the following spring, ADP and Volvo were adding racking to increase storage capacity and make better use of the space.

Yancey says ADP brought in a capable team to help Volvo get through that stretch. “We have a lot of good people inside Volvo Group, but we can also be spread very thin,” Yancey says. “ADP was able to jump through hoops, make things happen and bring in the right people to get this done.”

Building the warehouse around the parts

With this project, both Volvo and ADP had to carefully assess where parts belonged inside the warehouse and how quickly those parts needed to move. The operation was organized around truck model and product line, including Class 8 highway trucks, Class 6 trucks and cab-over models.

Each had its own parts profile, and some of the existing supplier’s part numbers didn’t follow Volvo’s normal numbering conventions. “This added complexity to the setup,” says Yancey.

As production patterns became easier to see, ADP and Volvo used that information to set up more directed putaway and improve turn times. “We set the operation up by model so we could move the fast-moving parts to the front and make our turn time quicker,” Yancey says. “If something didn’t move as often, we could put it deeper in the warehouse.”

There were also repacking requirements to consider because some of the manufacturer’s parts came in on one-way pallets or in packaging that didn’t match its standards. Some pallets also arrived with multiple part numbers or incomplete identification, which made them harder to receive, store and move efficiently. ADP had to move those parts into Volvo-approved containers before they could move through the warehouse and back to the plant.

Finally, the new warehouse had to meet Volvo’s compliance requirements, including both ISO and environmental standards. That meant handling cardboard, plastic, wood pallets and other waste streams properly rather than sending everything to a landfill. It added another layer of discipline during the early months, when ADP and Volvo were still improving the physical setup, training new employees, and bringing the warehouse up to Volvo’s standards.

Finding Its footing quickly

Going into the project, Volvo’s supplier had its own warehouse system, but ADP could generate inventory reports multiple times a day and help Volvo compare physical inventory against what the plant expected to see. That gave the teams a better handle on inventory accuracy while the operation was still settling in.

Volvo and ADP also built key performance indicators (KPIs) around the parts of the operation Yancey wanted to measure, including repacking activity, pallet handling and inbound and outbound volume. The prior day’s KPIs were available by noon the next day, followed by weekly and monthly summaries. That gave the team a way to spot bottlenecks, track warehouse performance, and make decisions as the operation matured.

With its first wave of inbound material under control, the newly-acquired operation started to find its footing. Power came online within the first few days, forklifts followed and the warehouse began taking on the pieces it needed to function properly, from racking and shelving to scanning.

By the following spring, ADP and Volvo were adding more racking to increase storage capacity and make better use of the space. “The facility went from being a shell to a functioning warehouse,” says Yancey. “The early stages of the transition were challenging, but overall it was a huge turnaround. The warehouse runs extremely well.”

A relationship built on trust

For ADP, Volvo’s Kings Mountain project grew out of a relationship built on both execution and trust. Chris Incudine, VP of solutions design at ADP, works closely with Yancey and says Volvo was asking the company to step into an unusual situation: help take over a supplier’s logistics operation because the manufacturer needed help stabilizing it.

“If you take the relationship out of it, you basically have a customer asking us to go run their supplier’s operation for them because they had more trust in us than they did in the supplier,” Incudine says. “That’s a unique scenario. There has to be a level of trust there, and that trust came from the partnership we’d already built.”

ADP, which wasn’t operating in North Carolina at the time, also had to step outside its own regional network to support the project. Incudine says the strength of the Volvo relationship made the decision possible. Regional LTL carriers like ADP can be a strong fit for this kind of work because they tend to know their customers closely, understand the freight and can respond quickly when a transportation issue becomes a broader supply chain challenge.

“We wouldn’t have been in North Carolina three years ago if we hadn’t gotten an ask from such a strong partner,” Incudine says. “I think it speaks to the level of partnership and trust we’d built through execution and transparency.”

That transparency had to flow both ways, of course. He says Volvo was direct about what it needed, while ADP was equally direct about what was working, what needed attention, and where the operation needed support. That open communication helped both companies work through a project that was anything but simple. “It wasn’t all easy. There was blood and sweat—and some tears,” he says, laughing, “but we got Volvo what they needed.”

Executive takeaways

Food and beverage supply chains operate under a particularly unforgiving set of demands. Products must move through complex networks quickly and accurately, inventory decisions can carry shelf-life implications, and changing consumer expectations require companies to support new channels without losing control of cost or service.

Those pressures are making artificial intelligence, automation and better-connected fulfillment operations increasingly important across the sector. They will also make food and beverage one of the industry paths attendees can follow at the 2026 NextGen Supply Chain Conference, taking place Oct. 21-23 at the W Nashville in downtown Nashville.

Across the conference’s awards program, main-stage panel discussions and interactive Small Group Sessions, leaders from Mars Snacking, Southern Glazer’s Wine & Spirits, Target and Berry Direct will offer practical perspectives on how food and beverage organizations are improving decisions, modernizing distribution and responding to a more demanding fulfillment environment.

Mars turns AI into faster supply chain decisions

The food and beverage conversation will begin Thursday morning when Mars Snacking receives the NextGen Supply Chain End User Award for Intelligent Transformation.

Kristen Daihes, senior vice president of analytics, digital and data at Mars Snacking, will represent the company during the awards program and discuss how Mars is embedding artificial intelligence into supply chain decision-making.

At the center of the transformation is V2C, or Volume to Customer, an AI-powered platform that brings sales, customer care and supply chain teams into a shared workflow. The platform replaces fragmented tools and manual processes with predictive analytics, machine learning and SAP integration.

To view the latest agenda, click here

To register for the conference, click here

Organizations interested in sponsoring the conference, click here

Mars said that V2C reduced a decision-making process that previously required more than 50 labor hours to seconds. The platform has also supported sales enablement, customer service, working capital performance and cross-functional alignment and is now being scaled globally.

The project illustrates an important shift in enterprise AI: The value does not come simply from generating another forecast or dashboard, but from connecting information and decisions across functions so teams can act faster.

Modernizing beverage distribution

Southern Glazer’s Wine & Spirits will bring the beverage distribution perspective to Thursday’s Small Group Sessions in a joint case study with Dematic, “Modernizing Beverage Distribution: How Southern Glazer’s and Dematic Built a Scalable Fulfillment Network.”

Karli Sage, vice president of supply chain management technology and engineering at Southern Glazer’s, and Paul Havens, director of project management at Dematic, will take attendees inside the work required to develop a more scalable beverage fulfillment operation.

The session adds a sector-specific dimension to one of the conference’s central themes: how companies can translate automation investments into operational capabilities that support growth, improve execution and accommodate changing customer requirements.

Like the other Small Group Sessions, the presentation will be offered during both the morning and afternoon blocks, allowing attendees to incorporate the case study into a personalized conference schedule.

Food fulfillment meets the new retail economy

Food and beverage fulfillment will also be represented during Thursday afternoon’s executive panel, “Retail Reinvented: Automation, Omnichannel Execution & the New Fulfillment Economy.”

Eric Watts, vice president of food supply chain operations at Target, and Jay Di Sieno, senior supply chain manager at Berry Direct, will join Jeff Kellan, division president, omnichannel retail in AmAPAC at GXO Logistics. Norman Katz, president and CEO of Katzscan, will moderate the discussion.

To view the latest agenda, click here

To register for the conference, click here

Organizations interested in sponsoring the conference, click here

Berry Direct supports the delivery and fulfillment network behind Edible Arrangements, giving Di Sieno a view into a specialized operation where product handling, customer experience and last-mile execution converge. Watts will bring the perspective of a major retailer managing food supply chain operations at scale.

Together, the panelists will examine how consumer expectations, omnichannel demand, automation investments, labor challenges and regulatory complexity are reshaping retail networks. The discussion will focus on the practical tradeoffs involved in balancing service, speed, cost, compliance and profitability.

A broader celebration of supply chain innovation

Mars is one of seven organizations being recognized through the 2026 NextGen Supply Chain Awards, sponsored by Zion Solutions Group. The program is designed to do more than recognize innovation: Winners will share the projects, implementation lessons and measurable results behind their achievements.

CVS Health will receive the End User Autonomous Operations Award for a robotic fulfillment ecosystem that increased daily processing capacity from 150,000 to more than 400,000 units, achieved greater than 99.9% pick accuracy and reduced picking costs by 40%.

Ryder and BJC HealthCare will receive the Partnership in Execution Award for a healthcare logistics collaboration that improved fulfillment, inventory visibility and service while reducing order-processing costs. Tractor Supply will receive the Visionary Award, with Chief Supply Chain Officer Craig Ledbetter discussing how supply chain can become an engine for growth.

To view the latest agenda, click here

To register for the conference, click here

Organizations interested in sponsoring the conference, click here

Friday morning’s Solution Provider Awards will recognize Netstock for Intelligent Transformation and Pickle Robot for Autonomous Operations. Robust.AI will receive the Startup Award for Carter, its collaborative mobile robot designed to work alongside warehouse associates while improving productivity and flexibility.

Nashville networking—with a soundtrack

NextGen’s educational program will be paired with networking opportunities throughout the three-day event, beginning with a Wednesday evening welcome reception and continuing through breakfasts, breaks, lunch and Thursday evening’s rooftop reception at the W Nashville.

The rooftop reception will feature a performance by Nashville songwriter Travis Hill, who performs under the name Scooter Carusoe. His songwriting credits include No. 1 songs recorded by Kenny Chesney, Darius Rucker and Brett Eldredge, along with songs recorded by artists including Tim McGraw, Taylor Swift, Keith Urban, Rascal Flatts, Eric Church, Lady A, Uncle Kracker and Dierks Bentley.

The mix of education, peer discussion and informal networking is designed to give attendees opportunities to continue conversations with speakers and fellow supply chain leaders beyond the formal sessions.

Sponsors support the NextGen experience

The 2026 NextGen Supply Chain Conference is supported by technology providers and service organizations looking to connect with senior supply chain decision-makers. Current sponsors listed in the conference materials include:

• Diamond Sponsor: Zion Solutions Group
• Platinum Sponsor: Gather AI
• Gold Sponsors: Cycle Labs, Dematic, Geek+, Dexory and Zimark
• Bronze Sponsor: Verity
• Associate Sponsors: AutoScheduler and Argano

Sponsorship opportunities remain available, including a limited number of Gold Sponsorships. Gold Sponsors receive a 30-minute customer case study presented jointly with an end-user customer, giving attendees a practical look at how supply chain technology is being implemented in real-world operations.

The 2026 NextGen Supply Chain Conference will bring together leaders from supply chain, logistics, procurement, operations and technology for three days of executive education, networking and peer-to-peer learning. Registration is open, with additional speakers and session details to be announced as the conference approaches.

APQC identifies response latency—the time from disruption to informed operational action—as a critical, often overlooked performance measure. Its model tracks how quickly companies identify impact, make decisions and execute responses.

Benchmark data shows critical risk information can take weeks to influence decisions, adding an average 16.4 days to the response cycle. Organizations can reduce these delays by clarifying ownership, eliminating unnecessary approvals, standardizing response processes and learning from disruptions to improve future supply chain speed, resilience and agility.

For more on this topic, visit www.apqc.org

 

Executive takeaways

Artificial intelligence has become nearly impossible to avoid in supply chain conversations. Planning platforms now include AI-assisted forecasting, logistics providers are embedding predictive models in visibility tools, and manufacturers are connecting equipment sensors with maintenance systems.

Yet activity is not the same as scale. A June 2025 Gartner survey found that only 23% of supply chain leaders had a formal AI strategy for their function. In the 2025 MHI Annual Industry Report, developed with Deloitte, 28% of respondents reported that AI was already in use, while another 54% expected to adopt it within five years. Together, the findings describe a market with strong momentum but uneven operational maturity.

That unevenness helps explain why the most convincing applications today are focused decision services rather than autonomous systems attempting to run an entire network. They address practical questions: Which purchase order is beginning to fail? Will this shipment meet the next cutoff? Is a critical asset showing signs of failure?

These systems extend familiar processes rather than replace them. They draw on ERP, WMS, TMS, asset-management, and IoT data and deliver a risk estimate or recommendation while people still have meaningful alternatives.

To understand how this works in practice, we interviewed two experienced supply chain technology practitioners. Anupam Bandyopadhyay is a senior supply chain technology leader with more than 19 years of global experience in warehouse modernization, logistics systems, automation, and AI-enabled distribution. Ramachandra Handaragal is a senior manager and solution architect at Peloton Consulting Group with 20 years of experience leading digital-transformation projects across aerospace, manufacturing, high technology, retail, food, utilities, and other sectors.

Their perspectives are complementary. Handaragal brings experience from procurement, production, and maintenance programs, while Bandyopadhyay focuses on how warehouse, transportation, automation, and real-time data systems turn AI outputs into action. Their observations point to three areas where AI is beginning to produce tangible value.

1. Supplier risk is moving from scorecards to open-order prediction

Supplier management has traditionally been retrospective. Measures such as on-time delivery, quality, responsiveness, and cost remain important, but they answer a historical question: How has the supplier performed? They are less effective at identifying which open purchase order is beginning to fail.

AI-supported supplier-risk systems move the analysis closer to the individual order, component, lane, and delivery commitment. A model can consider promised-date changes, advance shipping notice timing, receipt patterns, supplier history, transit variability, border delays, port congestion, weather, and external disruptions. No single signal proves that an order will be late, but several weak signals can become meaningful when considered together.

Handaragal first encountered this gap roughly a decade ago while working with an aerospace tier-one manufacturer in India. An Asian supplier repeatedly remained confirmed in the ERP system until an expected shipment failed to arrive. The experience predated today’s generation of AI tools, but it captured a persistent problem: the system displayed a supplier commitment almost as if it were an objective forecast.

“The system was green. The line was down,” he said.

Drawing on that earlier experience, Handaragal sees how AI can close the gap between reported status and probable performance. A model can combine purchase-order-line history, commitment slippage, ASN timing, border dwell, holiday calendars, supplier-specific patterns, and lane performance. In implementations using this approach, he reports that risk flags can emerge four to six days before scheduled delivery.

In daily execution, four days can separate a manageable problem from a fire drill. A buyer may still be able to verify the shipment, transfer inventory, reserve premium transportation, alter the production sequence, or find another source. Once the line is waiting, many of those options disappear.

Bandyopadhyay identifies a related challenge earlier in the inbound process: critical information is often trapped in inconsistent documents. When ASNs are missing or incomplete, facilities may depend on bills of lading whose layouts vary by carrier and vendor. AI-powered document-intelligence systems can extract purchase-order numbers, carton counts, pallet quantities, and weights; assign confidence scores; and route uncertain values to an employee for review. The extracted data can then be compared with the purchase order to flag missing references, quantity discrepancies, or unusual weights before receiving is completed.

Bandyopadhyay also connects AI-enabled inbound information with warehouse replenishment. Demand signals and near-term consumption patterns can help identify high-velocity SKUs, allowing the WMS to reprioritize replenishment and protect product availability for urgent demand. In this way, AI-enabled inbound information becomes an input into warehouse replenishment and inventory decisions rather than remaining isolated within the receiving process.

The same logic applies to expediting. Handaragal recalls an organization spending $8,000 to expedite components worth $3,000 without a shared framework for comparing intervention cost with operational exposure. A low-value part may protect a high-value operation, so the expedite was not necessarily unjustified. AI can help compare the probability and consequence of delay with the cost and likely effectiveness of each possible response.

Supplier AI is therefore moving beyond more elaborate scorecards. It is bringing together order behavior, transportation risk, inbound documents, inventory needs, and economic consequences to show where intervention matters most.

2. Predictive logistics is turning visibility into action

Traditional track-and-trace systems report milestones, showing where freight was at the last update but not necessarily whether it will meet the next production, dock, or customer cutoff.

Predictive ETA systems combine movement and milestone data with route characteristics, carrier performance, traffic, weather, congestion, and historical dwell times. DHL’s Smart ETA service illustrates the approach. DHL reports that its data-driven ocean-freight forecasts have improved ETA predictions by up to 48% compared with carrier estimates. Because this is a company-reported maximum, it is best understood as evidence of the technology’s potential rather than an independent benchmark.

A predictive ETA is useful not because it creates certainty, but because it represents uncertainty more honestly and gives planners a better basis for deciding whether to intervene.

Bandyopadhyay emphasizes that an ETA creates value only when it changes execution. Distribution centers and cross-docks use arrival information to schedule labor, assign dock doors, prepare staging space, and coordinate unloading. In food-service replenishment, these decisions span multiple systems: demand may originate in the ERP, routing commitments in the TMS, and picking and loading in the WMS. A changed ETA can move an urgent order forward in the picking queue, alter an outbound loading sequence, or give a temperature-sensitive delivery priority over freight with a wider window.

He describes the enabling architecture as a decision layer that receives real-time events from ERP, WMS, TMS, and IoT systems through APIs. It evaluates what has changed and returns an updated priority or recommendation to the system where the work is being performed. At that point, predictive visibility becomes orchestration rather than another number on a control-tower screen.

Handaragal’s field experience illustrates the human side of that transition. At a Midwestern distribution center, an inbound coordinator refreshed a TMS every 20 minutes and called carriers whenever a load stopped reporting. The practical visibility process relied on two whiteboards, a telephone, and years of accumulated judgment.

When the coordinator saw a probabilistic ETA model that updated every four hours, he reportedly said, “This is what I have been doing in my head for 15 years.” According to Handaragal, the difference was scale: the model could apply comparable reasoning across approximately 4,000 loads.

Exception management is developing alongside ETA prediction. AI can classify an exception, assemble relevant order and shipment data, estimate its potential impact, and recommend a response. In one unnamed consumer-goods engagement, Handaragal reports that three analysts were handling approximately 300 logistics exceptions per week. After a triage layer was added, roughly 60% were handled through predefined logic or routed to the appropriate employee with a recommended action before an analyst manually investigated them.

The analysts spent less time gathering routine information and more time on unfamiliar or high-impact disruptions. Bandyopadhyay sees the same pattern in warehouse execution: operators still need to understand why a task moved, which operating condition changed, and when human intervention is required. AI can prioritize and recommend, but consequential decisions still require accountable human judgment.

3. Predictive maintenance is becoming a supply chain capability

Predictive maintenance is often presented as an engineering application, but its effects reach across the supply chain. A critical failure can mean lost production, delayed orders, emergency parts purchases, reduced warehouse capacity and, in a cold chain, threatened inventory and food-safety exposure.

Predictive maintenance uses sensor and operating data to evaluate the condition of an individual asset. Models may analyze vibration, temperature, pressure, electrical current, acoustic signals, and other measures for deviations from normal behavior. ABB reports that its condition-monitoring system at Tenaris’s continuously operating Dalmine pipe mill monitors high- and low-voltage motors, helps predict maintenance needs, and supports alignment with planned production stops.

Handaragal describes a manufacturing engagement in which unplanned downtime on a critical conveyor was estimated to cost approximately $2 million annually. The maintenance team was completing its scheduled work, but the schedule had been established 15 years earlier and no longer reflected the equipment’s age or operating intensity.

“The PMs were happening on time,” he recalled. “The failures were happening anyway.”

The organization connected vibration and temperature data with two years of maintenance history. Handaragal reports that the team identified a pattern that preceded motor-bearing failures by eight to 12 days, and that the first prevented failure would otherwise have caused an estimated 16-hour stoppage. Those figures come from his project account rather than a named public case.

The warning created time to locate the bearing, reserve labor, and schedule the repair during a planned production gap. At another client, Handaragal worked on an IoT-enabled predictive-maintenance process connected with Oracle enterprise asset management. The approach reduced mean time to repair by approximately 60%, improved mean time between failures by roughly 45%, generated more than $2.5 million in annual savings per installation, and resulted in zero unplanned downtime for monitored assets during the first year.

Bandyopadhyay approaches the same issue from the warehouse and cold-chain perspective. Refrigeration systems and compressors protect temperature-sensitive inventory, while failures in interconnected warehouse equipment can disrupt fulfillment capacity. An alert may therefore require more than a maintenance work order. Inventory may need to move, inbound loads may need to be redirected, and maintenance timing may have to account for order volume, labor capacity, and transportation departure schedules.

For Bandyopadhyay, integration and trust are inseparable. The alert must connect with systems that know where inventory is located, what shipments are arriving, and what alternative capacity exists. Operators also need to understand why the alert appeared, how urgent it is, and what action is expected.

Handaragal makes the same point through model performance. A system that detects 95% of failures but raises false alarms 40% of the time may create less operational value than one that detects 80% with a 10% false-positive rate. The figures reflect one engagement, but the lesson is broadly applicable: repeated false alarms teach technicians not to trust the system.

The real shift is from AI tools to AI-supported work

The supply chain AI market can appear fragmented because the same label covers forecasting, computer vision, document extraction, optimization, generative interfaces, and autonomous agents. A more useful distinction is between AI that produces an interesting output and AI that changes an operational decision.

Supplier-risk models matter when buyers and warehouse teams can intervene before material is late. Predictive ETAs matter when they change dock, labor, production, inventory, and customer-service decisions. Predictive-maintenance models create value when an early warning mobilizes parts, technicians, capacity, inventory, and contingency plans before an asset fails.

Handaragal’s field experience shows how AI can formalize knowledge traditionally held by buyers, coordinators, planners, maintenance teams, and technicians. Bandyopadhyay’s experience shows how that intelligence becomes executable through document processing, connected sensors, warehouse automation, and integration among ERP, WMS, TMS, and asset-management platforms.

Their perspectives lead to the same conclusion: value does not come from prediction alone. It comes from connecting the prediction with the systems, decision rights, and people capable of changing the outcome. AI will not predict the future perfectly, but it can give supply chain teams enough time—and sufficiently useful information—to respond before the less costly options disappear.

About the author

Sara Hsu is a clinical associate professor of supply chain management at the University of Tennessee, Knoxville’s Haslam College of Business. Previously, she was an associate professor of economics at the State University of New York at New Paltz. Hsu specializes in supply chain disruptions and supply chain fintech.

Procurement teams invest heavily in the RFQ process. Suppliers submit detailed cost breakdowns across materials, labor, overhead, logistics, tooling, packaging, freight, tariffs, and margin. In advanced environments those breakdowns are checked against should-cost models, commodity indices, engineering estimates, and historical spend.

The data exists. The transparency exists. The analytical effort exists.

The problem is what happens next. Findings are based on responses from 100 procurement and supply chain professionals (approximately 80% manufacturing; 60%+ large enterprise with global operations). Company sizes ranged from fewer than 500 employees to more than 5,000. The sample reflects a broad mix of roles and seniority across cost-intensive industries where supplier cost transparency is critical. The research shows a consistent pattern. RFQs are treated as transactions rather than long-term data assets. Once the business is awarded, the underlying cost structure is often archived in spreadsheets, stored as PDFs, buried in email, or reduced to a single unit price in the ERP. The richness disappears, and the organization resets its knowledge base with every new cycle.

That is the missed opportunity. Companies don’t need more RFQs, they need a system that turns RFQs into continuous cost intelligence.

How companies use cost breakdowns today

The research reveals heavy tactical use and a sharp drop-off when the conversation turns strategic:

• Negotiation/price validation: 85% of respondents
• Benchmarking (supplier/region): 65%
• Design/engineering input: 50%
• Should-cost modeling: 45–50%
• Risk management (T2/T3, geo, tariffs): 40%
• Strategic decision-making (make/buy, reshoring): 30%

Teams are effective in the moment. They use cost breakdowns to challenge assumptions, identify outliers, benchmark quotes, and negotiate better pricing. They spot inflated labor rates, high overhead, outdated material assumptions, and freight or tariff drivers that inflate total landed cost.

Beyond that moment, usage falls off. Sixty to 75% of organizations treat RFQs as one-time events once business is awarded. Intelligence is created in the moment, but not carried forward. The result is strong tactical execution and weak institutional learning.

Maturity matters: where organizations stand

Cost-intelligence maturity can be viewed as a five-phase journey. The research places the large majority of organizations well below strategic maturity:

1. Phase 1: Tactical/documentation (35%). RFQs used for recordkeeping, quoting, and spreadsheets.
2. Phase 2: Negotiation support (40%). Cost data used reactively for price challenges and PPV savings.
3. Phase 3: Structured benchmarking & should-cost (20%). Manual benchmarking, early cost modeling, and some design input.
4. Phase 4: Integrated cost intelligence (5%). Data connected across systems.
5. Phase 5: Predictive & collaborative (~0–1%). World-class: continuous, forward-looking use of cost data.

More than 75% of organizations remain in Phases 1 and 2. They collect data and use it for documentation and negotiation. Few have built the systems and routines that turn transparency into continuous learning.

What organizations gain as cost-intelligence maturity improves

Over more than two decades of working with manufacturing procurement organizations, Advanced Purchasing Dynamics (APD) has consistently observed that as companies move from price-based purchasing to structured cost intelligence, the gap between quoted prices and economically supportable pricing narrows. This improvement comes from better cost visibility, stronger benchmarking, more informed supplier discussions, and the ability to capture, retain, and leverage supplier cost information for better sourcing and business decisions.

Based on APD's experience across manufacturing industries, organizations relying primarily on piece-price comparisons may leave an estimated 8% to 18% gap to economically supportable pricing. Consistent use of supplier cost breakdowns can reduce that gap to approximately 6% to 13%. Organizations using company-specific cost models often narrow it further to roughly 3% to 7%, while industry-based cost models can reduce it to approximately 2% to 5%. The closest alignment typically occurs when buyers and suppliers collaborate using open-book cost information and a shared understanding of cost drivers. These ranges are directional observations from APD's experience, not guaranteed savings, and actual results vary by category, market conditions, supplier dynamics, and data quality.

As organizations advance in cost-intelligence maturity, they typically achieve:

• Closer alignment to economically supportable pricing through greater visibility into cost drivers.
• Stronger negotiations focused on facts rather than quoted prices.
• Better supplier collaboration and a more strategically aligned supply base.
• Earlier identification of cost risks, market shifts, and sourcing opportunities.
• Greater organizational knowledge by preserving supplier cost history.
• Better cross-functional alignment across procurement, engineering, finance, and operations.
• Higher buyer productivity by reusing existing cost intelligence instead of rebuilding analyses.
• Improved visibility into supplier, commodity, tariff, and logistics risks.
• A stronger foundation for predictive analytics, AI-enabled decision support, and continuous improvement.

The greatest benefit is that value compounds over time. Each supplier cost breakdown strengthens future negotiations, benchmarking, sourcing, product design, and risk management. As cost intelligence accumulates, organizations create a continuously improving knowledge base that supports faster, better-informed procurement decisions.

The biggest barriers to strategic use

The issue is not effort, it is infrastructure. Respondents consistently pointed to six barriers:

• Data fragmentation (Excel, PDFs): 80%
• Lack of a central repository: 65%
• Poor ERP/system integration: 60%
• Limited cross-functional alignment: 45%
• Compliance and data-governance gaps: 40%
• Lack of tools and analytics capability: 35%

Cost knowledge often remains personal rather than institutional. As one respondent put it: “There is no place to know supplier history. It is based on what individuals remember.” When insight lives in buyer memory, old folders, and individual spreadsheets, turnover erodes capability, just as decisions grow more complex.

Three patterns we see repeatedly

Strong transparency, no continuity. A mid-sized manufacturer required detailed breakdowns and used them aggressively in negotiation. After award, files were archived by event. The next cycle required rebuilding much of the analysis. The company had transparency; it did not have intelligence.

The ERP disconnect. Teams built rich Excel models covering material, labor, overhead, freight, and duties. When data entered the ERP, everything collapsed into a single price field. Future increase requests could not be compared to original assumptions.

Lost benchmarking potential. A global organization collected breakdowns across regions but stored them inconsistently, different templates, depths, and locations. Scale produced more disconnected files rather than a powerful comparative asset.

From events to systems

Companies do not need more RFQs. They need systems that treat every RFQ as an input into a broader cost-intelligence capability. When cost data is structured and reusable, it supports design-to-cost, supplier development, risk identification, strategic sourcing trade-offs, and better financial planning.

Cost breakdowns can become a shared language for understanding business trade-offs, not merely a negotiation tool.

AI is the next multiplier, but data is the limiter

Seventy percent of respondents see AI-driven cost analysis as the next frontier. Interest in automated supplier comparison, outlier detection, and scenario analysis is real. Those use cases are promising, but AI depends on structured data.

AI is a multiplier, not the starting point. Without standardized formats and connected historical data, advanced analytics cannot deliver strategic value. Organizations that first standardize and integrate cost data will be positioned to benefit; those that skip the foundation will struggle.

What leading organizations do differently

• They standardize cost-breakdown formats so submissions are comparable.
• They centralize cost data so teams can retrieve, compare, and reuse it.
• They integrate cost insights across procurement, engineering, finance, and operations.
• They connect internal cost structures to external signals such as commodity indices, labor trends, exchange rates, tariffs, and freight markets.
• They build internal analytical capability, not just data storage.
• They use cost insight earlier, during design, supplier selection, budgeting, and strategy, not only at final negotiation.

Where to start

Most organizations do not need a dramatic transformation. Practical steps move the needle:

1. Standardize supplier cost-breakdown templates. Consistency beats perfection.
2. Create a centralized, searchable repository. A shared location is better than disconnected spreadsheets.
3. Preserve cost structure in core systems. Avoid collapsing every RFQ into a single unit price.
4. Connect procurement with engineering and finance. Cost data should inform design, margin planning, and strategy.
5. Start with simple analytics. Compare suppliers, track changes over time, identify outliers, and build basic category benchmarks before pursuing advanced AI.
6. Establish governance. Ownership, definitions, and rules keep the system usable.
7. Build toward predictive capability. Once the foundation exists, scenario modeling and AI become far more powerful.

Final thought

Procurement teams already collect the data. The challenge is not collection, it is utilization. RFQs are one of the richest sources of cost intelligence available to the organization.

Companies that treat them as such will build better supplier strategies, make better design decisions, improve risk visibility, and support stronger financial planning. They will not just negotiate better. They will make better decisions.

About the authors

Dr. Sime Curkovic is a professor of supply chain management and Lee Honors College Faculty Fellow at Western Michigan University. His research focuses on supply chain management, sourcing, operations, logistics, and risk management.

Jeoff Burris and Mike Wynn are principals at Advanced Purchasing Dynamics, where they help organizations build cost models, improve sourcing processes, and turn supplier cost data into actionable intelligence.

Executive takeaways

Artificial intelligence is rapidly transforming the way organizations plan, source, manufacture, move, and deliver products around the world. From demand forecasting and procurement optimization to logistics management and customer service, AI is helping companies make faster and smarter decisions. However, as organizations accelerate AI adoption, they are also becoming more interconnected through cloud platforms, digital infrastructure, operational technology, and increasingly complex supplier ecosystems.

This convergence creates tremendous opportunity, but it also introduces a new leadership challenge: trust.

Can organizations trust the data, systems, suppliers, and infrastructure on which AI-enabled decisions depend? Can organizations trust their partners and suppliers are cyber-ready and cyber-resilient? As organizations become increasingly connected, trust becomes the foundation that determines whether AI can securely scale.

Trusted AI depends on the entire supply chain ecosystem

A simple question illustrates the scale of the issue: How many companies help run your supply chain? Increasingly, these organizations do more than move products or provide services. They also generate data, operate digital platforms, and influence the information that AI uses to make recommendations. Enterprise AI is therefore only as trustworthy as the ecosystem on which it depends.

For many organizations, the answer ranges from hundreds to tens of thousands. Each supplier, partner, technology provider, and service organization is increasingly becoming part of a broader AI-enabled ecosystem. As connectivity expands, trust, resilience, and cyber readiness become critical business capabilities rather than technical considerations.

For years, cybersecurity was often viewed as a defensive function focused on protecting systems and preventing attacks. Today, that perspective is no longer sufficient. Organizations seeking to leverage AI at scale must ensure that their data, infrastructure, supplier networks, and decision-making processes are trustworthy. In this environment, cyber resilience is no longer simply a risk management discipline. It has become a business capability that enables trusted AI, resilient operations, and confident executive decision-making. It is about enabling innovation, maintaining operational continuity, and preserving stakeholder confidence.

The challenge is too large for any single organization to solve alone. Governments, technology providers, infrastructure operators, and private-sector enterprises each play a vital role in building trusted and resilient supply chains. This makes public-private collaboration increasingly important.

Supplier cyber readiness strengthens enterprise AI readiness

This challenge is particularly important across supplier ecosystems, where thousands of small and medium-sized businesses often support critical operations while operating with fewer cybersecurity resources than their larger customers. As enterprises increasingly rely on AI-driven decisions, the cyber readiness of these suppliers becomes a direct contributor to enterprise resilience. Strengthening supplier cyber readiness is therefore not simply a compliance exercise; it is an investment in trusted AI, operational continuity, and long-term competitiveness.

Governments establish policy and incentives that encourage secure innovation. Technology providers embed security, identity, and explainability into the platforms organizations rely upon. Infrastructure providers strengthen the resilience of the digital backbone that supports modern commerce. Large enterprises can use procurement and supplier engagement to raise cyber readiness across entire ecosystems.

The executive questions that will shape trusted AI adoption

As AI adoption accelerates, executive leadership teams face several critical questions. Where should organizations invest first to safely accelerate AI adoption? What role should trust play in supplier selection and ecosystem management? What infrastructure capabilities are required before businesses can confidently rely on AI at scale? How should cyber readiness influence supplier selection and ongoing supplier performance?  What happens when a critical supplier lacks the cyber capabilities required to support trusted AI? And, perhaps most importantly, how should leaders determine when AI can make decisions autonomously and when human judgment must remain in the loop?

Organizations that answer these questions effectively will be well positioned to compete in the next era of global business. Those that fail to address trust and resilience may find it increasingly difficult to scale AI initiatives, manage risk, and maintain customer confidence.

Looking ahead, the organizations that lead in the age of AI will not necessarily be those deploying the most advanced algorithms. They will be the organizations that build the strongest foundation of trust across their data, technology, operations, and supplier ecosystems. They will recognize that cyber resilience enables innovation, supplier readiness strengthens enterprise readiness, and collaboration across the public and private sectors creates competitive advantage.

In the age of AI, trust is no longer simply a security objective. It is a business strategy.

About the author

Sasha Pailet Koff, is managing director of the Cyber Readiness Institute. She is also founder and president of So Help Me Understand and a former senior vice president of digital supply chain at Dell. 

Executive takeaways

Retailers are not short of returns data. They can rank categories, brands, products and customers by return rate; forecast reverse-logistics volume; and estimate the cost of processing an item after it comes back. Yet the metric at the center of most returns conversations remains too blunt.

Saurabh Bahree

The National Retail Federation projected that the value of retail returns would reach $849.9 billion in 2025 and estimated that 19.3% of online sales would be returned. At that scale, a percentage-point change can affect transportation, inspection labor, recovery speed, markdown exposure and working capital. But not every percentage point means the same thing.

The raw rate can point management in the wrong direction

A fitted dress, a standardized phone charger and a fragile kitchen appliance do not carry the same inherent return risk. Categories differ in fit uncertainty, product complexity, price, channel mix, seasonality and customer expectations. Comparing them solely on raw return rate is like comparing hospitals solely on readmissions without considering the patients they treat.

This can create a management error. Research shows return propensity varies across products and can be predicted from product characteristics. A high-return category may be near its expected level, while a lower-return product may underperform comparable items because of defects, misleading content, fulfillment errors or damage. Raw rates reveal volume, not abnormal performance.

Return prediction does not solve this by itself. Models estimate whether an order is likely to come back, as ASOS researchers have demonstrated using GraphReturns. Most applications target individual transactions, customer messaging or volume forecasts. Healthcare offers another model: aggregate probabilities into expected outcomes and compare them with actual performance. Returns teams could apply this risk-adjustment logic.

Borrow a measurement discipline from healthcare

The Centers for Medicare & Medicaid Services does not assess hospital readmissions using raw counts alone. Its Excess Readmission Ratio compares predicted with expected unplanned readmissions after adjusting for patient case mix. The objective is not to excuse poor outcomes, but to avoid treating different underlying risk populations as though they were identical.

Retail can adapt that logic. For a product, supplier, category or operating node, calculate an expected return count by summing the pre-purchase return probability of every transaction in the group. Then compare observed with expected merchandise returns:

Excess Return Ratio = Observed merchandise returns / Expected merchandise returns

A ratio near 1.0 indicates performance broadly in line with the selected benchmark. A ratio materially above 1.0 identifies more return demand than the model expected. It is an investigation signal, not a finding of fault. Confidence limits and minimum-volume thresholds should prevent small or statistically insignificant deviations from triggering action.

Consider two hypothetical groups with 10,000 purchases each:

Illustrative comparison; figures are hypothetical.

The raw-rate dashboard prioritizes dresses. Risk adjustment points to appliances. If appliance returns also cost more to transport, inspect and recover, the financial priority becomes clearer still.

Risk-adjusted monitoring is not new. Manufacturing researchers have applied risk-adjusted control charts to warranty claims to detect emerging product-quality problems, while healthcare has long used observed-to-expected measures. The opportunity is to apply that discipline to e-commerce return demand across commercial and supply-chain owners.

The benchmark must follow the accountability question

The hardest decision is not which algorithm to use. It is which variables the benchmark should treat as legitimate context.

For group g, expected returns are the sum of its purchase-level probabilities: Eg=ipi. Its Excess Return Ratio is ERRg=Og/Eg, where Og is observed returns. A value above 1 indicates more returns than expected.

The variables used to estimate pi must match the accountability question. A supplier benchmark might adjust for category, price band, fit exposure, season, channel and market, but not supplier-controlled factors such as workmanship, sizing consistency or defect history. Otherwise, persistent poor performance raises the benchmark and begins to look normal. Apply the same rule to fulfillment and product content: adjust for conditions outside the owner’s control while leaving its own performance visible.

Expected also does not mean unavoidable. A model describes what is likely under specified assumptions. Only investigation can determine whether excess demand is preventable and who owns the cause.

Give excess return demand a budget

Measurement becomes operational when it creates a decision rule. Google popularized error budgets in site reliability engineering: teams define an acceptable level of service failure, then use budget consumption to decide whether releases can continue or reliability work must take priority. Retailers could apply an excess-return budget above the risk-adjusted benchmark.

A category, supplier or launch would consume that allowance when observed return demand credibly exceeds expectation. A slow, persistent burn could create an investigation ticket. A rapid burn associated with a launch, supplier batch or fulfillment node could trigger inventory inspection, content correction, containment or a replenishment pause.

This is not a target of zero returns. Legitimate returns reduce purchase risk and support conversion. The budget instead creates a shared tolerance for abnormal demand and a common escalation rule across merchandising, quality, e-commerce, finance and supply chain.

Close the signal, not just the transaction

When a threshold is breached, the organization still needs a cause-closure process. A practical sequence is to classify the suspected cause and confidence, attribute excess units and cost, assign the accountable owner, apply a cause-matched intervention, protect legitimate customers, and verify that performance improves.

The intervention should follow the evidence. A sizing problem belongs with product and supplier teams. Misleading imagery belongs with content. Damage may belong with packaging, fulfillment or the carrier. Customer friction should not be the default response to failures created upstream.

Leaders can test the approach in 90 days. Select one high-volume category, define the accountability question, agree on permissible adjustment variables, and compare raw with risk-adjusted rankings. Choose two or three statistically credible excess clusters, investigate them and track corrective action through an effectiveness check. Monitor conversion, complaints and defective-product indicators alongside return outcomes.

Retailers have spent years improving what happens after an item enters the reverse network. The next step is to improve how return demand is measured before assigning accountability and deciding where to intervene. The raw return rate describes workload. A risk-adjusted excess return measure can direct action.

About the author

Saurabh Bahree is an enterprise e-commerce and digital transformation leader with more than 17 years of global experience in consulting, program leadership and technology-enabled business transformation. He has led complex, cross-functional programs spanning digital commerce, platform operations and enterprise technology. He holds an engineering degree and an MBA and is a certified PMP and SAFe practitioner.

What’s missing is execution.

Agentic AI represents the next step in supply chain evolution, shifting systems from passive insight generation to active decision support and, in some cases, autonomous execution. Instead of simply surfacing disruptions, these systems can recommend or even take actions across planning and execution workflows, helping organizations respond faster to volatility, reduce manual intervention, and improve overall performance.

In this session, we’ll move beyond the hype and examine:

• How agentic capabilities are being applied today
• Explore what differentiates true “decision-oriented” AI from traditional analytics
• Where early adopters are seeing measurable impact
• How to move from insight to execution without overhauling your tech stack

Whether you’re experimenting with AI or looking to scale beyond pilot programs, this discussion will provide a practical framework for turning insight into action.

FEATURING:
Ben Dussault, Director, Product Management, Supply Chain, Anaplan; Dipti Gupta, Head of Product Strategy & Solutions, Logility and Caique Zaniolo, SVP of Product, Tradeverifyd

Executive takeaways

Logistics providers are being asked to do more than move and store products. Customers increasingly expect their 3PL partners to help redesign networks, deploy automation, improve inventory accuracy, manage risk and create the visibility needed to make faster decisions. Fulfillment operations face a similar mandate as companies balance speed and service with cost, labor constraints and rising operational complexity.

Those pressures and the strategies logistics leaders are using to address them will be a major focus of the 2026 NextGen Supply Chain Conference, taking place Oct. 21-23 at the W Nashville in downtown Nashville.

Logistics and fulfillment will represent one of several industry-focused paths attendees can follow throughout this year’s conference, alongside retail, food and beverage, and chemicals and pharmaceuticals. Across keynote presentations, fireside conversations, an executive panel and interactive Small Group Sessions, practitioners will share how new technologies and operating models are changing execution inside warehouses, transportation networks and customer fulfillment operations.

Ryder and BJC demonstrate the value of 3PL partnership

Thursday’s program will open with the NextGen Supply Chain End User Awards, including the Partnership in Execution Award for Ryder and BJC HealthCare.

Thys Visser, vice president of operations, healthcare and high tech at Ryder, and Jason Luby, vice president of value chain management and sourcing operations at BJC HealthCare, will discuss how the organizations’ redesigned healthcare logistics around patient outcomes.

To view the latest agenda, click here

To register for the conference, click here

Organizations interested in sponsoring the conference, click here

The partnership centers on a highly automated, 416,000-square-foot Consolidated Services Center that serves as a centralized logistics hub for BJC’s eastern region. By unifying supplier channels, improving delivery sequencing and integrating warehouse management with RyderShare visibility technology, the operation has helped BJC move from reactive supply management toward greater control, transparency and resilience.

The measurable results include hospital order fulfillment rising from 90% to more than 99%, on-time, in-full performance increasing from 27% to 75%, an 80% reduction in order-processing costs and 25 fewer days of inventory on hand. The case study will show how a 3PL relationship can extend beyond outsourced execution to become a platform for operational transformation.

Wayfair opens a window into complex home delivery

Following the awards, Nitin Kapoor, vice president of technology at Wayfair, will join Supply Chain Management Review Editor-in-Chief Brian Straight for the keynote fireside conversation, “Building the Future of Home Delivery: Wayfair’s Logistics Evolution.”

The session will examine how Wayfair has built and evolved its logistics network using technology to improve speed, reliability and scale. Kapoor will discuss the innovations driving the company’s supply chain strategy, lessons from operating a complex home-delivery network and recent enhancements to its delivery offerings.

For logistics and fulfillment leaders, the conversation offers a look inside one of e-commerce’s most demanding execution challenges: moving large and bulky products through a network and into consumers’ homes while meeting increasingly high service expectations.

Penske and DP World bring the 3PL transformation perspective

The main-stage program will also feature two perspectives from global logistics providers.

Andy Moses, senior vice president of sales and solutions at Penske Logistics, will join Peerless Media Group Editorial Director Michael Levans for a Thursday morning fireside chat on the state of logistics.

Later Thursday, Carey Boone, vice president of transformation-Americas at DP World, will take part in the fireside chat, “Scaling Transformation: Aligning Operating Models, Process Discipline, and Talent Capability.”

Boone’s session will examine why supply chain transformations often fall short when operating models, leadership behaviors and talent systems do not evolve alongside technology. The discussion will focus on aligning people and processes to support growth, future-proof operations and produce sustainable results.

Small Group Sessions go inside the warehouse

Some of the conference’s most detailed logistics case studies will take place during Thursday’s Small Group Sessions, which are scheduled in both morning and afternoon blocks so attendees can build a program around their priorities.

Vitti Logistics uses computer vision to close the system-reality gap

Antonio Luna, founder of Vitti Logistics, and Hilla Herzog Manor, vice president of business development and strategy at Zimark, will present “Closing the System Reality Gap: How Vitti Logistics Uses Computer Vision to Maintain Inventory and Shipping Accuracy with High-Value Goods.”

Vitti, a 3PL specializing in high-value goods and cross-border logistics, installed cameras on material-handling equipment and dock doors, supported by edge AI computing, to help keep its warehouse management system aligned with what is actually happening on the floor. The speakers will address the technology, return on investment and change-management requirements, as well as why computer vision can offer some automation benefits at a lower cost than full automation.

ODW Logistics replaces lengthy audits with continuous intelligence

Kayla Watson, director of inventory control at ODW Logistics, will join Todd Boone, vice president of North America at Dexory, for “From Weeks to Real-Time: Inside ODW Logistics' Journey to Warehouse Intelligence with Dexory.”

The session will show how ODW replaced manual audit cycles with continuous, autonomous inventory intelligence. The system can audit 1 million square feet in less than 24 hours and identify 90% of discrepancies before they reach the customer. Attendees will hear what the implementation looked like in practice and how faster inventory insight changes warehouse decision-making.

To view the latest agenda, click here

To register for the conference, click here

Organizations interested in sponsoring the conference, click here

DHL Supply Chain puts people at the center of automation

Mukul Parkhe, continuous improvement manager at DHL Supply Chain, will lead “The Human Advantage in Automated Warehouses.”

As AMRs, robotics, warehouse management, warehouse execution and warehouse control systems multiply, human operators increasingly manage the exceptions that automated systems cannot resolve. Parkhe will explore how organizations can design workflows, visibility and decision support that keep people effective as warehouses become more automated.

Southern Glazer’s and Dematic focus on scalable fulfillment

Karli Sage, vice president of supply chain management technology and engineering at Southern Glazer’s Wine & Spirits, and Paul Havens, director of project management at Dematic, will present “Modernizing Beverage Distribution: How Southern Glazer’s and Dematic Built a Scalable Fulfillment Network.”

GXO connects logistics with the new fulfillment economy

Logistics will also be central to Thursday afternoon’s executive panel, “Retail Reinvented: Automation, Omnichannel Execution & the New Fulfillment Economy.”

Jeff Kellan, division president, omnichannel retail in AmAPAC at GXO Logistics, will join Jay Di Sieno, senior supply chain manager at Berry Direct, and Eric Watts, vice president of food supply chain operations at Target. Norman Katz, president and CEO of Katzscan, will moderate.

The panel will explore how changing consumer expectations, omnichannel demand, automation investments, labor challenges and regulatory complexity are reshaping retail networks. Kellan’s 3PL perspective will help connect the retailer’s customer promise with the logistics operations required to deliver it while balancing speed, service, compliance, cost and profitability.

Amazon applies machine learning to carrier performance

Transportation execution will remain in focus Friday when Debanshu Sharma, senior supply chain manager at Amazon, presents “From Reactive to Predictive: How ML-Based Carrier Risk Scoring Reduced Pickup Defects by 35%.”

Sharma will explain a machine-learning framework built and validated across more than 150,000 loads and 1,600 carriers. The system generates pickup-risk scores at the carrier level, allowing operators to intervene before a likely failure rather than react after a defect occurs.

The reported results include a 35% reduction in pickup defects in the high-risk carrier segment, 85% model accuracy and more than $40 million in projected annual savings. The session illustrates a broader NextGen theme: applying AI to specific operational decisions where the business impact can be measured.

Multiple paths through NextGen

Logistics and fulfillment represent one path attendees will be able to follow through the 2026 program.

The broader agenda is being developed around several industries and operating environments undergoing significant supply chain transformation, including retail, food and beverage, and chemicals and pharmaceuticals. That structure allows attendees to focus on their own priorities while participating in broader discussions around artificial intelligence, automation, digital transformation, workforce development and operational execution.

The conference also features executives from organizations including Eli Lilly, Tractor Supply, Apple, Fanatics, Mars, CVS Health, Stanford Medicine, GE Healthcare, Evonik and Target, among others. Additional speakers and session details will be announced as the conference approaches.

More than conference sessions

NextGen is also designed to create opportunities for attendees to connect outside the meeting rooms.

The conference opens Wednesday evening with a Welcome Reception and Networking event, featuring Nashville-based singer-songwriter Nick DeLeo in The Living Room at the W Nashville. Emma White, one of Rolling Stone's “10 New Country Artists You Need to Know,” will perform during Thursday’s attendee luncheon at Zaytinya, sponsored by Gather AI.

Thursday concludes with a rooftop reception featuring songwriter Travis Hill under his performance name Scooter Carusoe. Hill co-founded Carnival Music and has written five No. 1 songs recorded by Kenny Chesney, Darius Rucker and Brett Eldredge, with additional credits for artists including Tim McGraw, Taylor Swift, Keith Urban, Rascal Flatts, Eric Church, Lady A, Uncle Kracker and Dierks Bentley.

Sponsorship opportunities remain available

The NextGen Supply Chain Conference continues to attract support from technology providers and service organizations looking to engage senior supply chain decision-makers. Current sponsors include:

• Diamond Sponsor: Zion Solutions Group
• Platinum Sponsor: Gather AI
• Gold Sponsors: Cycle Labs, Dematic, Geek+, Dexory and Zimark
• Bronze Sponsor: Verity
• Associate Sponsors: AutoScheduler and Argano

Sponsorship opportunities remain available, including a limited number of Gold Sponsorships. Gold Sponsors receive a premium speaking opportunity built around a 30-minute customer case study presented jointly with an end-user customer, giving attendees the opportunity to learn directly from organizations implementing supply chain technologies in real-world environments.

The 2026 NextGen Supply Chain Conference will take place Oct. 21-23 at the W Nashville in downtown Nashville, bringing together senior leaders from supply chain, logistics, procurement, operations and technology for three days of executive education, networking and peer-to-peer learning.

Early bird registration is currently open, and additional speakers, sessions and entertainment will be announced in the coming weeks.

Executive takeaways

Procurement’s remit has expanded dramatically in the past decade. As organizations faced supply chain disruption, inflationary pressure, and geopolitical uncertainty, procurement leaders were asked to do far more than negotiate savings. The function is now expected to protect margins, manage supplier risk, strengthen resilience, and help organizations navigate a more volatile operating environment.

Those responsibilities have not disappeared. Instead, new research from the Economist Enterprise Report, sponsored by SAP, suggests those expectations are now colliding with a familiar demand: cut costs. In the latest survey, 54% of executives identify cost savings and optimization as procurement’s primary source of value, up from 43% the previous year.

This does not mark a retreat to traditional procurement. It marks something more difficult: procurement must deliver financial discipline without giving up the strategic responsibilities it has gained.

The latest findings highlight why this shift is happening and the reasons leaders are increasingly looking to agentic AI to help organizations keep up with the challenges of modern-day procurement.

Cost savings and optimization have reclaimed center stage

Cost management has returned as procurement’s most visible contribution to the business.

According to the SAP and Economist Enterprise study, more than half of executives identify cost savings and optimization as procurement’s primary source of value—after several years in which resilience and risk management dominated procurement conversations.

The renewed focus on cost is not simply cyclical. Resilience itself has become more expensive. Dual sourcing, inventory buffers, nearshoring and supplier diversification all carry costs that organizations must fund somewhere. Procurement is increasingly being asked not only to find savings, but to help finance the resilience businesses now require.

The challenge, however, is that cost optimization has become even more complex. Savings opportunities are no longer limited to sourcing events and supplier negotiations. Category strategies, supplier performance, demand patterns, and market dynamics are being constantly monitored for cost-saving opportunities.

This requires greater visibility, faster decision-making, and more sophisticated analysis than many teams can realistically achieve through manual processes alone.

Digital transformation and AI are becoming procurement’s near-term imperative

The report also shows that procurement leaders recognize technology will play a critical role in meeting heightened expectations.

Among survey respondents, 60% identified digital transformation as a key priority over the next 12 to 18 months, and AI adoption is the single most important driver of that transformation, cited by 56%.

These findings reveal how organizations are no longer pursuing digital transformation solely to improve operational efficiency. Instead, they are investing in technology because better visibility and decision intelligence have become essential to procurement’s expanded mandate.

This is particularly relevant for category management, where procurement teams are expected to balance cost, supply continuity, risk exposure, sustainability requirements, and stakeholder needs simultaneously.

Agentic AI offers a new approach. Nearly two-thirds of executives (67%) identify greater use of AI-driven and predictive insights as the single most important driver of improvement in category management. Rather than simply automating individual tasks, AI agents can continuously analyze spend data, monitor supplier activity, surface emerging risks, identify sourcing opportunities, and recommend actions aligned with procurement objectives.

Instead of reacting to market changes after they occur, agentic AI allows teams to adjust strategies before costs escalate or risks materialize. The result is faster decision-making and more informed resource allocation across procurement’s highest-value activities.

Procurement is moving from AI experimentation to AI accountability

The appetite for AI is clear, but the next phase will be defined less by experimentation than by accountability. Executives are looking for measurable outcomes from AI investment, especially in areas where procurement teams already see practical gains: cost optimization, process automation, productivity and guided decision support.

At the same time, the research shows that AI’s impact on decision-making remains limited, underscoring why procurement leaders need to pair adoption with stronger data foundations, integration, and governance.

This is where agentic AI has the potential to fundamentally reshape procurement operations. More than half of executives (56%) say their organizations plan to implement or evaluate agentic AI in procurement over the next 12 to 18 months, making it the most sought-after technology in the survey.  AI agents can support sourcing decisions, identify contract leakage, guide compliant purchasing behavior, and continuously evaluate category strategies.

Importantly, this does not replace procurement expertise. Just 9% of executives want AI to lead most procurement decisions, while nearly half (46%) expect AI to handle tactical activities as humans retain strategic control. Even with agentic AI, the most critical procurement decisions still require human judgment, stakeholder alignment, and commercial insight.

The future of procurement is not autonomous decision-making. It is amplified decision-making. In an environment where procurement is expected to do more with the same resources, that distinction matters.

From cost control to financial resilience

The renewed focus on cost management is a fundamental evolution of procurement’s role. Of course, organizations still need procurement to strengthen resilience, manage risk, and support growth. What has changed is the expectation that these outcomes must now be delivered under tighter financial scrutiny.

Organizations that recognize cost discipline and strategic value creation as interconnected have the clearest path to long-term success.

As executive expectations continue to rise, procurement leaders are fully aware that AI will influence the function's future. The question now is how quickly they can use it responsibly and effectively to protect the strategic ground procurement has earned while delivering the financial discipline the business requires.

Growing warehouses face a familiar bind: customer expectations keep rising while headcount, space and IT resources stay flat. Many small and medium-sized businesses assume the only way to fix warehouse inefficiency is a costly, developer-heavy customization project — one most teams don't have the staff or budget to support.

There's a better path. Join Infios, Supplysoft and a guest customer for a candid conversation on how a configurable WMS lets SMB operations adapt to unique workflows, integrate with ERP systems and support growth — all without writing a line of code or waiting on a development queue.

In this session, you'll learn:

• Why configuration (not customization) is the faster, lower-risk path to warehouse efficiency

• How to evaluate whether your current WMS can flex with you as you grow

• What it really takes to become "cloud-ready" without an in-house IT team

• Real results from an SMB warehouse that scaled throughput and accuracy without adding headcount or infrastructure

Whether you're managing rising return rates, seasonal demand swings or the leap to next-day fulfillment, this webinar will show you how to get more out of your WMS investment starting on day one.

Featuring:
Justin Velthoen, Director of Product Management, Infios and Humberto “Bert” Rodriguez, Co-founder and the CEO, Supplysoft

Executive takeaways

For years, supply chain leaders have chased one goal: better visibility. They invested in ERP systems, transportation and warehouse management systems, control towers, IoT sensors, predictive analytics, and digital twins. Each new technology promised a clearer picture of what was happening across increasingly complex supply chains.

By almost every measure, those investments have delivered. Managers can track shipments moving across oceans in real time, detect supplier disruptions before they become crises, and forecast demand with remarkable accuracy. Today’s supply chains are more visible than ever before.

The Red Sea crisis showed both the power and the limits of that achievement. When attacks on commercial vessels escalated in early 2024, the disruption was visible immediately. Every control tower and tracking platform showed vessels diverting and queuing as container transits through the Suez Canal collapsed. Information was never the problem alone. The hard part came next. Should ships reroute around the Cape of Good Hope, a detour J.P. Morgan estimated adds roughly 4,000 miles to each voyage? Should critical goods shift to air freight as Asia–Europe rates surged? Which customers should be served first from constrained inventory? The dashboards displayed the problem. Managers still had to make every one of those calls.

That is because visibility and decision-making are not the same thing. For more than two decades, supply chain technology has focused on helping organizations answer one question: What’s happening? The next competitive advantage will come from answering a different one: What should we do next?

The visibility revolution is complete

Real-time shipment tracking, predictive demand planning, supplier monitoring, and integrated control towers, once revolutionary, are now standard operating practice. Better visibility has reduced uncertainty, improved coordination, and let organizations respond to disruptions faster than ever before.

But greater visibility has exposed a new challenge: seeing a disruption is only the beginning. A delayed shipment doesn’t reveal the best recovery strategy. A low inventory alert doesn’t determine whether stock should be transferred between facilities, replenished through expedited freight, or allocated to the highest-value customers. Every decision involves trade-offs among cost, service, resilience, and customer relationships, and technology has largely left those trade-offs to managers.

When more visibility creates more work

Greater visibility has unquestionably made supply chains better. But it has produced an unexpected consequence: more information creates more decisions. Every new dashboard introduces another metric to monitor. Every alert competes for attention. Every predictive model generates another scenario to evaluate.

Evidence suggests decision-making, not information, is now the binding constraint. In a 2023 Gartner analysis of 600 supply chain decision-makers, digital trade-off models made no meaningful impact on the rate of good decision outcomes, and more than half of leaders who relied on digital technology for a recent strategic decision said they would have reached a better outcome without their models. Gartner’s explanation is telling: up to 80% of actual, on-the-ground supply chain processes aren’t reflected in the digital models meant to optimize them.

The daily reality compounds the problem. Overnight alerts accumulate; a supplier falls behind; transportation costs shift; demand moves. These issues rarely arrive one at a time, and they rarely exist in isolation. Reallocating inventory to protect one customer may raise stockout risk for another. Every decision influences the next, creating a web of trade-offs no single dashboard can capture.

Organizations don’t suffer from a shortage of information. They suffer from a shortage of decision-making capacity. Over time, that shortage accumulates into what might be called decision debt. Like technical debt in software, decision debt builds gradually. A delayed decision today creates additional decisions tomorrow. Issues that should be resolved locally move up the organization in search of approval. A 2026 Gartner survey found that 72% of supply chain leaders have had to revisit final approvals for network decisions at least once, causing delays. The result isn’t simply slower decision-making. It is reduced agility at precisely the moments when agility matters most.

The next evolution isn’t better visibility. It’s better decisions.

If the last generation of supply chain technology was built to answer “What's happening?”, the next generation should answer a far more valuable question: “What should we do about it?” Collecting more data and generating better forecasts remains essential but no longer sufficient. The greatest opportunity now lies in helping managers move from information to action.

Imagine a distribution center learns that inventory for a critical product will fall below safety stock within three days. Today’s technology handles this well: it identifies the shortage, estimates demand, and warns managers before service levels suffer. Then the process stops. Someone still has to gather information, compare alternatives, coordinate across procurement, transportation, and operations, and decide how to respond. That work remains largely manual.

Decision support changes the role of technology. Instead of simply identifying the problem, the system assembles the relevant context—current demand, inbound shipments, supplier lead times, transportation capacity, customer priorities, and inventory across the network. It evaluates response options, estimates their consequences, and presents a recommendation with clear reasoning. Rather than asking managers to build every scenario themselves, the system prepares the decision for them.

The manager still decides. That distinction matters. The objective is not to replace human judgment but to elevate it and the evidence suggests the combination works. The same Gartner research found that when decision makers’ on-the-ground visibility is augmented with digital trade-off analysis, they are 83% more likely to make a good decision than a bad one.

This is where technologies such as agentic AI become valuable not because they automate decisions, but because they help organizations make better ones. Most organizations already use AI to detect patterns, forecast demand, or identify disruptions; these systems generate insights but typically stop there. Agentic AI extends the process: rather than producing another alert, it reasons through a problem much as an experienced analyst would—gathering information from across the supply chain, evaluating options against organizational objectives, explaining trade-offs, and recommending a course of action.

Consider a supplier that unexpectedly misses a shipment. Instead of simply notifying the planner, an agentic system could identify alternative suppliers, estimate the impact on production schedules, evaluate expedited transportation, and recommend the response that best fits the organization’s priorities. The manager reviews, applies judgment, and approves or modifies the action. AI doesn’t replace the decision-maker; it becomes a decision partner. Gartner now predicts that by 2031, 60% of supply chain disruptions will be resolved without human intervention.

Getting there won’t be automatic

None of this arrives simply by buying software. Decision support is only as good as the data beneath it, and most supply chains still run on fragmented systems where master-data cleanup and integration are prerequisites, not afterthoughts. Organizations must also make their priorities explicit: a system cannot weigh cost against service unless leadership has defined how those trade-offs should be resolved—a discipline many companies have never formalized. And trust must be earned incrementally. Recommendations will sometimes be wrong, so systems must show their reasoning, and organizations need clear governance over which actions run autonomously and which require human approval. Companies that treat decision support as a change-management effort rather than an IT purchase will pull ahead.

Supply chain technology has always evolved in stages. First, we learned to see. Then we learned to predict. Now we must learn to decide.

About the Author

Dr. Rishabh Rana is an Assistant Professor of Logistics and Supply Chain Management in the Dr. Sam Pack College of Business at Tarleton State University. His research focuses on AI-enabled decision-making, systems thinking, and complexity in supply chain operations.

References

1. “Red Sea Attacks Disrupt Global Trade,” IMF Blog, March 7, 2024; “Shipping Disruptions in the Red Sea: Ripples Across the Globe,” Federal Reserve Bank of St. Louis, February 2024.
2. “The Impacts of the Red Sea Shipping Crisis,” J.P. Morgan Global Research, 2024.
3. “Gartner Says 80% of Supply Chain Not Accounted for in Current Digital Decision Models,” Gartner press release, September 27, 2023.
4. “Gartner Survey Shows 72% of Supply Chain Leaders Revisit Final Approvals for Network Decisions at Least Once, Causing Delays,” Gartner press release, July 14, 2026.
5. “Gartner Predicts 60% of Supply Chain Disruptions Will Be Resolved Without Human Intervention by 2031,” Gartner press release, March 18, 2026.

Executive takeaways

Volatile markets have made annual savings targets harder to meet. Geopolitical disruption, commodity price surges and component shortages all push prices up. Yet procurement is still judged largely on savings, a number that only shows whether the company paid less than the year before.

Savings give a limited view of procurement’s effectiveness. They show improvement against a prior price. They cannot show that the company is paying a competitive price in absolute terms. In an inflationary market, a procurement team can buy at the best price available and still report little or no savings.

Without evidence of the company’s market position, the CPO cannot tell the CFO whether a missed target reflects poor buying or an unrealistic expectation. The target stands, and procurement chases reductions the market cannot support.

That is where the savings death spiral begins. Procurement pushes suppliers past the point of a fair deal, or accepts lower specifications, longer lead times and thinner service levels to make the number. Each round makes the next one harder, because the easy price is already gone. The pressure increases again. The savings are reported. The supplier stress, quality erosion and added risk show up later and rarely on procurement’s scorecard.

A narrow view of procurement’s own value compounds the problem. Thirty-seven percent of CPOs rank hard cost savings as their most important metric for communicating procurement’s value, according to Gartner. But how do CPOs demonstrate value when inflationary forces make hard savings impossible?

Instead, CPOs should prioritize cost leadership. Gartner defines this as securing the best available price and terms for the organization’s requirements, purchasing scale and prevailing market conditions. Proving it requires information from outside the company’s own purchasing history.

Four shifts can provide that context and show that the company secured a competitive deal under current market conditions.

1. Shift from price reductions to price competitiveness

CPOs should supplement savings with evidence that they are accessing the best prices and terms available in the market.

Frame the assessment around one question: could another buyer with similar requirements and purchasing scale obtain materially better pricing or terms under the same market conditions?

Savings show movement from a baseline. Competitive evidence shows whether the company holds a strong commercial position. Keep reporting savings during the transition. For major categories, start explaining where current prices sit relative to the market and how confident you are in that assessment.

2. Shift from internal baselines to external evidence

By 2028, CPOs that cannot demonstrate cost leadership will struggle to defend procurement’s value proposition to the CFO, according to Gartner.

Three methods help. Competitive benchmarks compare the company’s price with those paid by similar buyers. Market indexes show whether a contracted price is responding properly to changes in commodities or other inputs. Should-cost models break a product or service into its underlying costs to estimate what a buyer should reasonably expect to pay.

The right method varies by category. In raw materials, if a commodity index falls 10% while the contracted price falls 6%, procurement records savings even as its competitive position weakens. External evidence makes that deterioration visible.

3. Shift the best price check earlier in sourcing

Market evidence is most useful before the company signs a contract.

For awards above a defined spending threshold, require a benchmark, index comparison or should-cost estimate as part of contract approval. The evidence does not need to be perfect. It needs to be consistent enough to flag a questionable price while there is still time to negotiate or reconsider the award.

This also exposes trade-offs early. A lower price may carry changes in specifications, lead times or service. Reviewing the market position before signature puts those consequences on the table alongside the reported savings.

4. Shift investment toward the categories that matter most

Proving cost leadership across every purchase would consume time and money without a proportional benefit.

Start with categories that have a material effect on product cost or financial performance. Large, direct-material categories are often strong candidates because commodity indexes or external benchmarks already exist. Other high-spend categories may justify building a should-cost model.

AI-enabled procurement technology is making market intelligence, benchmarking and cost analysis more practical. Most functions will still need a phased plan to close their data gaps. Early progress in a few important categories gives the CFO better evidence while procurement builds broader coverage.

Build a more credible cost story

Cost leadership gives procurement a stronger answer when the CFO sets a savings target the market cannot support. It shows where prices are competitive, where improvement is still possible, and where more supplier pressure would buy risk instead of value. That last point is what breaks the death spiral. Once procurement can prove it is already at the best available price, the argument shifts from how much more can you cut to what else can we improve.

Gartner analysts are providing further analysis on this topic at the Gartner Procurement Conference, taking place in San Diego, CA on Sept. 15-16.

About the author

Miguel Cossio is a Senior Director Analyst in Gartner’s Supply Chain practice. Miguel focuses on helping procurement leaders deliver greater value by adopting the latest trends in supplier collaboration and innovation, performance management and sustainable procurement.

Executive takeaways

Earlier this year, as conflict escalated in the Middle East, enterprises did not have days to replan. They had hours. Teams across commercial, supply chain, logistics and sourcing functions were forced to redesign, plan, and execute in real time.

Some approaches held up under that pressure. Others did not. The difference was not model sophistication. It was whether those systems could correctly contextualize what was happening against the physics of the enterprise—the flows, constraints and dependencies that govern how materials, information and decisions move when conditions change.

The cost of getting that wrong is not abstract. It shows up quickly: missed deliveries, misaligned inventory, financial performance under pressure and decisions that made sense in a controlled demonstration or a war room but fall apart in execution.

There is a simple way to assess whether AI is built for this reality: ask how it handles the bullwhip effect.

What the physics reveals

Disruptions are often described as isolated events. A plant goes offline. A supplier misses a shipment. A lane closes. But in reality, the consequences begin propagating immediately in both directions.

Inventory is already in motion toward locations that can no longer receive it. Customers are waiting on orders that will not arrive. Signals cascade through the network, becoming increasingly disconnected from what is happening on the ground. 

This is where many AI approaches struggle. Handling that kind of chain reaction requires more than access to data or a sophisticated algorithm. It requires an understanding of how a specific enterprise network of suppliers, contracts, inventory positions and constraints behaves under stress.

The most capable systems do more than generate insights. They sense changing conditions in real time, analyze options across the full network and act within live business processes—learning continuously as outcomes unfold.

The bullwhip effect is a useful test precisely because it cannot be explained away with abstractions. It exposes whether systems align to the physics of how the enterprise actually operates.

The real problem is not one decision. It is the chain.

Much of the AI applied to supply chain today is designed to answer individual questions, and many of those answers are increasingly accurate.

But supply chain decisions do not exist in isolation. They are flows of material, information and financial commitments moving through a network of constraints. What happens in one place creates consequences everywhere else, often before teams have time to react.

This is where enterprise AI still falls short. Getting information, or a chatbot answer, has advanced quickly. What has not kept pace is building true enterprise intelligence: systems where information flows are connected to the underlying physics of the business, external signals are correctly contextualized and models identify the right pressure points, generate the scenarios that matter and translate decisions into action. 

That is not a supply chain problem. It is an enterprise problem. The organizations making progress are not looking for faster answers to individual questions. They are focused on outcomes—orchestrating decisions into action across R&D, finance, operations and commercial teams as one connected environment.

What orchestration actually means

Orchestration is often treated as a feature that simply triggers multiple actions. In reality, it is a capability. 

It has multiple dimensions—data, systems, processes, decisions and actions—and each must move in concert. A decision made in one part of the business must propagate across all of them, from procurement to logistics to customer service, fast enough to matter. Orchestrating one dimension while the others lag is not orchestration at all.

This is not just system connectivity. It is the coordinated orchestration of the enterprise, enabling decisions to be simulated, tested and executed within the flow of the business. 

The companies seeing real value from AI are not simply implementing LLMs. They are driving strategies that transform how they operate—from function-driven organizations to flow-driven ones, powered by shared enterprise intelligence and orchestration across every dimension.

The supply chain is the heart of the enterprise—the system through which every decision ultimately flows. Orchestrate it well, and it becomes a source of lasting differentiation.

About the author

Manik Sharma is Chief of Agentic Solutions at Kinaxis. He focuses on translating AI capabilities into measurable business value and advancing enterprise decision-making and operational performance.

Executive takeaways

Few industries demonstrate the transformation taking place across supply chains more clearly than retail.

Consumers expect products to be available where and when they want them. E-commerce and omnichannel fulfillment continue to reshape distribution networks. Artificial intelligence is changing forecasting and decision-making. At the same time, retailers must balance speed and service with cost, labor availability and increasingly complex operations.

Those challenges, and the strategies leading retailers are using to address them, will be a major focus of the 2026 NextGen Supply Chain Conference, taking place Oct. 21-23 at the W Nashville in downtown Nashville.

Retail will represent one of several industry-focused paths attendees can follow throughout this year’s conference, alongside logistics and fulfillment, food and beverage, and chemicals and pharmaceuticals. Across keynotes, presentations, executive panels and interactive Small Group Sessions, attendees will hear directly from the practitioners implementing new technologies and operating models inside their organizations.

Leading the retail conversation will be two of the conference’s most prominent sessions: Thursday morning’s opening keynote from Wayfair and the Visionary Award keynote featuring Tractor Supply.

Wayfair opens Thursday programming with keynote

Following Thursday morning’s NextGen Supply Chain End User Awards, Nitin Kapoor, vice president of technology at Wayfair, will join Supply Chain Management Review Editor-in-Chief Brian Straight for a keynote fireside conversation, “Building the Future of Home Delivery: Wayfair's Logistics Evolution.”

To view the latest agenda, click here

To register for the conference, click here

Organizations interested in sponsoring the conference, click here

The conversation will examine how Wayfair has built and evolved its logistics network using technology to improve speed, reliability and scale. Kapoor will discuss innovations driving the company’s supply chain strategy, lessons from operating a complex home-delivery network and recent enhancements to Wayfair’s delivery offerings designed to improve the customer experience.

The session provides attendees with a look inside one of e-commerce’s most complex supply chain challenges: efficiently delivering large, bulky products directly to consumers while meeting increasingly demanding service expectations.

Tractor Supply brings a growth perspective to supply chain

Later Thursday, Craig Ledbetter, senior vice president and chief supply chain officer at Tractor Supply, will take the stage for the Visionary Award keynote, “Supply Chain as a Growth Engine.”

Ledbetter will receive the 2026 NextGen Supply Chain Visionary Award, which recognizes individuals and organizations that are helping redefine the role supply chain plays within the enterprise.

The keynote will examine a fundamental shift taking place in supply chain leadership. Rather than measuring supply chains solely through efficiency and cost, leading organizations increasingly expect the function to support growth, improve customer experience and create competitive advantage.

To view the latest agenda, click here

To register for the conference, click here

Organizations interested in sponsoring the conference, click here

Ledbetter will share lessons from Tractor Supply’s growth journey, including investments in network expansion, fulfillment capabilities, operational scalability and last-mile delivery, and discuss how supply chain leaders can better align strategy, technology and execution with business growth.

Retail Reinvented

Those individual perspectives will come together Thursday afternoon during the executive panel, “Retail Reinvented: Automation, Omnichannel Execution & the New Fulfillment Economy.”

The discussion will bring together leaders representing different points across the retail fulfillment ecosystem:

• Jeff Kellan, Division President, Omnichannel Retail in AmAPAC, GXO Logistics
• Jay Di Sieno, Senior Supply Chain Manager, Berry Direct
• Eric Watts, VP of Food Supply Chain Operations, Target
• Norman Katz, President & CEO, Katzscan, moderator

The panel will explore how changing consumer expectations, omnichannel fulfillment, automation investments, labor challenges and regulatory complexity are reshaping retail networks. Panelists will discuss how organizations are balancing service, speed, cost, compliance and profitability as fulfillment becomes more complex.

AI moves into retail execution

Artificial intelligence will also run through several of the retail-focused sessions.

Bijoy Sasidharan, director of analytics, capacity planning & forecasting at Fanatics, will present a real-world case study examining how the high-velocity retailer re-architected its e-commerce forecasting using agentic AI orchestration and a revitalized Forecast Value Add discipline.

The session will focus on measurable outcomes, including forecasting accuracy, faster responses to volatility and the ability to scale the approach across the operation.

On Friday, Debanshu Sharma, senior supply chain manager at Amazon, will present “From Reactive to Predictive: How ML-Based Carrier Risk Scoring Reduced Pickup Defects by 35%.”

To view the latest agenda, click here

To register for the conference, click here

Organizations interested in sponsoring the conference, click here

Sharma will explain a machine-learning framework built and validated across more than 150,000 loads and 1,600 carriers that generates carrier-level pickup risk scores. The approach reduced pickup defects in the high-risk carrier segment by 35%, achieved 85% model accuracy and generated more than $40 million in projected annual savings.

Additionally, Piu Ghosh, manager of product operations at Apple, will lead attendees through a discussion on the evolving talent landscape during a Small Group Breakout session on Thursday.

Together, the sessions illustrate a central theme of NextGen 2026: moving AI beyond experimentation and into decisions and workflows that produce measurable operational results.

Multiple paths through NextGen

Retail represents just one path attendees will be able to follow through the 2026 program.

The broader agenda is being developed around several of the industries and operating environments undergoing significant supply chain transformation, including logistics and fulfillment, food and beverage, and chemicals and pharmaceuticals.

That approach allows attendees to build an experience around their own priorities while still participating in broader discussions around artificial intelligence, automation, digital transformation, workforce development and operational execution.

The conference already features executives from organizations including Eli Lilly, GE Healthcare, Apple, Amazon, Stanford Medicine, DHL Supply Chain, Penske Logistics, GXO Logistics, Evonik, Southern Glazer’s Wine & Spirits and ODW Logistics, among others. Additional speakers and sessions will be announced as the conference approaches.

More than conference sessions

NextGen is also designed to create opportunities for attendees to connect outside the meeting rooms.

The conference opens Wednesday evening with a Welcome Reception and Networking event, giving attendees an opportunity to meet fellow supply chain executives, speakers and industry partners before Thursday’s programming begins. Nashville-based singer-songwriter Nick DeLeo, will entertain attendees during the opening reception in The Living Room at the W Nashville hotel.

Emma White, one of Rolling Stone’s “10 New Country Artists You Need to Know,” will bring her country-pop music sound to the Thursday attendee luncheon in the Zaytinya restaurant. The lunch and networking session is being sponsored by Gather AI.

Thursday concludes with an evening rooftop reception at the W Nashville, featuring live entertainment from Travis Hill under his performance name Scooter Carusoe. Hill co-founded Carnival Music and writes songs under the name Scooter Carusoe, including five No. 1 hits for Kenny Chesney, Darius Rucker and Brett Eldredge. He also has writing credits for artists such as Tim McGraw, Taylor Swift, Keith Urban, Rascal Flatts, Eric Church, Lady A, Uncle Kracker and Dierks Bentley.

To view the latest agenda, click here

To register for the conference, click here

Organizations interested in sponsoring the conference, click here

Additional entertainment and networking details will be announced ahead of the conference.

Sponsorship opportunities still available

The NextGen Supply Chain Conference continues to attract support from technology providers and service organizations looking to engage senior supply chain decision-makers. Current sponsors include:

• Diamond Sponsor Zion Solutions Group
• Platinum Sponsor Gather AI
• Gold Sponsors Cycle Labs, Dematic, Geek+, Dexory and Zimark
• Bronzer Sponsor Verity
• Associate Sponsor AutoScheduler and Argano

Sponsorship opportunities remain available, including a limited number of Gold Sponsorships. Gold Sponsors receive a premium speaking opportunity built around a 30-minute customer case study presented jointly with an end-user customer, giving attendees the opportunity to learn directly from organizations implementing supply chain technologies in real-world environments.

The 2026 NextGen Supply Chain Conference will take place Oct. 21-23 at the W Nashville in downtown Nashville, bringing together senior leaders from supply chain, logistics, procurement, operations and technology for three days of executive education, networking and peer-to-peer learning.

Registration is currently open, and additional speakers, sessions and entertainment will be announced in the coming weeks.

Executive takeaways

In 2025, GS1US conducted an online survey with 1,005 US adults in regard to food recalls and safety. Overwhelmingly, consumers are concerned about this issue, and are hesitant about purchasing a food item that has previously been recalled. 

The FDA (Food and Drug Administration) FSMA (Food Safety Modernization Act) Rule 204 was supposed to be implemented in January 2026, but that deadline has been extended to July 2028. This rule states that food and beverage companies (with products on the Food Traceability List) must retain certain supply chain data, at minimum, in a searchable spreadsheet, for at least two years. The key supply chain data is full lot traceability, manufacturing date, and expiration date of the item.

However, retailers and grocers are not waiting for the extended deadline for this item data: they are demanding it from their food and beverage vendors now. Retailers and grocers are apparently not willing to be more at risk then they are now; they want full supply chain traceability for the products they are selling to their consumers. This has become a supply chain vendor compliance issue. Failure to comply means financial penalty chargebacks and possible refusal to accept shipped goods.   

The FDA may only require the data be accessible in spreadsheets, but retailers and grocers expect more: they need this data conveyed to them with each shipment. Retailers and grocers communicate business transactions with their vendors essentially via one methodology: X12 EDI (Electronic Data Interchange). The transaction that carries this FSMA data is the EDI856 ASN (Advance Ship Notice). Lot identification, manufacturing data, and expiration date will be at either the tare (pallet), carton, or item level depending upon whether you mix lots per package and based on each retailer or grocer’s EDI856 specifications.

If you distribute using a 3PL (third-party logistics) facility, you will need to convey the FSMA data to your 3PL when you transfer the inventory to them, and your 3PL will advise you of the lots they have picked from for fulfillment. This is where the EDI943/EDI944 transaction pair for inventory transfers and inventory receipts, and the EDI940/EDI945 transaction pair for warehouse orders and warehouse shipments can be readily utilized. 

FDA FSMA Rule 204 compliance isn’t just about what your company needs to do internally to meet this regulatory rule. It’s also about what your company needs to do externally in working with your fulfillment partners and customers to ensure you are a non-disruptive vendor and are fully compliant with supply chain requirements. Remember what I always say: In a commoditized world, execution is the competitive edge. 

Executive takeaways

Walk into almost any mid-to-large manufacturing supply chain today and you will find three expensive things running side by side: a material planning engine (MRP), a transactional system of record (ERP), and a business intelligence layer full of dashboards. Each was bought to make the operation smarter. And yet, in a striking number of these organizations, the people running the supply chain still make their most important decisions in spreadsheets, on instinct, and after the fact.

The instinct in that moment is to buy something new, usually “AI.” But the problem is rarely a missing tool. The problem is that the tools an organization already owns are operating as three disconnected islands instead of one system. The planning engine runs on parameters nobody maintains. The dashboards report yesterday without changing what happens tomorrow. And the analytics inherit data the transactional system never reconciled. Layering artificial intelligence on top of that arrangement doesn’t fix it; it simply makes the disconnection faster and more confident.

What actually changes the game is unglamorous and rarely discussed: architecting MRP, ERP, and BI as a single, governed, closed-loop control layer. Not a pipeline that pushes data one direction, but a loop in which planning, execution, and analytics continuously inform one another around a single, trusted version of the truth. This article lays out what that control layer looks like, the governance discipline that keeps it reliable, and how a supply chain leader can begin building one because it is this foundation, not the algorithm on top of it, that separates the organizations getting value from their technology from the ones still exporting to Excel.

The disconnection tax

Consider the everyday failure modes that quietly drain performance. A lead time entered as a placeholder during an implementation years ago still drives safety stock today. A planning parameter set once, under deadline pressure, has never been revisited even as demand patterns shifted underneath it. A dashboard shows a beautiful trend line that no one acts on because it arrives a week after the decision window closed. Each of these is minor in isolation. Together, they compound into what I think of as a disconnection tax—a persistent drag on service, inventory, and cash that no single system owner sees because the failure lives in the gaps between systems rather than inside any one of them.

The reason this tax is so hard to eliminate is organizational as much as technical. MRP belongs to planning. ERP belongs to IT and finance. BI belongs to analytics. Everyone is optimizing their own island, and no one owns the water between them. The control layer is, at its heart, a way of making that water someone's responsibility.

The architecture: a loop, not a pipeline

The core shift is conceptual before it is technical. Most integration efforts treat the flow as linear: ERP feeds MRP, MRP produces a plan, BI reports on the results. That is a pipeline, and pipelines leak at every seam.

A control layer treats the three as a closed loop around a single reconciled source of truth. Execution data from the transactional system continuously updates the picture of reality. Planning consumes that reconciled reality, not a stale snapshot, to generate signals. Analytics observe both the plan and the execution, surface the gaps, and feed those insights back into how planning is parameterized and how execution is prioritized. The loop closes: what the operation learns this week changes how it plans next week.

The single most important design decision is the reconciled source of truth at the hub. When planning, execution, and analytics each carry their own version of on-hand, lead times, or open orders, people stop trusting all three and retreat to manual workarounds. Consolidating to one governed version is what makes every downstream output believable enough to act on. Without it, even technically correct analytics produce conflicting answers and conflicting answers are how you lose an organization’s confidence in the entire system.

The governance model: why integration alone fails

Here is the part most integration projects skip, and the reason so many of them decay within a year of go-live: integration without governance does not last. Systems connect beautifully on launch day and drift out of alignment steadily thereafter, because the data that feeds the loop is not static. It rots unless someone is accountable for keeping it true.

A durable control layer rests on four governance pillars.

Accuracy means the data reflects physical reality on an ongoing basis lead times that match what suppliers actually deliver, on-hand that matches what is actually in the building. This is not a one-time cleanse; it is a continuous reconciliation between the system and the real world.

Ownership means every critical data element and every parameter set has a named person accountable for it. Data with no owner is data in decay. In my experience, assigning unambiguous ownership of the parameters that drive planning is the single highest-leverage organizational change most supply chains can make and it costs nothing.

Maintenance means parameters are reviewed on a cadence tied to how fast the underlying conditions change, rather than set once and forgotten. Reorder points, planning fences, and lot-sizing rules get a scheduled review the way equipment gets scheduled maintenance. It is unglamorous, and it is precisely what separates a system that ages well from one that quietly becomes a liability.

Integration means the connections are maintained as a first-class asset, not assumed to hold forever. When a source system changes, the loop is updated deliberately, not discovered to be broken during a stockout.

These four are what convert a clever architecture into a durable one. The architecture is the what; governance is the why it still works two years later.

From reporting to activation

The final component is the one that changes the daily experience of the operation. Most BI stops at reporting: it tells you what happened. A control layer’s analytics are built to activate to surface the handful of exceptions that actually require intervention and route them to the person who can act while the decision still matters.

This is a shift in tempo more than technology. Instead of a monthly review of what went wrong, the operation moves to continuous surfacing of what is about to go wrong—the emerging shortage, the excess building quietly, the supplier drifting off pace. The measure of a control layer is not how many dashboards it produces but how much sooner the organization intervenes.

In practice, an operation that makes this shift tends to see the same pattern of results: materially improved inventory accuracy, tighter adherence between plan and execution, and a planning function that has moved from reactive firefighting to proactive management. The specific magnitudes vary by starting point, but the direction is consistent because the gains come from closing the loop, not from any single feature.

How to start

For a supply chain leader looking at three disconnected systems and wondering where to begin, the sequence matters more than the technology choice.

Start by establishing the single reconciled source of truth and getting the organization to trust it. That trust is the foundation everything else stands on. Next, assign explicit ownership of the critical master data and planning parameters; this is organizational work, not an IT ticket, and it requires someone senior to protect it from being deprioritized. Then institute the maintenance cadence, so the foundation stays true. Only once that governed loop is running should you turn analytics from reporting toward activation and only after that should you consider layering AI on top. AI applied to a governed control layer compounds your advantage. AI applied to disconnected systems compounds your problems.

The real prerequisite

The organizations that win with supply chain AI over the next few years will not be the ones with the most sophisticated models. They will be the ones that did the boring architectural work first that turned three expensive islands into one governed loop, and made the water between the systems someone’s job.

The control layer is not a product you buy. It is a discipline you build. And it is the prerequisite that has to be true long before the AI is ever switched on.

About the author

Sirajudeen Shahul Hameed is a supply chain and manufacturing operations leader specializing in ERP and MRP modernization, integrated planning architecture, and large-scale supply chain transformation.

Executive takeaways

Editor’s note: How Do you Really Do It? is a monthly series on Supply Chain Management Review designed to clarify how organizations can adopt common supply chain strategies. The series is authored by Andrew Byer, a former P&G supply chain leader, and Mike Dobslaw, who leads EY’s Supply Chain Planning Practice, and appears on the second Thursday of the month. 

A common supply chain target is to operate with “real-time visibility.” But having real-time visibility data is not the same as being able to use that data to impact results. So, how do you really do it?

First, a quick definition of real-time visibility

In supply chain vernacular, the term means getting concurrent status updates in a digital form for elements of the operation at a specific point in time and, often, forward projections based on the actual status and outlook. Think of this as a GPS + health and activity monitor for your supply chain. This visibility is converted to useful information about your supply chain. For example, real-time visibility can tell you where trucks are on the road and their ETAs, how your lines are running (or whether they are down), and demand trends or sales vs. expectation vs. levels of inventory. The potential value of real-time visibility is immense. For example, making better informed decisions (as simple as “load this trailer first, because the driver of the other trailer is projected to arrive later”) or proactively offsetting risks. Another example is accelerating a shift to produce product that’s selling more in-market vs. forecasted items that are moving slower than expected (physical scans or online orders). These examples show that getting real-time visibility data is not sufficient—it needs to be acted upon to add value.

Visibility tools aren’t new

Some digital tools to convey supply chain data have been around for a while (think EDI since the 1960s, POS scanners since the 1970s or early forms of RFID during WWII). Telematics to operationalize the tools have been growing in capability, including third-party companies making it easier for data to get from suppliers to manufacturers, and from manufacturer to customers. More recent developments have been the setting of industry expectations and capabilities to transfer, receive and activate data. New signal capabilities are being created, some coming from mandates, e.g., electronic logging devices (ELD).

Finally, interoperability to connect data in system A to system B, whether within an enterprise or directly between suppliers and customers, has created more real-time visibility. An example of the latter might be knowing DC inventory levels of a SKU to better predict when replenishment may be triggered.

And while visibility tools aren’t new, they continue to change—especially with Agentic AI capabilities providing automation and touchless transactional operations happening at scale. These capabilities are enabling the creation of orchestration “control towers” that leverage visualization to accelerate decision-making and pre-empt potential issues.

How to use real-time visibility to impact results?

The first steps are to define and prioritize what real-time visibility data can add value to your supply chain planning and operations. A good place to start defining where to get value is by looking at output measures with gaps vs. targets (examples of typical output measures are customer service, costs and inventory). If you are able to close these gaps, it will likely directly impact the top or bottom lines. Well-known tools like value-stream mapping can help document current work process steps, sequence, and timing. This value-stream map can help make it clearer where real-time visibility data can improve or alter decision-making. For example, in a production-constrained operation, getting data showing an inbound raw material will arrive late creates opportunity to adjust plans for item production with sufficient materials on hand. Prioritization is next: assessing the opportunities to apply real-time visibility based on the relative “size of prize” and amount of time and effort required.

How to consistently operationalize visibility to impact results

Once identification and prioritization steps are complete, the next step in getting value from real-time visualization is using the data to enable better decisions. These decisions can be better developed plans or interventions into operations. If a company has real-time visualization data and is not using it to make better decisions, they are not generating added value from the visibility. Incorporating visualization data into formal systems (sales & operations execution, daily management systems, etc.) is a way to embed the capability into how work is done.

Importantly, a key need to operationalize visualization data is to confirm a smooth flow from source to use system—cleanly, consistently, and accurately. To change the work processes for employees to leverage real-time visibility data, that data must be consistently available. This includes format and timing. Systems that rely on users to check multiple sources “in case there’s a status update somewhere” are weak and will not stand up to the pressure of daily business needs.

Finally, supply chain leaders will want to assess how visualization data can enable shifts in accountability. For example, delegating key performance indicator (KPI) responsibility to frontline employees who use the tools and giving them increased accountability to take action and be accountable for overall results.

Benefits of implementing real-time visualization in a way that impacts results: Knowing what’s going on in your supply chain and using that intelligence to make better decisions or interventions is the top benefit. But there are other benefits, including:

• Extending the benefits of real-time visualization to suppliers and customers, where the whole supply chain ecosystem receives benefits (not just one node).
• Improved productivity by reducing surprises that require firefighting and high-touch resolution.
• Reduced costs from expediting to cover unforeseen gaps in execution vs. plan.
• Improved customer service and customer relations. The customer is the “finish line” of most supply chains; the cumulative impact of issues often shows up to your customer.
• Increased competitiveness. Businesses don’t operate in a vacuum. Your competitors are gaining benefits from real-time visualization, and companies cannot afford to fall behind.

Watchouts: Unfortunately, there can be many intended or unintended barriers to implementing real-time visualization in a way that impacts results:

• Not adapting workflows and user training to match the new data and capabilities; the decision-making processes don’t change to take advantage of better data.
• Not getting real-time visibility intelligence to the people who need it
• Creating too many visualization inputs but lacking in actionability (descriptive but not predictive and prescriptive — “the truck is at spot X”; not “the truck will be late” and “a new delivery appointment should be made right away …”)
• Lags that convert real-time signals into batch-like processes
• Mixed quality of the real-time signals—undermining trust and usability 
• Not empowering the people who access visualization tools to take action on the data
• Adding visualization tools “on top” of already-busy operators’ desks without seeking offsetting ways to free up time to leverage the capability

Summary: How to implement real-time visualization in a way that impacts results?

Define where result improvement is needed. Map out the workflows tied to that result to understand where better data can enable better results (typically through better decision-making). The key is understanding the existing workflows and then identifying how available but untapped real-time data (e.g., truck sensors, machine sensors, POS data) might better inform decision-making. Once the use of real-time data is proven to improve decision-making, operationalize the process by updating the standard workflows.

About the authors

Andrew Byer is a former P&G Supply Chain Leader. Mike Dobslaw leads EY Global Supply Chain Planning Practice. To learn more about how EY and P&G team to support Supply Chain Transformations please write michael.dobslaw@ey.com

The views reflected in this article are the views of the author(s) and do not necessarily reflect the views of Ernst & Young LLP or other members of the global EY organization.

Executive takeaways

Supply leaders are “in it” right now. As of this writing, the conflict surrounding the Strait of Hormuz has dominated headlines for months. Cargo ships have been forced to reroute around Africa. The global oil supply has been shocked like never before. Other industries like manufacturing and agriculture are feeling the effects, too. And this is just the latest in a never-ending series of disruptions.

Tammy Kulesa

It’s easy to feel like there’s a cloud constantly hanging over global supply chains. Each disruption has palpable business impacts and aftershocks. Leaders are accommodating more variables than ever before. But it’s not all doom and gloom. If you take a step back, you’ll see more than just clouds in the big picture. You’ll see the sunny side of supply chain management.

As challenging as each disruption may be, our ability to respond and pivot has improved by an order of magnitude in recent years. AI and advanced technology have made our supply chains smarter and more resilient than we previously thought possible. Supply chain teams aren’t relegated to monotonous tasks like data entry; instead, they’re freed up for more strategic, value-additive work.

Business intelligence is at an all-time high and, actually, the global supply chain is the strongest it’s ever been. That’s something worth celebrating.

Let’s face it: supply chain management is hard

Between fluctuating tariff policies and geopolitical instability, the past 12 months have been challenging. But frankly, these challenges are just the cherry on top of a foundation of persistent disruptions.

Even under ideal operating conditions, supply chain management involves countless moving parts: collaborating with partners; developing informed business plans; pivoting when those plans inevitably change; making investment decisions to support stronger decision-making; keeping up with consumer demand trends—the list goes on.

Supply chain management isn’t for the faint of heart, and managing all those moving parts can often feel like trying to juggle a dozen balls at once. And when you do get a grasp on one of the variables, making even the slightest adjustment causes a cascade of downstream impacts that can be hard to trace.

The sunny side of supply chains

But here in 2026, we can manage all these moving parts better than ever before—and it’s all thanks to advanced technology.

• Predictive AI is helping supply chain leaders continuously monitor risk signals, model potential impacts, and build contingency plans before disruption reaches critical operations.
• AI-powered decision support and agentic AI brings teams closer to the technology than was previously possible, democratizing data access and generating outputs in natural language.
• A connected supply chain data foundation accelerates planning and execution motions to a mind-boggling degree, making cross-party collaboration and information sharing near instantaneous.

In other words, businesses can plan better, pivot faster, be smarter, and collaborate more efficiently. Leaders have unparalleled visibility into each decision’s downstream effects. These gains not only support smarter, faster operations but also industry morale.

What the data tells us

Blue Yonder recently polled supply chain leaders about their organizational readiness and optimism for the future. On a scale from -5 (extremely pessimistic) to +5 (extremely optimistic), the median response was +2.9. On average, leaders feel moderately optimistic about the future of their supply chains.

Those who are most confident about the future feel that way due to their end-to-end oversight and visibility. What’s more, leaders who use advanced technology are 10 times more likely to say they’re fully prepared to handle significant disruptions.

This is a big deal! Companies around the world are deriving significant value from their technology investments; their businesses are more agile and resilient than ever and can respond nimbly to disruptions as they come.

Compare this to the Covid years—another notoriously challenging era for businesses around the world. During the 2020–2021 peak, shipping delays were 12 times worse than average. Businesses often relied on just-in-time fulfillment, leaving them incredibly vulnerable to any change of plans. There was a lot more global reliance on China, and we didn’t have nearly as much visibility into suppliers and other partners.

Today, businesses have secondary and tertiary suppliers in place from more varied geographies; they’re using AI to make inventory management more intelligent and data-informed than ever; networked visibility gives teams real-time insight into partners’ availability and constraints.

Yes, companies are regularly beset with disruptions and challenges. But compared to just a few years ago, we’re in a significantly better position to respond to them and move forward. With fewer barriers blocking their view of the horizon, supply chain leaders can see for miles.

Staying in the light

As leaders are keenly aware, there are innumerable business factors beyond their control; what they can control is their team’s preparedness, their leadership style and their overall attitude. Focusing on these factors will give you the best view of the sunny side.

Give your team the tools and context they need

Where possible, keep investing in new tech solutions that reduce clerical work and improve efficiency. Ask employees about their day-to-day challenges and fix what’s fixable. Make it a point to make their lives easier.

As you bring new tools into the fold, double down on change management. Help teams understand why the new tools are in place and how they’ll help. Lead with empathy and give teams the gift of clarity.

Protecting team morale is just as important

Reduce feelings of chaos by helping teams with prioritization. Not everything is an emergency. Help teams understand what needs to happen today and what doesn’t.

Strike a balance here by resisting the urge to micromanage. Let your people make strategic decisions; let them propose solutions before you share your ideas. Over-involvement will erode their confidence.

The goal isn’t perfection; it’s resilience. Acknowledge effort, not just outcomes, and celebrate every win you can.

Finally, you’ve got to walk the walk

Don’t just believe in the power of effective, intelligence supply chain planning. Celebrate it. Lean into your data insights, geek out about all the visibility you have and get caught doing it.

This doesn’t mean being performative or disingenuous. In fact, you should be transparent when things don’t work out. Setbacks happen; they’re a part of the business. Transparency and trust are stabilizers amid disruption.

Any guiding light is helpful in a storm. Show others not just your vision for the path forward, but the steppingstones you’ll take to get there.

We’re in our best position yet to tackle disruptions and find new ways forward. We have the technology to help us get it done; we have the capability to assess complex situations and make informed decisions; we can choose to look on the sunny side of supply chains.

About the author

Tammy Kulesa is Senior Director, Supply Chain Execution at Blue Yonder.

Tariffs are adding another layer of cost and uncertainty to already complex global supply chains. But duties are only one part of the cost equation. As organizations adjust sourcing strategies, supplier relationships and inventory positions, supply chain leaders need to look across the operation for opportunities to offset rising costs while maintaining flexibility and resilience.

In this roundtable discussion, we’ll explore how companies can respond to tariff volatility through smarter procurement and contract management, greater visibility into costs and supplier obligations, and more flexible approaches to inventory and storage.

We’ll also examine how data, technology and cross-functional decision-making can help organizations manage costs today while remaining agile enough to respond to whatever comes next.

Featuring: 
T.J. LaSalle, Principal Solution Consultant and Global Procurement Practice Leader, Agiloft and Jonathan (John) Brooks, Chief Executive Officer, Warehouse on Wheels

Executive takeaways

Retailers have largely won the race to advertise faster delivery. The more difficult challenge is determining whether those promises create profitable growth. Customers increasingly expect same-day and next-day delivery as standard offerings rather than premium services. Studies indicate that 61% of consumers expect free next-day shipping and more than half expect free same-day delivery, signaling that delivery speed has shifted from a competitive differentiator to a baseline expectation. Faster and more reliable fulfillment also strengthens customer satisfaction, trust, repurchase intent, and brand advocacy.

Yet every reduction in delivery time fundamentally changes the economics of inventory, transportation, and fulfillment. Companies often view faster delivery as a transportation problem when it is, in reality, a network design problem. While many organizations can technically offer same-day or next-day delivery, far fewer can do so profitably at scale.

I refer to the framework for making these decisions as Delivery Promise Engineering (DPE), a methodology for designing fulfillment networks that balance customer expectations with economic performance. Rather than asking, "Can we deliver faster?" DPE asks a more important question: "Where does faster delivery create sustainable value?"

Delivery Promise Engineering evaluates four interconnected dimensions:

(Photo: Author)

Demand density: The foundation of speed

Many organizations benchmark Amazon’s delivery promises without recognizing that Amazon’s competitive advantage is not simply its transportation network, it is the demand density that allows that transportation network to operate efficiently. Demand density measures the concentration of customer orders within a geographic area and is arguably the single greatest driver of fulfillment economics. Higher concentrations of orders enable more deliveries per route, shorter travel distances, better vehicle utilization, and lower labor costs.

The financial impact is significant. Last-mile delivery typically costs between $8 and $15 per order and can account for more than half of total logistics expenses. Industry estimates suggest that reducing average delivery distance from more than 30 miles to less than five miles can reduce delivery costs by more than 50%

Consider two fulfillment networks processing the same 100 daily orders. In one scenario, those orders are concentrated within a five-mile radius, allowing drivers to complete 20 to 25 stops per hour while traveling relatively short distances between deliveries. In the second scenario, the same order volume is dispersed across a 50-mile service area, reducing productivity to five to eight stops per hour and significantly increasing travel time. Although both networks fulfill the same number of customer orders, the high-density operation may deliver at roughly one-third the labor cost.

Demand density ultimately determines where rapid fulfillment is economically viable. Same-day delivery often succeeds in dense metropolitan markets because concentrated demand supports dedicated routes and high stop density. Rural markets, by contrast, require longer routes, fewer deliveries per hour, and substantially higher transportation costs.

The strategic question therefore is not whether a company can offer next-day delivery. It is where demand density is sufficient to support that promise profitably

Inventory proximity: Speed comes at a price

High demand density creates the economic foundation for rapid fulfillment. The next question is whether inventory is positioned close enough to capitalize on that demand.

Reducing the distance between inventory and customers shortens transit times and improves delivery reliability. However, every additional fulfillment location fragments inventory across the network and increases working capital requirements.

As delivery promises compress from two days to next-day and ultimately same-day, retailers must decentralize inventory across more stocking locations. Safety stock increases, replenishment becomes more complex, and inventory balancing becomes increasingly difficult. While the exact impact varies by SKU portfolio, demand variability, and service levels, inventory investment grows disproportionately as delivery expectations accelerate.

Supporting same-day and next-day fulfillment often requires inventory decentralization that can double safety stock when expanding from one to four stocking locations and triple it when expanding to nine locations. The result is higher carrying costs, lower inventory productivity, and greater exposure to markdowns and obsolescence.

The relationship is straightforward: Speed is not free. It is financed through inventory.

Illustrative Impact of Delivery Speed on Inventory Requirements

Illustrative network scenario; actual requirements vary based on SKU breadth, demand variability, service targets and network design

Transportation reach: The power of network architecture

Even with inventory positioned closer to customers, geography alone does not determine delivery performance. Network architecture determines how efficiently that inventory reaches the customer.

Transportation reach depends on more than warehouse locations. Middle-mile optimization, regional sortation, zone-skipping, postal injection, distributed order management, and intelligent carrier selection can dramatically expand one- and two-day delivery coverage without requiring additional fulfillment centers.

These strategies reduce parcel zones, improve carrier utilization, shorten transit times, and often lower transportation costs simultaneously.

Amazon’s regionalization initiative demonstrates the potential impact. By redesigning its fulfillment network around regional demand patterns rather than relying solely on national inventory pools, the company increased the number of items eligible for same-day or next-day delivery while simultaneously reducing transportation costs.

This illustrates an important principle: transportation optimization is frequently the most capital-efficient lever available. Before investing hundreds of millions of dollars in additional facilities, organizations should determine whether better network design can deliver comparable service improvements at a fraction of the cost.

Cost-to-serve: The hidden economics of delivery speed

The previous three dimensions ultimately converge in a single metric: cost-to-serve.

Cost-to-serve extends well beyond parcel expenses. It encompasses fulfillment labor, inventory carrying costs, transportation, facility investments, technology, inventory imbalances, and the operational complexity required to sustain accelerated delivery promises.

Perhaps more importantly, these costs interact with one another. A decision that improves one metric often worsens another.

Adding fulfillment centers may reduce transportation costs while increasing inventory investment. Expanding inventory availability may improve service levels but reduce inventory turns. Faster delivery promises may increase conversion rates while simultaneously eroding contribution margins.

The relationship between delivery speed and cost is nonlinear. Moving from two-day to next-day delivery may increase fulfillment costs by 30% to 50%, depending on network characteristics. Moving from next-day to same-day delivery can produce another substantial increase as inventory duplication rises, transportation density declines, and specialized delivery capabilities become necessary.

This explains why many organizations successfully offer same-day delivery for selected products or metropolitan markets but struggle to scale those offerings nationally. Beyond a certain point, the incremental cost of faster delivery exceeds the incremental customer value it creates.

The objective is therefore not to minimize transportation costs or maximize delivery speed independently. It is to optimize total network economics.

Engineering the right delivery promise

Delivery Promise Engineering is not about maximizing delivery speed. It is about engineering the right delivery promise for the right customer, in the right market, at the right cost.

Higher demand density improves transportation efficiency while reducing the inventory required to support rapid fulfillment. Bringing inventory closer to customers shortens transit times but increases working capital. Transportation optimization can expand delivery reach without requiring additional facilities. Cost-to-serve integrates these tradeoffs into a single economic view of network performance.

Rather than asking, "Can we offer same-day delivery?" supply chain leaders should ask, "Where can we profitably offer same-day delivery?"

The answer will vary by ZIP code, product category, customer segment, order profile, and network design.

Delivery promises have become strategic products in their own right. Like any product, they require engineering—not only to satisfy customers but to generate sustainable returns. The companies that succeed over the next decade will not necessarily be those that promise the fastest delivery everywhere. They will be the organizations that understand where speed creates customer value, where economics begin to break down, and how to continuously design fulfillment networks that balance service, cost, and long-term profitability.

About the author

Nitin Kumar has more than 10 years of experience in logistics and transportation within the parcel industry, specializing in strategic growth, last-mile delivery, and network optimization. He is currently the director of planning & analytics at Fanatics, with previous experience leading teams at Walmart, OnTrac, Wayfair, and Shopify in the transportation space.

Executive takeaways

Supply chain AI investments are generating a familiar pattern. A planning system demonstrates impressive optimization results in a controlled evaluation: better demand forecasts, improved inventory positioning, reduced safety stock requirements, more accurate supplier lead time predictions. The business case is approved. The system goes live. And then, somewhere between the planning recommendation and the execution of that recommendation, the ROI calculation quietly unravels.

This is not primarily a model quality problem. It is a handoff problem. The handoff between planning and execution in supply chains was already difficult before AI entered the picture. AI makes it faster, more consequential, and harder to diagnose when it fails.

What the handoff actually involves

A planning AI recommendation is a set of conclusions drawn from aggregated data about demand signals, inventory levels, supplier capacity, lead times, transit constraints, and cost parameters. That data is never perfectly current. It is a representation of reality as of the last update from each contributing system.

The execution environment, on the other hand, is operating in real time. A distribution center is running against actual pick rates and actual dock schedules. A supplier is operating against actual production constraints that may have changed since the last portal update. A transportation lane is subject to conditions the planning model’s data did not capture. When a planning recommendation reaches execution, the question is not only whether the recommendation was mathematically optimal. It is whether the assumptions the model used still hold in the environment where execution has to act.

When they do not, the recommendation creates work rather than value. Someone in execution has to recognize the gap, decide whether to follow the recommendation anyway or deviate, document or not document the deviation, and absorb the consequences if the deviation creates a downstream problem. That recognition-decision-documentation loop is invisible to the planning system and often invisible to the leaders who approved the AI investment.

Three places where AI supply chain ROI actually disappears

Data latency at the decision point. AI planning systems are typically fed from batch ETL processes, nightly file transfers from suppliers, or periodic data syncs from warehouse management systems. When those feeds are delayed, the AI operates on a version of reality that is hours or days old. A demand spike, a supplier disruption, a quality hold, or a transportation disruption that happened after the last data sync will not appear in the planning model’s recommendation. Execution teams discover the gap and compensate manually, which means the AI recommendation is ignored for the cases where it most needed to be right. A concrete version: a planning system recommends accelerating a purchase order for a component supplier, based on a lead-time value last updated in the supplier portal three weeks ago. The supplier’s actual lead time has extended by 10 days because of a production line change. The execution team learns this only when the order is placed. The AI recommendation was mathematically optimal against data that no longer reflected reality. That gap, multiplied across hundreds of SKUs and dozens of suppliers, is where AI supply chain ROI quietly disappears.

Ownership gaps at the exception. When a planning recommendation cannot be executed because a supplier cannot deliver on the committed date, or because a product attribute has changed, or because a customer entitlement rule creates a conflict, someone has to decide what to do. In many organizations, the exception handling process for AI-generated recommendations was never defined. The recommendation was built. The exception path was not. Execution teams route around it, create informal workarounds, or escalate to the planning team, which re-runs the model with manually corrected inputs. The result is a combination of AI-generated recommendations and human-corrected exceptions that is harder to manage than either pure approach would have been.

Feedback loops that do not close. The planning model does not learn from execution deviations unless those deviations are captured, classified, and fed back into the model’s training or parameter set. In most deployments, deviations are captured informally or not at all. The model continues to recommend based on parameters that do not reflect what execution actually experiences. Over time, the gap between what the model recommends and what execution can actually achieve grows, and the business attributes the problem to model quality when the real issue is a feedback loop that was never designed.

What to build before expanding AI planning autonomy

Supply chain leaders who want to protect their AI ROI should treat three capabilities as prerequisites before expanding planning autonomy. Data contracts between the planning system and each contributing source: a defined freshness standard, an owner for each data class, and a specified behavior for the planning system when the data does not meet the standard. A formal exception taxonomy: a classification of the types of deviations that occur at the planning-execution handoff, ownership assignments for each, and a structured path from exception to resolved record to model feedback. A measurement framework that captures execution fidelity, not only planning accuracy: how often did execution follow the recommendation, how often did it deviate and why, and how did the deviations affect actual outcomes versus the model’s projection?

A measurement framework that captures execution fidelity is the third prerequisite, and it is the one most often skipped. Execution fidelity measures how often the AI recommendation was followed, how often it was overridden and why, and how the actual outcome compared to the model’s projection. Those numbers are not a report card for the AI. They are a signal for where the handoff infrastructure needs investment. High override rates in a specific product category usually indicate a data freshness problem in that category. High deviation rates on a specific supplier usually indicate a service-level metadata gap. Measuring fidelity gives the planning and execution teams a shared language for improving the system rather than arguing about whose number is correct.

The organizations that extract durable value from supply chain AI are the ones that invest as seriously in the handoff infrastructure as in the model itself. Planning AI is valuable. Planning AI that closes the loop between its recommendations and execution reality is compoundable.

About the author

Hemang Upadhyay is a senior product and AI leader with 16+ years of experience across enterprise AI product strategy, digital commerce, product data governance, PIM/CMS/DAM systems, and AI-enabled customer experience. His work focuses on moving AI from pilots into governed, accountable production systems.

Executive takeaways

The debate around AI and jobs is often looked at as a question of replacement. Will technology lead to job losses, reduce headcount and make parts of the workforce obsolete?

In the supply chain sector, evidence suggests hiring is slowing across several key functions, particularly those where AI is expected to automate routine activities. Zero100’s recent analysis of 115,000 LinkedIn job postings found sourcing hiring fell 31% and planning hiring fell 13% in the past year. Looking ahead to 2030, survey respondents expect headcount reductions of around 8% in planning, logistics and sourcing.

But hiring data alone tells only part of the story. While demand is falling for some roles, organizations are simultaneously investing in new capabilities and redesigning teams around AI.

Creating value, not cutting headcount

Indications are that the overall goal in the sector is that AI improves how supply chains operate and create value. The near-term business case for AI is centred on better decisions, better outcomes and immediate savings, rather than labor productivity alone.

Putting it succinctly, one chief procurement officer we spoke to argued that the opportunity presented by AI is not reducing a team of 100 people to 90, but enabling those same 100 people to generate twice the value through better sourcing and hedging decisions.

That view was indicative of the conversations we’re having generally with sector leaders, who are facing a range of new challenges that are difficult to predict and even harder to manage. The list is ever-growing: volatility around tariffs, disruption to critical shipping corridors, floods, droughts and other climate-driven disruptions.

With that context in mind, rather than focusing solely on where AI can reduce labor requirements, many companies are exploring how it can improve decision-making, strengthen resilience and create value—and what that means for how future supply chain teams are put together.

Human-machine harmony

The future of supply chain work is unlikely to be a choice between workers and AI. Instead, organizations are moving toward human-machine teams, where a portion of tasks will be machine-led with human oversight or human-led with machine augmentation. AI handles increasingly complex analysis and execution while people focus on judgment, governance and navigating ambiguity.

As agentic systems become more capable, conducting operations with a greater level of independence, the most valuable human contribution may be deciding when to intervene, escalate or challenge what the technology recommends or actions.

What AI does change is the mix of skills organizations need. The reality is that some jobs will be under more pressure from automation than others. The most impacted roles are expected to be supply planners, buyers, sourcing analysts, sourcing contract managers, logistics analysts and route planners.

While the jobs that appear to be more at risk vary in function, they share a common characteristic in that much of their work involves analyzing large volumes of information, identifying patterns, modelling scenarios and coordinating routine decisions. As AI becomes more capable of handling these tasks, the human contribution shifts toward oversight, judgment and exception management.

At the same time, demand is growing for a different set of capabilities. More than 60% of supply chain leaders in our recent report expect increased hiring demand for digital product owners, while over 70% anticipate growth in data architecture and engineering roles directly within supply chain.

These roles are the essential building blocks of fusion teams—the tech-ops organizational structures that will redefine supply workflows and build technology solutions. In practice, that might look like stress-testing digital twins—real-time virtual supply chain replicas—against climate events and geopolitical disruptions using satellite and social sentiment data. Another example could be multi-agent systems that manage inventory decisions in response to supplier disruptions across global networks before they occur.

The role of retraining

The challenge is not just identifying the skills that will be needed, but developing them within the workforce. Having deep knowledge of core functional processes of supply chain will still be a valuable foundation for AI-first roles, meaning retraining may be mutually beneficial for companies and displaced employees, helping experienced professionals apply their expertise in new ways.

Easing the transition, a number of roles may evolve rather than disappearing altogether. For example, procurement analysts may become procurement data managers and procurement managers may transition into AI-enabled sourcing strategy leads. We are already seeing this happen in practice. One client, a food and beverage company, built nine sourcing-focused AI agents and then partnered with us to map how existing procurement roles could evolve alongside them, rather than simply disappear.

But the likely reality is that these initiatives will vary from company to company, and from individual to individual. Not every employee has the inclination or necessary skills to shift into that kind of role. But it does appear that the supply chain sector is building out AI capability to create smarter, more resilient supply chains and unlock greater value, rather than simply drive efficiencies.

And that means a step closer to human-machine teams becoming the norm. The future is most likely to look like humans and increasingly autonomous systems working across a continuum rather than one replacing the other outright.

The most important question facing supply chain leaders may not be how quickly they adopt AI, but how they redesign their organizations and talent pipelines to make the most of it. As technological capabilities advance, talent strategy may become the defining factor in determining who gets the most value out of AI.

Executive takeaways

Technology alone rarely transforms a supply chain. Transformation happens when organizations align around a common objective, combine complementary expertise and execute with discipline.

That philosophy is embodied by Ryder System, Inc. and BJC HealthCare, recipients of the 2026 NextGen Supply Chain Conference Partnership in Execution Award for redefining how healthcare supply chains can operate when logistics excellence is aligned with patient outcomes.

Presented during Thursday morning’s general session, the award recognizes partnerships that deliver measurable business value by combining operational innovation, technology and execution. Rather than recognizing either organization individually, the award celebrates how collaboration between partners can achieve results neither could accomplish alone.

For Ryder and BJC HealthCare, that collaboration fundamentally changed how medical supplies move through one of the nation's leading healthcare systems. Thys Visser, Vice President of Operations, Healthcare & High Tech for Ryder, and Jason Luby, Vice President, Value Chain Management & Sourcing Operations, for BJC HealthCare, will sit down for a fireside chat on Thursday, Oct. 22, 2026, to discuss their partnership.

All of the 2026 NextGen Supply Chain Conference awards, which include the End User, Solution Provider, Startup, Partnership in Execution, and Visionary, are sponsored by Zion Solutions Group.

A new model for healthcare logistics

Like many healthcare organizations, BJC HealthCare historically managed a fragmented supply chain characterized by multiple supplier channels, inconsistent delivery performance and unnecessary manual work performed by clinical staff.

Rather than accepting those limitations, BJC partnered with Ryder to rethink the operating model from the ground up.

To view the latest agenda, click here

To register for the conference, click here

Organizations interested in sponsoring the conference, click here

Drawing on logistics best practices developed across industries, including automotive and advanced manufacturing, Ryder worked with BJC to design and launch a highly automated 416,000-square-foot Consolidated Services Center (CSC) that now serves as the centralized logistics hub supporting the health system's eastern region.

The facility supports 14 hospitals, more than 3,300 licensed beds and approximately 4,600 physicians, consolidating supplier channels into a single scalable operation that improves inventory management, delivery performance and operational consistency across the enterprise.

Putting patients at the center of the supply chain

While the project introduced sophisticated automation and advanced logistics technologies, its primary objective was never simply operational efficiency. It was ensuring caregivers always have the supplies they need to care for patients.

Ryder engineered highly sequenced deliveries that prioritize the most critical products for immediate availability at hospital loading docks, helping clinicians spend less time searching for supplies and more time caring for patients.

Supporting that strategy is an integrated warehouse management system connected with RyderShare, Ryder’s proprietary visibility platform, providing end-to-end inventory visibility, demand planning and collaborative issue resolution across the supply chain, even during natural disasters, product shortages and other disruptions.

Perhaps most importantly, the partnership transformed BJC’s role within its own supply chain. Rather than reacting to shortages and supplier constraints, the health system now has direct sourcing capabilities, complete inventory visibility and real-time cost transparency that enable more proactive decision-making and stronger operational resilience.

Results that extend beyond the warehouse

The operational improvements have been substantial.

Hospital order fulfillment increased from 90% to more than 99%, while on-time, in-full delivery performance nearly tripled—from 27% to 75%.

Order processing costs fell by 80%, inventory-on-hand declined by 25 days, and nursing unit service levels climbed above 98.5%, ensuring clinicians consistently have the supplies needed at the bedside.

The operation also achieved 100% inventory control with real-time, end-to-end visibility, giving supply chain leaders unprecedented insight into inventory, demand and system-wide performance.

NextGen judges recognized the Ryder-BJC collaboration for demonstrating how deep strategic partnerships can produce transformational results that extend well beyond operational efficiency. By aligning logistics expertise with clinical priorities, the organizations created a patient-centered supply chain model that improves resilience, transparency and quality of care while establishing a new benchmark for healthcare supply chain execution.

More than an award presentation

Unlike many industry recognition programs, the NextGen Supply Chain Awards are designed to turn success stories into practical learning opportunities.

Award recipients share the challenges they encountered, the decisions they made and the measurable outcomes they achieved, providing attendees with actionable ideas they can apply within their own organizations.

The Partnership in Execution Award is part of a broader conference program featuring executives from organizations including Wayfair, Eli Lilly, Tractor Supply Company, Apple, Amazon, Stanford Medicine, Target, DP World, Fanatics, Evonik and many other industry leaders. Across keynote presentations, fireside conversations, executive panels and interactive Small Group Sessions, attendees will explore artificial intelligence, automation, digital transformation, workforce development and operational execution through real-world case studies.

Sponsorship opportunities continue to fill

The NextGen Supply Chain Conference continues to attract strong industry support from leading technology providers and service organizations committed to advancing supply chain innovation. Current sponsors include:

• Diamond Sponsor Zion Solutions Group
• Platinum Sponsor Gather AI
• Gold Sponsors Cycle Labs, Dematic, Geek+, and Dexory
• Bronzer Sponsor Verity
• Associate Sponsor AutoScheduler

Organizations interested in participating still have opportunities available, including a limited number of Gold Sponsorships.

Gold Sponsors receive a premium speaking opportunity featuring a 30-minute customer case study presented jointly with an end-user customer, allowing attendees to hear firsthand how organizations are solving today’s most pressing supply chain challenges through measurable business outcomes. With just 7 Gold sponsorship opportunities remaining, organizations interested in participating are encouraged to reserve their space soon.

Learn more about sponsorship opportunities here: https://www.nextgensupplychainconference.com/sponsors/

Experience NextGen in Nashville

The 2026 NextGen Supply Chain Conference will take place October 21-23 at the W Nashville in downtown Nashville, bringing together senior leaders from supply chain, healthcare, manufacturing, retail and technology for three days of executive education, networking and peer-to-peer learning.

Early-bird registration is currently available for professionals looking to gain practical insights from organizations leading supply chain transformation.

Companies interested in connecting with senior supply chain decision-makers can also explore remaining sponsorship opportunities. A limited number of Gold Sponsorships remain available, each including a 30-minute customer case study presented jointly with an end-user customer.

Whether you’re building a more resilient healthcare supply chain, strengthening strategic partnerships or modernizing logistics operations, the NextGen Supply Chain Conference offers direct access to the executives leading these transformations.

Executive takeaways

The world’s attention is fixed on AI’s transformative potential, from generative models reshaping enterprise workflows to autonomous systems redefining industries. But beneath the headlines, a quieter crisis is unfolding in the semiconductor supply chain. The accelerating demand for NAND flash memory is outpacing the industry’s ability to supply it, and AI is the primary driver.

Piu Ghosh

Since 2022, hyperscaler AI infrastructure investment has grown at an estimated 35% to 60% annually, far exceeding NAND capacity expansion in the roughly 8% to 15% range. This widening gap is reshaping enterprise storage economics as AI-driven demand accelerates faster than manufacturing capacity can scale.

AI demand is changing the NAND market

Historically, NAND demand was driven by smartphones, PCs, and consumer electronics with relatively predictable refresh cycles. AI infrastructure is creating a fundamentally different demand pattern.

Training large language models, scaling inference systems, and expanding AI data centers require massive storage capacity and higher-performance enterprise SSDs. As organizations race to deploy AI capabilities, memory demand is rising simultaneously across cloud infrastructure, enterprise AI platforms, edge computing, autonomous systems, and hyperscale data centers.

Traditional planning models calibrated for roughly 15% annual growth are struggling to absorb this shift.

The supply side can’t keep up

NAND production remains concentrated among five manufacturers—Samsung, SK Hynix, Micron, Kioxia, and Western Digital—which together control more than 95% of global capacity, according to IDC. While the industry is investing in next-generation 3D NAND beyond 232 layers, new capacity still requires 2 to 3 years and more than $20 billion to scale. At the same time, transitions to higher layer-count architectures—including Samsung’s 280-layer V8 NAND and SK Hynix’s 321-layer production ramp—are temporarily reducing effective wafer output by approximately 8% to 15%, according to TrendForce.

Traditional forecasting models are breaking down

AI demand behaves differently from previous technology cycles. Enterprise adoption can accelerate rapidly, while inference workloads continue expanding as organizations deploy AI applications at scale.

AI workloads also exhibit approximately 3x to 4x higher volatility than traditional enterprise IT demand, making static forecasting increasingly unreliable.

In response, supply chain leaders are shifting toward scenario-based planning systems, predictive analytics, digital twins, and AI-driven forecasting tools capable of evaluating multiple disruption and demand conditions simultaneously.

The transition toward adaptive infrastructure planning has begun, but not fast enough.

A framework for evaluating exposure: the NAND Resilience Index (NRI)

As NAND supply chains become increasingly critical to AI infrastructure, procurement leaders need more sophisticated ways to evaluate exposure to memory supply disruption.

To address this challenge, I propose the NAND Resilience Index (NRI)—a five-factor framework for assessing procurement resilience:

• Supplier concentration risk: How dependent is the organization on a limited number of NAND vendors? Organizations sourcing over 50% to 60% of NAND from a single supplier face elevated allocation and pricing risk.
• Geographic dependency. What percentage of supply originates from a single country or region? More than 70% of global NAND manufacturing capacity remains concentrated in East Asia, particularly South Korea and Japan.
• Capacity flexibility ratio. How much supply buffer exists relative to forecast volatility and demand uncertainty? Flexibility ratios below 1.2x increase vulnerability to sudden supply constraints and storage demand spikes.
• Technology transition exposure. How reliant is the organization on suppliers undergoing process-node or technology transitions?
• Demand volatility coefficient. How variable is quarterly storage demand across business units and end markets?

Organizations exposed across three or more dimensions face materially higher supply continuity risk.

Mitigation strategies include reducing supplier concentration below 40% to 50%, expanding geographic diversification, securing long-term capacity agreements, increasing strategic inventory buffers, and strengthening supplier collaboration during technology transitions.

NAND resilience is becoming a strategic priority

As AI infrastructure expands, NAND supply chains are evolving from supporting technology layers into strategic infrastructure systems.

Three structural shifts are likely between 2026 and 2028:

• Enterprise SSD pricing could increase 35% to 50% from late-2024 levels as capacity remains constrained during layer-count transitions.
• At least two major NAND fabrication investments outside Asia are likely to be announced, most likely in the United States or the European Union, supported by CHIPS Act incentives.
• Hyperscaler-supplier joint capacity agreements will emerge as a new procurement model, with buyers underwriting dedicated manufacturing capacity in exchange for priority allocation.

What this means

The organizations best positioned for the next phase of AI growth will be those capable of building resilient semiconductor ecosystems that can adapt to rapid shifts in demand and supply volatility. Companies that adopt frameworks like the NAND Resilience Index (NRI), alongside diversified sourcing and capacity partnerships, will shape the future of AI procurement.

About the Author

Piu Ghosh is a technology operations and supply chain professional with experience in semiconductor infrastructure, manufacturing operations, and global program management at Apple. Her work focuses on supply chain resilience, manufacturing scalability, and the operational challenges emerging from next-generation technology infrastructure.

Executive takeaways

Warehouse automation is entering a new era, one not defined by replacing workers, but by empowering them. That vision earned Robust.AI  the 2026 NextGen Supply Chain Conference Startup Award, recognizing one of the industry’s fastest-growing innovators for redefining how artificial intelligence and robotics can improve warehouse operations through collaboration rather than replacement.

Presented during Friday morning’s general session, the Startup Award recognizes emerging companies developing technologies capable of transforming supply chain operations through innovation, scalability and measurable customer impact. More than simply recognizing a promising young company, the award highlights organizations that are already delivering real-world business results for customers.

The 2026 NextGen Supply Chain Conference will take place Oct. 21-23, 2026, at the W Nashville in downtown Nashville. Registration is ongoing.

All of the 2026 NextGen Supply Chain Conference awards, which include the End User, Solution Provider, Startup, Partnership in Execution, and Visionary, are sponsored by Zion Solutions Group.

Founded in 2019 by robotics and artificial intelligence pioneers—including CTO Rodney Brooks, co-founder of iRobot and Rethink Robotics, and CEO Anthony Jules, whose experience includes Google Robotics, X and Redwood Robotics—Robust.AI was created with a simple philosophy: warehouse robots should work for people, not instead of them.

At the center of that vision is Carter, a collaborative mobile robot designed to increase warehouse productivity while fitting seamlessly into existing operations.

Unlike traditional warehouse automation, which often requires extensive conveyor systems, fixed infrastructure and lengthy implementation projects, Carter can be deployed quickly with minimal facility modifications. The result is a flexible automation platform that helps warehouses improve productivity while maintaining the agility needed to respond to changing customer demand.

That flexibility has resonated across the logistics industry.

Kyle Detwiler, Head of Sales & Solutions for Robust.AI, will accept the Startup Award and present on Robust.AI’s journey.

Proven in real-world operations

DHL Supply Chain selected Robust.AI as one of its strategic robotics partners in 2024, deploying Carter in a Las Vegas facility where the collaborative robot delivered productivity improvements exceeding 60% within its first weeks of operation, according to the company.

The success of that deployment led DHL to expand the relationship into a five-year strategic partnership that now includes the company’s first collaborative robotics deployment within its Latin American operations.

To view the latest agenda, click here

To register for the conference, click here

Organizations interested in sponsoring the conference, click here

Saddle Creek Logistics Services has also deployed Carter inside a fulfillment center supporting a major beauty products customer. Operating as what the company describes as a “virtual conveyor,” the robot moves efficiently among more than 20 drop-off points without requiring permanent infrastructure, reducing associate travel time while allowing employees to focus on higher-value work.

To support continued growth, Robust.AI has established strategic partnerships with Foxconn to scale global manufacturing and Aptiv to integrate advanced autonomous vehicle technologies into future generations of its robotics platform.

Putting people at the center of automation

While many warehouse automation providers focus primarily on replacing manual labor, Robust.AI has taken a different approach.

Carter combines physical AI with intuitive human-robot interaction, including innovations such as gesture-based controls, to make robotics easier to deploy, easier to operate and more natural for warehouse associates to use.

That human-centered philosophy has helped distinguish Robust.AI within both the robotics industry and the broader business community. In 2026, Fast Company ranked the company No. 2 in the Robotics & Engineering category of its World’s Most Innovative Companies list, recognizing Robust.AI for advancing collaborative robotics that combine artificial intelligence with intuitive design to improve warehouse productivity while empowering the human workforce.

Together, those innovations demonstrate how collaborative robotics can help organizations address labor shortages, increase operational flexibility and accelerate automation without sacrificing the people at the center of warehouse operations.

More than an award presentation

Unlike many industry recognition programs, the NextGen Supply Chain Awards emphasize education as much as celebration.

Award recipients share the implementation strategies, customer successes and lessons learned behind their innovations, providing attendees with practical insights they can apply within their own organizations.

The Startup Award is part of a broader conference program featuring executives from organizations including Wayfair, Eli Lilly, Tractor Supply Company, Apple, Amazon, Stanford Medicine, Target, DP World, Fanatics, Evonik and many other industry leaders. Across keynote presentations, fireside conversations, executive panels and interactive Small Group Sessions, attendees will explore artificial intelligence, warehouse automation, digital transformation, workforce development and operational execution through real-world case studies.

Sponsorship opportunities continue to fill

The NextGen Supply Chain Conference continues to attract strong industry support from leading technology providers and service organizations committed to advancing supply chain innovation. Current sponsors include:

• Diamond Sponsor Zion Solutions Group
• Platinum Sponsor Gather AI
• Gold Sponsors Cycle Labs, Dematic, Geek+, and Dexory
• Bronzer Sponsor Verity
• Associate Sponsor AutoScheduler

Organizations interested in participating still have opportunities available, including a limited number of Gold Sponsorships.

Gold Sponsors receive a premium speaking opportunity featuring a 30-minute customer case study presented jointly with an end-user customer, allowing attendees to hear firsthand how organizations are solving today’s most pressing supply chain challenges through measurable business outcomes. With just 7 Gold sponsorship opportunities remaining, organizations interested in participating are encouraged to reserve their space soon.

Learn more about sponsorship opportunities here: https://www.nextgensupplychainconference.com/sponsors/

Experience NextGen in Nashville

The 2026 NextGen Supply Chain Conference will take place Oct. 21-23, 2026, at the W Nashville in downtown Nashville, bringing together senior leaders from across supply chain, logistics, manufacturing and technology for three days of education, networking and peer learning.

Early-bird registration is currently available for professionals looking to gain practical insights from organizations leading supply chain transformation.

Companies interested in showcasing their innovations to an executive audience can also explore remaining sponsorship opportunities. A limited number of Gold Sponsorships remain available, each featuring a 30-minute customer case study presented jointly with an end-user customer.

Whether you’re evaluating collaborative robotics, exploring physical AI, or looking for practical strategies to improve warehouse performance, the NextGen Supply Chain Conference offers direct access to the innovators and practitioners shaping the next generation of supply chain technology.

Executive takeaways

Eight thousand Mini Coopers crossed the U.S. border with parts that were illegal to import. Nobody at the top of the chain knew until a Senate committee told them.

Here is how it happened, because the mechanics matter more than the headline.

A small California supplier, Bourns, Inc., bought electronic components from a Chinese manufacturer, Sichuan Jingweida Technology Group (JWD), that U.S. authorities had placed on the forced-labor entity list. That California firm sold the parts to Lear Corporation, a large Tier 1 supplier. Lear built them into modules and shipped them to BMW, Jaguar Land Rover, Volkswagen, and Volvo. When the banned link surfaced in January 2024, Volkswagen disclosed it and swapped the parts. BMW kept importing. At least 8,000 Minis with the banned components entered the country, and the imports only stopped after a congressional committee asked, repeatedly, why they hadn’t (U.S. Senate Committee on Finance, 2024).

Every automaker involved said much the same thing: they didn’t know. The flagged supplier sat three tiers down, in a layer of the network none of them could see, BMW’s own Tier 3 (U.S. Senate Committee on Finance, 2024).

Sit with that for a moment, because it is the whole story in miniature. That single forced-labor link was a human-rights failure, a customs violation, and an operational disruption, all at once, all triggered by the same event, all originating in a part of the supply chain nobody was watching. It did not respect anyone’s org chart. It did not arrive labeled ESG or compliance or risk. It arrived as 8,000 cars that suddenly couldn’t legally be sold.

That is the shift every COO and chief supply chain officer needs to absorb. The three jobs you used to run separately have become one job. The regulations made sure of it.

Three teams, one event

For 20 years, most companies split this work three ways. The risk team watched for disruption such as fires, floods, a Tier 1 going bankrupt, a port shutting down. The compliance and trade team watched for sanctions, tariffs, and product rules. The sustainability or ESG team watched emissions and ethics, usually from a corporate-affairs office nowhere near the procurement floor.

Three teams. Three systems. Three reporting lines. That worked when the risks stayed in their lanes.

They don’t anymore. A single forced-labor link three tiers down now triggers a detention, a production stoppage, and a reputational hit simultaneously. A high-carbon supplier is now a cost line, a reporting obligation, and a market-access question at the same time. The exposure is one thing. The response to it is still three things, scattered across three teams who each see only their slice.

That gap between a converged risk and a fragmented response is where companies are getting caught. The teams aren’t failing. The structure is.

Why this lands on operations, not legal

It would be easy to file all of this under legal or ESG reporting and move on. Don’t. The reason this is a CSCO and COO problem is that the consequences show up as the things you own and they are expensive.

Stranded inventory. Goods stopped at the border don’t generate revenue. U.S. Customs and Border Protection has reviewed more than 18,000 shipments worth roughly $3.81 billion under the Uyghur Forced Labor Prevention Act (UFLPA) since enforcement began in June 2022 (Troutman Pepper Locke, 2026; CBP, 2026). Solar products dominate: by value, roughly 83% of everything detained under the UFLPA has fallen under the single tariff code that covers solar cells and modules (PV Tech, 2026).

Halted lines and idled people. When Hanwha Qcells, the largest U.S. solar manufacturer had Korean-made solar cells detained at U.S. ports under the UFLPA, the compounding delays forced it to furlough roughly 1,000 workers at its Dalton and Cartersville, Georgia, facilities in November 2025. Production did not return to normal until March 2026 (PV magazine USA, 2025; PV Tech, 2026). That is a supply chain event, not a sustainability footnote.

Lost market access. As of Jan. 1 2026, the EU’s Carbon Border Adjustment Mechanism (CBAM) entered its definitive phase. Importing steel, aluminum, cement, fertilizer, hydrogen, or electricity into the EU above a 50-ton annual threshold now means measuring and reporting the carbon embedded in those goods, holding authorized-declarant status, and from 2027 paying for that carbon through surrendered certificates (European Commission, 2026a). Carbon stopped being a reputation metric and became a customs cost. Without verified emissions data, EU market access is now at risk.

Margin erosion across the board. None of this is cheap to get wrong, and the bill compounds. The McKinsey Global Institute’s analysis of global value chains found that companies can expect supply chain disruptions to cost, on average, close to 45% of one year’s profits over the course of a decade (McKinsey Global Institute, 2020). The regulatory layer now sits on top of that.

These aren’t compliance abstractions. They are the line items a COO answers for every quarter.

What actually changed

You do not need to memorize the directives. You need to know what they collectively did: they turned voluntary commitments into enforceable duties that reach deep into the supplier network. Here is the landscape in plain terms.

• Forced labor is now a border issue, not a values statement. The U.S. bars goods linked to China’s Xinjiang region outright under the UFLPA (CBP, 2026). The EU’s own forced-labor ban covering any product, any country, any tier, applies from Dec. 14 2027 (Regulation (EU) 2024/3015).
• Carbon is now a cost. The EU’s CBAM began its definitive phase on Jan. 1 2026; covered importers must report verified embedded emissions, with certificate payments beginning in 2027 on 2026 imports (European Commission, 2026a).
• Deforestation-linked commodities. Cattle, cocoa, coffee, palm, rubber, soy, and wood need proof of deforestation-free origin to enter the EU from Dec. 30 2026, for large and medium operators (Regulation (EU) 2025/2650; European Council, 2025).
• Due diligence is a legal duty. Large companies must identify and address human-rights and environmental harms across their chain of activities. After the 2025 Omnibus simplification, the EU’s Corporate Sustainability Due Diligence Directive (CSDDD) applies from July 26 2029, with national transposition due by July 26, 2028 (Directive (EU) 2026/470; European Commission, 2026b).
• Scope 3 is on the books. Value-chain emissions which run, on average, around 26 times higher than a company’s own operational emissions, are now a reporting obligation where material (CDP & BCG, 2024).

One caution, because the news cycle muddied it: in 2025 the EU pulled back. Its Omnibus package narrowed scope, pushed out deadlines, and dropped some of the harshest provisions, including the CSDDD’s mandatory climate-transition-plan implementation duty and its EU-wide civil liability regime (Directive (EU) 2026/470; White & Case, 2026). Plenty of companies read that as permission to stand down. That is the wrong read. The EU simplified the paperwork; it did not repeal the enforcement. U.S. UFLPA enforcement intensified into 2026, with CBP stopping more than 50% more shipments in fiscal 2025 than in fiscal 2024 and releasing only about 6.5% of them (Troutman Pepper Locke, 2026). The carbon mechanism is live, and the forced-labor and deforestation rules are coming. The direction of travel never changed, only the speed.

The one capability that serves all three

Here is the good news for an operator: you don’t fix this with three new programs. You fix it with one.

Every problem above has the same root cause. You cannot see far enough down your own supply chain. And every regulation above demands the same thing; visibility several tiers deep, into the suppliers of your suppliers’ suppliers, where the worst exposures hide.

The numbers say almost nobody has it. McKinsey’s 2025 survey of supply chain leaders found that while 95% of companies now have visibility into at least their Tier 1 supplier risks, that visibility extends to Tier 2 or beyond for only 42% of them (McKinsey, 2025). That blind majority is exactly where the banned components, the Xinjiang-linked polysilicon, and the deforestation links live.

So, the move is straightforward to state, if not to execute: map the network once, and run risk, compliance, and ESG off the same map. The same multi-tier visibility that flags a Tier 3 supplier in financial distress also tells you whether that supplier sits on a forced-labor entity list or in a high-carbon region. One backbone. Three uses. And it is the direction the field is converging on, because the regulations themselves now require exactly the deep, n-tier view that most programs still lack (McKinsey, 2025).

The key takeaway: a triage matrix

You cannot map everything at once, so the real question is where to start. Plot your highest-spend and most production-critical categories on a simple 2×2 regulatory and ESG exposure on one axis, how far down the chain you can actually see and verify on the other and the priorities sort themselves.

(Photo: Author)

Placing a category. Rate its exposure high if it touches restricted-origin inputs (polysilicon, cotton, aluminum, lithium), CBAM-covered goods (steel, aluminum, cement, fertilizer, hydrogen, electricity), EUDR commodities (cattle, cocoa, coffee, palm, rubber, soy, wood), carries high Scope 3 intensity, or is single-source and production-critical. Rate its visibility low if you know your Tier 1 but not the origin, rely on supplier self-attestation without independent verification, or cannot produce documented proof of origin or verified emissions on demand.

Work the top-right first and treat the grid as living. A category migrates rightward as enforcement expands (a new UFLPA priority sector, a CBAM scope extension to downstream goods), so re-score at least once a year.

What to do Monday

This is a CSCO and COO agenda, not a checklist to delegate. Three decisions are yours to make.

1. Decide who owns the converged risk and give them one view. Today the picture is split across procurement, trade, sustainability, and risk, and no one sees the whole supplier. Stand up a single cross-functional owner with executive backing and one shared supplier-risk picture. The structural fix matters more than any new tool.
2. Put the visibility money where the severe risk is not everywhere. You cannot map every supplier at once, and you shouldn’t try. Use the triage matrix above: attack the top-right quadrant—high exposure, low visibility—first, targeting anything touching restricted-origin inputs (polysilicon, cotton, aluminum, lithium), the EU-regulated commodities, and the carbon-covered goods. Aim to be in that 42% minority for the categories that can actually stop your lines.
3. Build for proof, not paperwork. Enforcement is shifting from “did you file a report” to “can you prove origin and emissions.” Under the UFLPA, importers carry the burden of clear and convincing evidence, and generic supplier certifications no longer suffice (Troutman Pepper Locke, 2026). Start collecting verified supplier data like carbon intensity on covered imports, substantiated origin on high-risk inputs into sourcing decisions now, before the 2026 and 2027 deadlines make it urgent.

The companies that get blindsided in the next three years won’t be the ones that lacked a policy. They’ll be the ones who kept running risk, compliance, and ESG as three separate jobs while the rest of the world fused them into one. The 8,000 Minis weren’t a sustainability failure or a compliance failure. They were a visibility failure and visibility is an operations problem.

It’s yours now.

References

CBP (U.S. Customs and Border Protection). (2026). Uyghur Forced Labor Prevention Act Statistics (enforcement dashboard) and Forced Labor Enforcement pages. https://www.cbp.gov/trade/forced-labor/enforcement

CDP & Boston Consulting Group. (2024, June 25). Scope 3 Upstream: Big Challenges, Simple Remedies (press release: "Corporates' supply chain Scope 3 emissions are 26 times higher than their operational emissions"). https://www.cdp.net/en/press-releases/corporates-supply-chain-scope-3-emissions-are-26-times-higher-than-their-operational-emissions

European Commission. (2026a). Carbon Border Adjustment Mechanism (Taxation and Customs Union). https://taxation-customs.ec.europa.eu/carbon-border-adjustment-mechanism_en

European Commission. (2026b). Corporate sustainability due diligence. https://commission.europa.eu/topics/business-and-industry/doing-business-eu/sustainability-due-diligence-responsible-business/corporate-sustainability-due-diligence_en

European Council. (2025, December 18). Deforestation: Council signs off targeted revision to simplify and postpone the regulation (press release). https://www.consilium.europa.eu/en/press/press-releases/2025/12/18/deforestation-council-signs-off-targeted-revision-to-simplify-and-postpone-the-regulation/

McKinsey & Company. (2025). Supply chain risk pulse 2025: Tariffs reshuffle global trade priorities. https://www.mckinsey.com/capabilities/operations/our-insights/supply-chain-risk-survey

McKinsey Global Institute. (2020, August). Risk, resilience, and rebalancing in global value chains. https://www.mckinsey.com/capabilities/operations/our-insights/risk-resilience-and-rebalancing-in-global-value-chains

pv magazine USA. (2025, November 10). Qcells furloughs 1,000 Georgia workers due to U.S. Customs delays. https://pv-magazine-usa.com/2025/11/10/qcells-furloughs-1000-georgia-workers-due-to-u-s-customs-delays/

PV Tech. (2026, March 11). Qcells resumes normal US solar module production after UFLPA detainments (including CBP detained-value data by tariff code). https://www.pv-tech.org/qcells-resumes-normal-us-solar-module-production-after-uflpa-detainments/

Regulation (EU) 2024/3015 of the European Parliament and of the Council on prohibiting products made with forced labour on the Union market (EU Forced Labour Regulation).

Regulation (EU) 2025/2650 amending Regulation (EU) 2023/1115 (EU Deforestation Regulation; published in the Official Journal 23 December 2025).

Directive (EU) 2026/470 of the European Parliament and of the Council (the "Omnibus I" directive amending the CSRD and CSDDD; published in the Official Journal 26 February 2026, in force 18 March 2026).

Troutman Pepper Locke. (2026, February). High-Voltage Enforcement: UFLPA Turns Up the Heat on Lithium-Ion and Energy Storage Imports (analysis of CBP UFLPA dashboard data through early 2026). https://www.troutman.com/insights/high-voltage-enforcement-uflpa-turns-up-the-heat-on-lithium-ion-and-energy-storage-imports.html

U.S. Senate Committee on Finance. (2024, May 20). Automakers Shipped Cars and Parts Made by Chinese Company Banned for Forced Labor to the United States (majority staff investigation). https://www.finance.senate.gov/chairmans-news/automakers-shipped-cars-and-parts-made-by-chinese-company-banned-for-forced-labor-to-the-united-states-car-companies-are-failing-to-police-their-supply-chains-for-chinese-components-made-with-forced-labor-finance-committee-majority-staff-investigation-finds

White & Case LLP. (2026). Simplified, not abandoned: EU Corporate Sustainability after the Omnibus I Package. https://www.whitecase.com/insight-alert/simplified-not-abandoned-eu-corporate-sustainability-after-omnibus-i-package

In this session, we will talk about how procurement’s utilization of AI-enabled CLM is starting to rival even legal’s. The discussion will draw on real-world examples to explore how self-service access to contract information is changing the way legal, procurement, finance, and operations teams engage with the data, reducing bottlenecks and improving commercial outcomes.

Speakers will answer questions about:

• Common AI use cases and workflows that fall within the typical contract lifecycle

• What ‘data availability’ means in the context of agreements and how wider access is increasing company-wide understanding of commitments

• The value of standardizing contract terms, especially for the sake of obligation management and exposure assessment

FEATURING: 

Ceschino Brooks de Vita, Founder of The Legal Tech Guide and Navin Mahavijiyan, Head of Community, Agiloft

Executive takeaways

For years, supply chain leaders have been told that visibility is the answer. Invest in dashboards. Build control towers. Connect systems. Capture more data. Monitor every shipment, every supplier, every inventory position, every production constraint, and every maintenance event.

Those investments have helped. Most organizations today have far better visibility than they had a decade ago. They can see more problems, sooner, across more parts of the enterprise.

But visibility alone has not solved the execution challenge.

In many supply chains, the problem is no longer a lack of information. The problem is what happens after the information appears on the screen. A planner sees hundreds of exceptions. A logistics team sees delayed shipments. A buyer sees supplier commitments shifting. A plant supervisor sees a production constraint. A maintenance leader sees an asset that may fail.

Everyone can see the issue. The hard part is deciding what to do first.

That is why the next frontier in supply chain execution is not another dashboard. It is the ability to turn insight into action. This is where AI agents are beginning to change the conversation.

Figure 1. AI agents extend traditional control towers by converting operational visibility into prioritized actions.

The real bottleneck is decision capacity

During large-scale supply chain and ERP transformation programs, I have seen a common pattern. Organizations spend years improving data visibility, but the daily work of decision-making still depends heavily on people manually interpreting alerts, reconciling data across systems, and deciding which action matters most.

A planner may start the day with 500 exceptions. Not all of them are equally important. Some may be noise. Some may be duplicates. Some may impact low-volume items. Others may put high-priority customer orders at risk. The system may show all of them, but it may not clearly explain which ones deserve immediate attention.

That is decision latency.

In fast-moving supply chains, decision latency is expensive. A late purchase order, a missed material shortage, an unresolved shipment delay, or a delayed maintenance action can quickly ripple across inventory, production schedules, transportation, customer commitments, and financial performance.

Dashboards tell organizations what is happening. AI agents help organizations decide what to do next.

The challenge is not the number of alerts. It is separating the signals that require immediate action from the noise that can safely wait.

Figure 2. AI agents reduce decision latency by prioritizing the few operational issues that matter most.

From process automation to decision automation

Traditional automation has delivered significant value in supply chain operations. It works well when the process is stable, repetitive, and rule-based.

A system can automatically release purchase orders. A planning job can run on a schedule. A shipment notification can be sent when a milestone is reached. A maintenance work order can be generated when a threshold is crossed.

These are valuable capabilities. But they are still process automation.

AI agents introduce a different operating model. They are designed to observe events, evaluate context, reason across constraints, recommend actions, and in some cases coordinate execution across systems within defined business rules.

The difference is important.

Traditional Automation

"What rule should I execute?"

AI Agents

"What is happening, why does it matter, and what action should be considered?"

That shift moves supply chains from process automation toward decision automation.

This does not mean humans disappear from the process. In fact, the best use of AI agents will keep people deeply involved, especially for high-impact decisions. The goal is not to remove human judgment. The goal is to reduce the manual effort required to reach a good decision.

Where AI agents can create value

AI agents are especially useful where supply chain teams face high exception volume, fragmented systems, and time-sensitive decisions.

Planning

Planning is one of the clearest use cases.

Most planning teams already use advanced systems to generate recommendations. The challenge is that planners often receive more recommendations than they can reasonably evaluate.

Imagine a planner arriving Monday morning to find more than 500 planning exceptions. Instead of reviewing each alert individually, an AI agent identifies that most originate from only three supplier disruptions, ranks them by customer impact, and explains why they deserve immediate attention.

For example, an agent may identify that 10 separate planning exceptions all trace back to the same supplier delay. That is far more useful than showing 10 disconnected alerts.

The planner still makes the decision. But the agent reduces the time needed to understand the situation.

Inventory

Inventory teams face constant trade-offs. Too much inventory ties up working capital. Too little inventory creates service risk. The right answer changes as demand, supply, lead times, and customer priorities shift.

AI agents can monitor inventory positions across locations and recommend actions such as transfers, replenishment adjustments, or exception reviews. More importantly, they can explain the trade-off behind the recommendation.

Should inventory be moved from one distribution center to another? Should a planner expedite supply? Should excess inventory be held because demand is likely to recover? These are not simple yes-or-no decisions.

AI agents can help teams move from static reporting to continuous decision support.

Manufacturing

Manufacturing execution is full of constraints. Material availability, labor, machine capacity, tooling, quality holds, and maintenance downtime all influence what can be produced and when.

When one constraint changes, the impact is rarely isolated. A material shortage can affect a production schedule. A schedule change can affect labor requirements. A machine issue can impact customer orders.

AI agents can help monitor these relationships and recommend schedule adjustments, identify bottlenecks, and surface the downstream impact of operational decisions.

The value is not in replacing supervisors or planners. The value is in helping them respond faster when conditions change.

Rather than replacing production supervisors, AI agents act like experienced operations coordinators who continuously monitor constraints across planning, production, inventory, and maintenance.

Logistics

Transportation teams live in a world of exceptions. Shipments are delayed. Carriers miss appointments. Freight costs change. Capacity tightens. Weather, congestion, and labor issues create disruption.

A traditional dashboard may show that a shipment is late. An AI agent can go further by identifying which customer orders are impacted, whether alternate carriers are available, whether inventory exists at another location, and what action should be considered.

That is the difference between visibility and decision support.

Maintenance

Predictive maintenance has been discussed for years, but many organizations still struggle to convert predictions into coordinated action.

Knowing that an asset may fail is only part of the problem. The organization also needs to know whether a technician is available, whether spare parts are on hand, whether production can absorb downtime, and whether the work should be done now or deferred.

AI agents can connect these signals and recommend practical maintenance actions. They can help maintenance teams prioritize work based on asset criticality, production impact, part availability, and labor constraints.

Again, the decision remains with people. But the agent reduces the coordination burden.

Figure 3. AI agents orchestrate decisions across planning, manufacturing, inventory, logistics, and maintenance.

Governance cannot be an afterthought

The excitement around AI agents can sometimes create the impression that companies should move quickly toward full autonomy. That would be a mistake.

In supply chain execution, trust matters.

AI agents should be introduced with clear governance. Organizations need to define which actions can be recommended, which actions can be executed automatically, and which decisions always require human approval.

There should be audit trails. Recommendations should be explainable. Users should understand what data was considered, why the agent made a recommendation, and what level of confidence the system has in that recommendation.

This is especially important in supply chain environments where decisions can affect customers, suppliers, inventory valuation, production schedules, financial commitments, and compliance.

Human-in-the-loop control is not a limitation. It is how trust is built.

Figure 4. Human governance remains essential for trustworthy AI-assisted decision-making.

The best early AI agent deployments will not be the ones that promise full autonomy. They will be the ones that deliver reliable assistance, transparent recommendations, and measurable improvements in decision speed and quality.

The foundation still matters

AI agents will not fix poor master data. They will not overcome broken processes. They will not magically align disconnected organizations.

If anything, they make foundational issues more visible.

Organizations preparing for agent-enabled supply chains should focus on four fundamentals.

First, they need trusted data. Item data, supplier data, customer data, lead times, inventory balances, sourcing rules, and transaction accuracy still matter. AI agents depend on the quality of the information they consume.

Second, they need process clarity. If decision rights are unclear today, AI will not make them clearer. Companies should define who owns decisions, when escalation is required, and what policies govern execution.

Third, they need explainability. Users are more likely to adopt AI recommendations when they understand the reasoning behind them. A black-box recommendation may be ignored, even if it is technically correct.

Fourth, they need change management. AI agents change how people work. Planners, buyers, warehouse teams, and maintenance leaders must understand that agents are not replacing their expertise. They are helping them focus their expertise where it matters most.

Start small, then scale

The best place to begin is not with a broad enterprise-wide AI agent program. It is with a focused use case where the business pain is clear and measurable.

Good starting points include planning exception prioritization, supplier risk monitoring, inventory transfer recommendations, shipment delay management, or maintenance work prioritization.

These use cases work because they share three characteristics: high exception volume, clear decision points, and measurable business outcomes.

Once teams see that AI agents can reduce manual effort, improve consistency, and accelerate decisions, trust begins to build. From there, organizations can expand into more complex and cross-functional use cases.

The mistake would be trying to automate too much too quickly.

The smarter path is to let AI agents earn trust one decision at a time.

The next competitive advantage

For years, supply chain leaders have focused on optimizing the flow of materials. That will always matter. But in increasingly complex supply chains, the next competitive advantage may come from optimizing the flow of decisions.

How quickly can an organization detect a risk?

How accurately can it understand the impact?

How consistently can it choose the right action?

How effectively can it coordinate execution across functions?

These are the questions that will define the next generation of supply chain performance.

AI agents are not a replacement for supply chain professionals. They are a way to amplify human expertise. They can reduce noise, surface patterns, explain trade-offs, and accelerate execution.

The future of supply chain execution is unlikely to be fully autonomous. It will be built on collaborative intelligence—people and AI agents working together to make better decisions faster.

Dashboards helped supply chains see better.

Dashboards helped supply chains become more informed. AI agents may help them become more decisive.

And in a world where disruptions move quickly, the ability to act better may be the advantage that matters most.

Executive takeaways

Manufacturing is entering a pivotal period as artificial intelligence, workforce shortages and renewed government interest in domestic production reshape how companies think about technology investments, according to QAD CEO Sanjay Brahmawar.

Speaking with Supply Chain Management Review, Brahmawar described the current environment as a “watershed moment” for manufacturing—one that is restoring the industry’s strategic importance while creating opportunities for companies to modernize decades-old systems without undertaking massive digital transformation projects.

“I think manufacturing as an industry and domain area is going through a lot of change,” Brahmawar said.

Brahmawar joined QAD in March after serving as CEO of Software AG, where he led the company’s transition from on-premises software to a cloud-based software-as-a-service business model while growing annual revenue from approximately $800 million to more than $1 billion. Earlier in his career, he helped build IBM’s Watson business and has spent much of his career focused on manufacturing software and industrial technology.

At QAD, he said his priorities have been twofold: returning the nearly 40-year-old company to its manufacturing roots while accelerating its AI strategy.

Unlike many enterprise resource planning systems that were originally built around financial processes, Brahmawar noted that QAD’s ERP platform was designed around manufacturing itself.

“Manufacturing is all about managing those constraints,” he said.

That manufacturing-first approach has helped establish the company in industries including automotive, industrial manufacturing, medical technology and food and beverage, where regulatory compliance and production traceability are critical.

But Brahmawar believes AI now represents the industry’s biggest opportunity.

Rather than layering generic AI tools on top of existing software, QAD has focused on embedding AI directly into manufacturing workflows through what it calls Champion AI, an orchestration layer designed to support decision-making while maintaining governance and regulatory controls.

“AI allows humans to make decisions, and it operates within a controlled setting,” he said. “That is very important for clients operating in these regulatory environments.”

The company has introduced eight AI agents supporting functions ranging from procurement to shop-floor operations. Brahmawar said success will ultimately be measured not by the number of AI agents released, but by the business value they create.

“It is not how many agents we create, but are we providing value to our clients,” he said.

That focus reflects what Brahmawar says he is hearing directly from manufacturers.

Earlier this year, QAD brought together manufacturing executives to discuss AI adoption. According to Brahmawar, three consistent themes emerged.

First, manufacturers are willing to invest in AI, but only by redirecting existing technology budgets.

“There is no extra money to spend on AI,” he said. Instead, companies are carving out roughly 5% to 7% of existing IT budgets and demanding measurable returns quickly.

“If the AI doesn’t deliver ROI in 90 days, they are killing them,” he said.

Second, manufacturers recognize they are unlikely to compete with major technology companies for AI talent.

Rather than building internal AI teams, many are looking for trusted software partners to deliver AI capabilities already embedded within manufacturing applications.

Finally, trust has become a prerequisite for adoption.

Highly regulated manufacturers must ensure proprietary production data remains protected while maintaining traceability and governance requirements.

“We cannot afford to just let any type of solution work in our systems because traceability and governance are important,” Brahmawar said.

Those concerns are shaping how QAD develops its AI capabilities. Brahmawar said customer data remains isolated from the large language models powering AI functions, preventing proprietary manufacturing information from being used to train foundation models.

Beyond AI, Brahmawar sees broader structural changes reshaping manufacturing.

Governments around the world increasingly recognize manufacturing as a strategic capability rather than simply an economic sector.

“Manufacturing has been somewhat of a laggard in AI,” he said. “They have built up some tech debt.”

AI, however, may allow manufacturers to modernize without first replacing every legacy system.

“We don’t need to solve all the tech problems,” he said. “AI allows [companies] to bring agents and orchestration to manufacturing layers.”

That creates what he believes is an opportunity for manufacturers to “leapfrog this whole conversation of digital transformation.”

The technology also arrives as manufacturers continue struggling with labor shortages.

Brahmawar cited estimates showing roughly 500,000 manufacturing jobs currently remain unfilled in the United States, with open positions expected to exceed 2 million later this decade.

Rather than replacing workers, he argues AI offers manufacturers a practical way to improve productivity while helping experienced employees become more effective.

“The whole idea that AI is taking away jobs is not happening in manufacturing,” he said.

Manufacturers today have three choices, Brahmawar said: retrain an aging workforce, invest heavily in robotics or use AI to improve productivity across existing operations.

“The third area is the most viable today,” he noted.

Ultimately, Brahmawar believes manufacturers will judge AI not by the sophistication of the technology but by how quickly it delivers measurable operational improvements.

“We think about it from a perspective of ease of use of AI, ease of deployment, and paying enough attention to implementation and adoption rather than just selling software,” he said. “How do you help the customer to really get results out of that AI?”

Executive takeaways

Ask a demand planning leader how often their statistical models get reviewed and recalibrated across the full item portfolio. The honest answer, in most organizations, is seldom. Some high-priority SKUs get regular attention. All the rest is based on whatever configuration was set up at implementation, gradually drifting out of alignment as markets shift and portfolios change.

This is not a failure in effort. It’s not structurally possible. It would not be practical for a human team to meaningfully manage a portfolio of 150,000 item-location combinations, each of which needs model family selection, smoothing parameters, outlier treatment, and seasonality settings to be evaluated, on any practical review cycle.

Oracle Demand Management Cloud (DM), part of the Oracle Fusion Cloud Supply Chain suite, addresses this through AI-based Automated Forecast Tuning, more commonly known as hyper tuning.  

The premise is simple, machine learning algorithms handle the model evaluation and parameter calibration work that human demand planners cannot do at scale. They run all the time, evolve as demand behavior changes and permit planning teams to focus on the judgment-intensive work that machines cannot do.

What the system actually does

AFT is a continuous background process across the entire planning universe. During every plan run, for each item-location combination based on predefined criteria, the system evaluates multiple statistical model configurations simultaneously, scores each against held-out historical periods, and selects the configuration that best predicts actual demand. Where no single model clearly dominates, it applies a weighted blend of several candidates.

The model library spans single, double, and triple exponential smoothing variants, Croston’s method for intermittent demand, ARIMA-class models for autocorrelated patterns, and more recently, gradient-boosted and neural forecasting components for items with complex, non-linear demand behavior.

What distinguishes AFT from a conventional batch optimization is what happens after initial selection. The system tracks forecast accuracy continuously on a rolling basis. When a previously reliable model begins producing larger errors, that degradation is treated as a signal worth acting on. The system reduces that model’s influence and elevates alternatives showing better recent fit. This means demand pattern shifts get detected and acted upon within planning cycles rather than waiting for the next scheduled model review.

Outlier management works on the same adaptive logic. Rather than applying a blanket treatment to any observation that falls outside a statistical threshold, the system attempts to distinguish between data artifacts with no real-world meaning and genuine structural changes in the demand baseline. A mis-keyed entry or an allocation-driven shipping spike should be excluded from model training. A step change in baseline demand driven by a new customer or channel should be incorporated. Oracle DMC makes this distinction at the item level, with planners retaining the ability to override classifications where business context warrants it.

When organizations connect external causal data such as promotional calendars, pricing history, and macroeconomic indicators, the system can quantify the contribution of each factor, separate event-driven volume from baseline demand, and incorporate planned future events into forward projections. For consumer goods companies, this shifts the forecast from reactive to genuinely anticipatory.

Where organizations see results

We have deployed AFT (hyper tuning) functionality for a number of customers, and have seen MAPE improvements of 5 to 25 percentage points for organizations moving from static or default configurations to AI-driven tuning. The range reflects real differences in data quality and portfolio characteristics. Those with clean histories and integrated causal data tend to perform at the higher end, while those organizations with data hygiene issues often see little improvement until those issues are resolved.

Planning teams that have been spending the majority of their time on model maintenance are seeing the benefits of redirecting that capacity toward commercial intelligence, new product planning, and consensus forecast management, the activities where experienced planners add the most value.

Before the technology can work

There’s a common pattern that connects every organization that has discovered durable value in AFT. The preparatory work came before go-live, not after. In my experience, it’s super critical to ensure:

1. Demand history was cleaned and distorted periods were annotated.
2. Causal data was integrated with discipline.
3. Exception management processes were setup in advance to identify the cases where human judgment should take precedence over the AI before they occurred.

The technology is ready for production. The question for any organization thinking about it is whether the conditions for it to work are present in the organization. That honest answer before deployment is what separates a successful implementation from an expensive one.

About the author

Mukul Goyal is an Associate Director at Accenture with over 24 years of experience in supply chain and digital transformation. He has led supply chain planning related solution deployments across manufacturing, retail, CPG, oil and gas, aviation, and life sciences, working with clients across North America, EMEA, Asia Pacific, and Latin America. His work sits at the intersection of technology and operational reality, helping organizations move from legacy planning approaches to modern, AI driven forecasting capabilities. Mukul writes from direct field experience, having navigated the complexities of large scale supply chain transformations across some of the world's most demanding industries. Linkedin- https://www.linkedin.com/in/mukul-goyal-8b52645/

Executive takeaways

Technology matters, but execution matters even more. That philosophy defines the 2026 NextGen Supply Chain Conference, where two of the world’s leading supply chain organizations will be recognized during the annual NextGen Supply Chain End User Awards for transforming operations through artificial intelligence, automation and operational excellence.

Presented during Thursday morning’s opening general session, the awards recognize organizations that have successfully translated technology investments into measurable business outcomes. Rather than simply celebrating innovation, the program gives attendees the opportunity to learn directly from the practitioners responsible for designing, implementing and scaling these transformational initiatives.

This year’s winners demonstrate two distinct but equally powerful approaches to supply chain transformation, showing how intelligent technologies can improve both enterprise decision-making and warehouse execution.

The 2026 NextGen Supply Chain Conference will be held October 21-23 at the W Nashville in downtown Nashville. The End User Awards will kick off the event on Thursday, Oct. 22.

All of the 2026 NextGen Supply Chain Conference awards, which include the End User, Solution Provider, Startup, Partnership in Execution, and Visionary, are sponsored by Zion Solutions Group.

Intelligent Transformation Award: Mars

Mars earned the End User Intelligent Transformation Award for embedding artificial intelligence into enterprise-wide supply chain decision-making through its Digital Foundry and its AI-powered V2C (Volume to Customer) platform.

Kristen Daihes, Senior Vice President of Analytics, Digital and Data, will show how the platform brings together sales, customer care and supply chain teams into a single collaborative environment, replacing fragmented planning tools with predictive analytics, machine learning and seamless SAP integration.

The results have been significant. Decision-making that once required more than 50 man-hours can now be completed in seconds, improving customer service, sales enablement, working capital performance and cross-functional collaboration while allowing associates to focus on higher-value strategic work.

Now expanding globally, V2C illustrates how AI can move beyond isolated pilots to become an enterprise capability that fundamentally changes how organizations plan, collaborate and serve customers.

Autonomous Operations Award: CVS Health

Warehouse automation has become a competitive necessity, and CVS Health is demonstrating what that future looks like.

Winner of the End User Autonomous Operations Award, CVS Health transformed its Lumberton Distribution Center into one of the industry’s most advanced robotic fulfillment operations by integrating high-density goods-to-person automation, autonomous robotic sortation and robotic palletizing into a unified fulfillment ecosystem.

The results speak for themselves.

Daily throughput increased from approximately 150,000 units to more than 400,000 while achieving greater than 99.9% pick accuracy, reducing picking costs by 40%, improving associate safety and dramatically shortening employee training time.

By combining multiple automation technologies into a scalable enterprise operation, CVS Health has established a new benchmark for autonomous fulfillment while creating a model other organizations can learn from.

Learning from the organizations leading transformation

Unlike many industry awards programs, the NextGen Supply Chain Awards are designed to turn recognition into education. Award recipients share the challenges they faced, the decisions they made and the measurable results they achieved, providing attendees with practical insights they can apply within their own organizations.

Those sessions complement a conference agenda featuring executives from organizations including Wayfair, Eli Lilly, Tractor Supply Company, Apple, Amazon, Stanford Medicine, Target, DP World, Fanatics, Evonik and many other industry leaders. Through keynote presentations, fireside conversations, executive panels and interactive Small Group Sessions, attendees will explore artificial intelligence, warehouse automation, digital transformation, workforce development and operational execution through practical, real-world examples.

Sponsorship opportunities continue to fill

The NextGen Supply Chain Conference continues to attract strong industry support from leading technology providers and service organizations committed to advancing supply chain innovation. Current sponsors include:

• Diamond Sponsor: Zion Solutions Group
• Platinum Sponsor: Gather AI
• Gold Sponsors: Cycle Labs, Dematic and Geek+
• Bronze Sponsor: Verity
• Associate Sponsor: AutoScheduler

Organizations interested in participating still have opportunities available, including a limited number of Gold Sponsorships.

Gold Sponsors receive a premium speaking opportunity featuring a 30-minute customer case study presented jointly with an end-user customer, allowing attendees to hear firsthand how organizations are solving today’s most pressing supply chain challenges through measurable business outcomes. With just 7 Gold sponsorship opportunities remaining, organizations interested in participating are encouraged to reserve their space soon.

Learn more about sponsorship opportunities here: https://www.nextgensupplychainconference.com/sponsors/

Experience NextGen in Nashville

Whether your organization is exploring agentic AI, modernizing planning processes, strengthening execution capabilities or preparing the workforce for the next era of supply chain leadership, the NextGen Supply Chain Conference offers practical insights from the executives leading these transformations every day.

To view the latest agenda, visit: https://www.nextgensupplychainconference.com/agenda/

To register for the conference, visit: https://ngsc.regfox.com/nextgen-supply-chain-conference-2026

Organizations interested in sponsoring the conference can learn more here: https://www.nextgensupplychainconference.com/sponsors/

Executive takeaways

Supply chain AI is generating significant attention, and for good reason. Demand forecasting, supplier risk scoring, logistics optimization, inventory positioning, and procurement automation all benefit from the pattern-recognition and prediction capabilities that modern AI systems bring. Most of the early returns are real.

What is less visible is the infrastructure that AI supply chain systems actually run on. Not the models, which receive most of the investment and attention. The data: supplier identities, product and part attributes, service-level metadata, exception taxonomies, and the ownership structures that determine which system is authoritative when two sources disagree. When AI agents begin acting on that data in real time, its quality stops being an operational inconvenience and becomes a strategic risk.

The planning-execution gap that AI makes visible

Supply chain AI is often deployed in one of two zones. Planning zone systems optimize demand, inventory, and sourcing decisions. Execution zone systems handle order management, warehouse operations, transportation, and supplier communication. The gap between them, where planning assumptions meet execution reality, is where AI failures tend to be most consequential.

A planning model can optimize against supplier lead times that have not been updated in the supplier portal for six weeks. An AI-generated purchase order can rely on a pricing agreement that was superseded by a spot-market negotiation no one updated in the system of record. A demand recommendation can treat a discontinued product variant as active because the lifecycle flag was never closed in the product master. In each case, the model is doing exactly what it was designed to do. The problem is the data it was given.

Five governance checkpoints for agentic supply chains

Before expanding AI autonomy in supply chain operations, organizations should establish five governance checkpoints. These are not a compliance exercise. They are the infrastructure that determines whether an AI agent operating in a supply chain environment makes decisions the business can actually stand behind.

Supplier identity and hierarchy. AI agents working across procurement, logistics, and fulfilment need to operate from a single, authoritative supplier master. That master should include parent-subsidiary relationships, approved trading entity identities, site-level capabilities, and compliance certifications. When the agent evaluates a supplier, qualifies a new source, or escalates a risk flag, it needs to know it is looking at a complete, current, and authorized record. If the supplier master has duplicates, stale records, or unresolved merges from an acquisition, the agent will operate on that ambiguity at machine speed.

Product and part attribute ownership. AI-driven procurement and fulfilment decisions depend on accurate product and part data: dimensions, materials, specifications, compatibility, country of origin, compliance classifications, and technical substitution rules. In many organizations, this data is distributed across product lifecycle management systems, ERP, supplier portals, and engineering databases, with no clear owner for each attribute at the point of an AI decision. Before deploying agents that act on this data, the organization needs to assign an accountable owner for each attribute class, define a freshness standard, and specify what the agent should do when the data is missing or in conflict.

Service-level and constraint metadata. AI-driven scheduling, allocation, and logistics decisions require more than capacity numbers. They require constraint metadata: which lanes are currently disrupted, which suppliers have active quality holds, which SKUs are subject to allocation restrictions, which distribution channels have priority during a shortage. When that metadata is incomplete or stale, the AI agent will allocate capacity it does not have, commit lead times it cannot meet, and create downstream exceptions that require expensive human correction.

Exception taxonomy. Agentic supply chains will generate exceptions. The question is whether those exceptions are classified, routed, and resolved in a way that improves the system or simply managed manually until the next occurrence. Before deploying agents, organizations should define an exception taxonomy: what categories of failure are possible, what the escalation path is for each, who owns resolution, and how resolved exceptions feed back into the agent’s decision parameters. An exception that becomes a private workaround is lost learning. An exception that becomes a classified, routed, resolved record improves the system over time.

Recovery ownership. The final checkpoint is the clearest test of supply chain AI maturity: who owns the outcome when the agent is wrong? Not who is notified. Not who writes the incident report. Who is accountable for returning the affected supplier relationship, the affected order, or the affected inventory position to its correct state, and who is responsible for preventing the same failure from recuring? If that ownership is unclear before the agent is deployed, it will be unclear when the agent fails, which is a much more expensive time to figure it out.

A useful framing for supply chain leaders

The most practical question for a supply chain leader evaluating an AI initiative is not whether the model can make better decisions than a human planner. In many cases it can, under good conditions. The useful question is: what conditions does this model actually need to perform reliably, and can we guarantee those conditions in production? That is not a technology question. It is a data and process governance question, and it belongs at the beginning of the AI initiative, not at the postmortem after the first significant failure.

Supply chain AI will deliver its most durable value to organizations that treat data ownership, exception management, and recovery design as infrastructure investments on the same level as model selection and integration architecture. The model is the capability. The data governance is the foundation it runs on.

About the author

Hemang Upadhyay is a senior product and AI leader with 16+ years of experience across enterprise AI product strategy, digital commerce, product data governance, PIM/CMS/DAM systems, and AI-enabled customer experience. His work focuses on moving AI from pilots into governed, accountable production systems.

Executive takeaways

When Jackie Zhu took over Schneider Electric’s North American Supply Chain earlier this year, he stepped into one of the industry’s most demanding leadership roles. Schneider Electric has topped Gartner’s Supply Chain Top 25 rankings for four consecutive years, earning global recognition for operational excellence, innovation and execution.

For Zhu, the promotion wasn’t simply the culmination of more than two decades at Schneider Electric. It was the product of a career deliberately built across nearly every major function inside the business—from sales and procurement to logistics, industrialization and research and development.

“I’ve [not] been in this role not very long, around five months,” Zhu told Supply Chain Management Review. “There’s been no honeymoon. There are many challenges, but many achievements as well.”

Now serving in the role of Senior Vice President, North America Supply Chain Officer, Zhu believes his broad career experiences are exactly what is setting him up for success in his new role.

Building a career across the business

Many supply chain leaders spend their careers mastering a single discipline before moving into executive leadership. Zhu intentionally did the opposite.

Over 23 years at Schneider Electric, he has led procurement, strategic sourcing, logistics, industrialization, global supply chain strategy and research and development. Most recently, he led supply chain for the Power Products division, overseeing both R&D and global supply chain strategy for products supporting data centers, hospitals, airports and other mission-critical infrastructure.

His first professional role, however, wasn’t in supply chain at all. It was sales. Looking back, Zhu credits those early years with shaping how he approaches leadership today.

“I learned how important it is to listen to customers, how challenging it is to win business, and how important supply chain is in supporting customers and the business,” he said.

The career move that changed everything

For much of his Schneider career, Zhu considered himself a procurement professional.

He spent more than a decade in procurement, eventually leading sourcing operations across China. Then Schneider Electric approached him with an unexpected opportunity: become vice president of logistics.

He almost declined.

“To be frank, I was really hesitant,” Zhu recalled. “I’d worked in procurement for so many years.”

Senior leaders encouraged him to think differently.

“They came to me and said, ‘We have trust in your mindset, your leadership and your ability to drive transformation,’” Zhu said.

Their advice resonated.

“What you’ve learned in procurement wouldn’t be lost,” they told him. “What you learn in logistics will broaden your vision, your experience and your career.”

The experience fundamentally changed his perspective on leadership.

“I proved that when you move into a totally new domain, you learn much faster. Your agility and resilience become much higher,” Zhu said. “After that, I told myself that if someone asked me to take on something where I had no experience, I wouldn’t hesitate at all.”

Every decision starts with the customer

Ask Zhu what has remained constant throughout his career, and the answer comes quickly. Everything begins with the customer.

“I always tell myself and my team that every decision we make needs to be around the customer,” he said. “It can be an external customer or an internal customer.”

That philosophy extends throughout Schneider Electric’s operations. Factories serve other factories. Procurement serves manufacturing. Logistics serves both internal partners and end customers.

“The key is asking whether we’re creating value for the customer and improving customer satisfaction,” Zhu said. “That’s the ultimate goal and the ultimate principle that helps us make decisions.”

Leading the industry’s benchmark North American supply chain

Stepping into leadership of Schneider Electric’s North American supply chain means balancing operational excellence with continuous transformation. For Zhu, that means advancing Industry 4.0 initiatives, expanding artificial intelligence and building greater end-to-end visibility across the business while maintaining the operational discipline that earned Schneider Electric its reputation.

“We need to continue the transformation using AI, IoT and data so we have end-to-end traceability and end-to-end visibility across the supply chain,” he said. “That enables us to make decisions in real time.”

Yet Zhu believes technology alone isn’t enough.

Leadership requires what he calls “intellectual honesty.”

“We need to be much more open,” he said. “Sometimes we need to have intellectual honesty—to admit where we still have gaps and how we can learn from inside the organization, from the market, and even from startups.”

He also believes organizations often undermine themselves by trying to accomplish too much at once.

“If you have 10 or 20 priorities, it’s difficult to align the organization,” Zhu said. Instead, leaders should focus on “the top three or five priorities” that matter most to customers and the business.

Designing resilience into the supply chain

Like many supply chain executives, Zhu spends significant time thinking about resilience. Unlike many others, he believes resilience begins before a disruption ever occurs.

“We call it resilience by design,” he said.

Rather than reacting to disruptions, Schneider Electric incorporates resilience into network design decisions—from plant locations and supplier strategies to transportation flows and distribution center placement.

Artificial intelligence and end-to-end visibility then enable faster responses when disruptions occur. Zhu described one example in which damaged freight automatically triggered an AI-supported recommendation to fulfill the order from another warehouse, allowing Schneider Electric to replace the shipment within 24 hours without disrupting the customer.

Sustainability through visibility

The same philosophy recently earned Schneider Electric national recognition.

Earlier this year, the company received the 2026 U.S. Department of Energy Better Practice Award for scaling circularity across five pilot sites and is currently working to expand that effort to more than 20 North American sites. The initiative uses digital technologies to improve visibility into material flows, reduce waste and converse resources and strengthen operational resilience.

According to Zhu, the company applies the same technologies internally that it delivers to customers.

“We deploy the same approach we deliver to our customers—to electrify, automate and digitize—so they can achieve higher productivity, improve efficiency, reduce waste and modernize infrastructure,” he said.

The initiative demonstrates how greater visibility into materials and inventory can uncover opportunities to extend product lifecycles, reduce waste and improve resource utilization at scale. 

A leader still learning

Despite leading one of the world’s premier supply chain organizations, Zhu insists the work is never finished.

“It’s a great responsibility,” he said. “I consider it positive pressure because it pushes us toward continuous improvement, continuous learning and keeping an open mind to transform our supply chain.”

Looking back, Zhu believes the willingness to leave his comfort zone—not mastering any single function—prepared him for the role he holds today.

It’s a lesson he now hopes to pass along to the next generation of supply chain leaders: the best executives aren’t defined by the number of years they spend in one discipline, but by the breadth of perspectives they develop across the business.