Executive takeaways

Manufacturing is a future growth engine for enterprises—and is also the constraint that limits organizations from achieving that goal. The disconnect between corporate vision and how factories operate is where leadership ambitions get stuck. The obstacle? Engrained behaviors and ways of working that reward local optimization or CapEx constraints that defer technology investment, and resistance from leadership.

For chief supply chain officers (CSCOs), the misalignment between their transformation aspirations and their plant leaders’ short-term objectives is becoming a hindrance to transformation. Gartner research finds that when manufacturing operations report to the CSCO, organizations are 68% more likely to have stronger alignment with the broader supply chain. Yet manufacturing reports to the CSCO only 29% of the time. At the same time, only 17% of CSCOs are prioritizing expanding their scope of operations to new areas such as manufacturing and IT into new areas such as manufacturing and IT.

That gap matters because manufacturing operating model change depends on plant leaders’ buy-in for new ways of working and the technologies that support them. If they remain anchored to site-level habits, even the most ambitious network strategy will lose momentum.

Make technology useful before asking for belief

Plant leaders often resist abstract enterprise mandates that do not take into consideration the realities of how factories operate. A plant leader already fighting downtime may hear an AI proposal as another experiment that will consume scarce engineering capacity.

CSCOs should start with a visible pain point. A maintenance team, for example, could use real-time equipment data to create an early-warning signal for welding quality issues. The goal is practical: help operators see a defect risk earlier, prevent scrap and gain confidence that the technology solves a problem they recognize.

Adoption is easier when cross-functional teams include IT, operations and site employees who understand the process. Seed funding can help these teams move quickly without forcing every pilot through a full capital request. Then, once a plant owns the solution, resistance often weakens because the technology feels practical rather than imposed.

Stop accepting maturity as an excuse

Some plant leaders argue that their sites are too immature for transformation. That claim can be valid when foundational systems are missing. It can also become a convenient reason to delay change.

One manufacturing site with aging machinery and 35% overall equipment effectiveness asked for years to build maturity before joining the transformation effort, citing a lack of investment over the last decade as cause for delay. Leadership took a different route. The site received a focused investment to improve essential maintenance activities. Teams used value stream mapping to find hidden bottlenecks and remove avoidable friction. By reinforcing fundamentals, within a few months throughput and sales rose by nearly two-thirds, with no new capital equipment.

The lesson for CSCOs is practical: maturity should be proven through progress on operational basics, not claimed as a barrier. Sites that demonstrate discipline can earn more advanced investment, and those that cannot should receive targeted support to fix the fundamentals first.

Reframe the investment conversation

Traditional ROI models can incentivize plant leaders to reject anything that lacks fast savings. That creates a dangerous bias against projects that build capacity, improve resilience or free expert time for higher-value work.

Consider an engineering team buried in documentation. Using generative AI to create a complete maintenance manual may appear modest in a conventional ROI review. However, this type of project can take mere minutes and save months of engineering effort. 

Leaders should also prioritize tempering or changing the perception of automation as a job killer. This can be accomplished by showing how it can help absorb a significant increase in manufacturing, connecting the investment to business expansion.

Use governance to end site-by-site drift

Gartner research finds 62% of respondents cite silos and conflicting goals as the biggest barrier to aligning manufacturing with supply chain needs over the next several years. CSCOs cannot solve that through persuasion alone.

They need governance that connects site metrics to network objectives. A plant can still own execution, but performance should be judged against enterprise outcomes as well as local efficiency. Data from newer, smarter factories can help by showing how changes inside one facility alter service reliability or capacity elsewhere in the network.

Manufacturing transformation requires more than central slogans or local heroics. CSCOs need to make technology tangible and fund investments that protect future growth. Ultimately, transformation only sticks when plant leaders can turn enterprise ambition into day-to-day operating discipline.

About the author

Simon Jacobson is a VP Analyst in Gartner’s Supply Chain Practice. Simon’s research focuses on converging the strategies for manufacturing, digitization, automation, and workforce development.

Executive takeaways

For years, supply chain conversations around last-mile delivery have largely focused on routing optimization, carrier capacity, and delivery speed. But as retailers and logistics providers continue compressing fulfillment windows, attention is increasingly shifting to a much smaller but operationally critical challenge: what happens after the delivery vehicle actually arrives.

At Home Delivery World, HERE Technologies was on hand explaining how that “last meter” of delivery can be leveraged by organizations to improve the delivery experience.

The concept moves beyond simply getting a truck to the correct address. Instead, it focuses on helping drivers navigate the final steps of delivery more efficiently, whether that means identifying the best parking location, the fastest walking path to a building entrance, or the correct access point inside increasingly complex urban and commercial environments.

Bart Coppelmans, senior director of product management, Business Unit Head Enterprise products, at HERE Technologies, told Supply Chain Management Review the industry is beginning to recognize that delivery execution depends not only on route planning, but on the constant feedback loop between planning systems and real-world driver behavior.

“The plan is not always realistic,” Coppelmans said during the interview at the event. “There are things changing [so you] need to be much more dynamic in last-minute orders. There’s certain things you need to change and also take the feedback from the driver into account in order to kind of improve the plan.”

As companies attempt to improve delivery density, reduce failed deliveries, and maximize driver productivity, resolving that operational disconnect between the plan and actual execution is becoming more important.

Moving beyond rooftop navigation

Traditional navigation systems have historically focused on directing drivers to a geographic destination, often a street address. But that level of precision is increasingly insufficient in dense urban areas, apartment complexes, campuses, hospitals, and commercial environments where the final delivery handoff can consume significant time.

To address that challenge, HERE recently unveiled an AI-powered “Last Meter” guidance solution designed to provide more granular delivery guidance after a driver exits the vehicle.

According to Coppelmans, the system uses sensor and positioning data collected from handheld devices and driver navigation systems to better understand how deliveries are actually completed in the field.

“What we basically do is deploy a client-side on that device and then it’s automatically in the background collecting the trace,” he explained.

The system attempts to differentiate between traffic stops, parking locations, walking paths, and building entrances. Over time, repeated delivery patterns allow the platform to identify commonly used parking areas and preferred delivery approaches.

“The more deliveries that are executed, the more it improves the directions for the next one,” Coppelmans said. “And it is also benefiting the broader community.”

The company says the goal is not to rigidly dictate driver behavior but to create operational recommendations that improve delivery consistency and reduce wasted motion.

That flexibility remains important, particularly as logistics providers attempt to balance automation with driver autonomy.

“We basically give different options of how they want to configure it for their customers,” Coppelmans said. “This is really tied to their operations and how much flexibility they want to give the drivers or not.”

Why seconds matter in modern delivery networks

While saving a few seconds on a single stop may appear insignificant, those efficiencies compound rapidly across large delivery networks. Coppelmans said one of the primary KPIs being evaluated during current pilot programs is whether the technology can reduce service time at each stop enough to increase total delivery productivity.

“If it saves 30 seconds of delivery, maybe it doesn’t sound that much,” he acknowledged. “But at the end of the day, maybe they’ve saved half an hour and now they can make another five deliveries.”

As labor costs rise and delivery expectations tighten, logistics organizations are increasingly searching for operational gains in smaller increments rather than relying solely on large-scale network redesigns. The challenge, however, is that many of those inefficiencies exist in areas traditional routing software was never designed to address.

The company’s current pilot programs in the U.S. and Europe are attempting to determine how effectively AI-driven guidance can improve execution precision while maintaining enough operational flexibility for real-world delivery conditions.

AI still struggles with geospatial reasoning

The conversation also highlighted another growing challenge inside supply chain AI initiatives: most large language models still struggle to understand geospatial reasoning. While generative AI tools have rapidly improved conversational capabilities and workflow automation, Coppelmans argued that many models still produce unreliable results when dealing with complex routing, mapping, and logistics constraints.

“What we’re seeing with AI … and all kinds of LLMs a little bit, is that they don’t understand geospatial,” he said. “And they really also hallucinate in certain complex queries.”

That limitation becomes particularly problematic in logistics operations involving truck restrictions, compliance requirements, delivery sequencing, or complex route optimization.

As an example, Coppelmans described how current AI systems may struggle with relatively straightforward logistics questions involving truck-routing constraints, mandatory parking requirements, or geographic stopover calculations.

To address that issue, HERE recently introduced what it calls “location reasoning,” a geospatial grounding layer designed to provide AI systems with contextual location intelligence.

The technology is intended to help AI agents and logistics systems better interpret routing constraints, location data, and real-world operational conditions before generating decisions or recommendations.

As more logistics providers implement Agentic AI systems capable of making operational decisions, those systems will require increasingly accurate location awareness to function reliably in physical environments.

From generative AI to physical AI

For much of the past two years, the industry’s AI focus centered heavily on generative AI. In 2026, however, more conversations are shifting toward what some are calling “physical AI” — the use of AI systems inside real-world operational environments involving robotics, autonomous systems, and dynamic execution workflows.

Coppelmans said the company is already exploring how its location intelligence technologies may eventually support curbside robotics and autonomous delivery systems.

“We’re monitoring the effect of robotics on curbside robotics deliveries,” he said.

That includes evaluating how mapping precision, geospatial grounding, and real-world execution feedback could support robotic delivery operations in the future.

“How effectively can these robotics [companies] automate in the operational space,” he said, “and how do they also need mapping and location technology grounding further to make sure that they can better deploy that in their operations.”

AI’s role in logistics is rapidly evolving beyond simple automation or predictive analytics. Increasingly, the next phase appears focused on helping AI systems better understand and operate inside physical environments where precision, location awareness, and real-time adaptability matter just as much as raw computational power.

Executive takeaways

For years, last-mile delivery success has been viewed through the transportation lens. But more companies are starting to recognize that a successful delivery begins long before a package is loaded onto a truck.

Increasingly, retailers, logistics providers, and brands are recognizing that many last-mile problems originate much further upstream because of inventory allocation strategies, warehouse execution systems, order management systems, and even product development decisions. The final delivery may be the moment customers experience the problem, but the root causes often begin earlier in the supply chain.

That broader view of fulfillment execution was a central theme in a conversation with Prashant Shah, head of e-commerce for North America for A.P. Moller-Maersk, at the recent Home Delivery World event. While much of the discussion focused on Maersk’s evolving end-to-end logistics capabilities, the larger themes extended well beyond a single provider and reflected broader shifts happening across e-commerce fulfillment operations.

Shah argued the industry is increasingly using delivery, returns, and operational performance data to better understand upstream weaknesses that impact the customer experience long before the delivery driver arrives.

“I think they are using, and we are using our own data, to help them really understand where the issues upstream are happening,” he said.

That includes everything from inventory positioning and warehouse workflows to order timing and returns analysis.

Visibility alone isn’t enough anymore

Supply chain organizations today have access to enormous amounts of operational data. The challenge is translating that visibility into execution.

“Knowing a shipment is delayed is useful,” Shah suggested. “Knowing what to do next is where the value arrives.”

That operational reality becomes especially important in e-commerce fulfillment environments where customer expectations continue to compress around speed and reliability. Shah pointed to the ripple effects created when order timing, warehouse readiness, and carrier dispatch schedules become misaligned.

“If the order is placed and then the order is not ready to be picked up, it’s now late for the delivery to happen for the customer hands,” he explained. “That expectation of the customer that I’m supposed to get my order within a day, but now it’s not coming in a day, now it’s coming in two days, that expectation goes out of window.”

The issue, however, is not simply warehouse speed. It also involves inventory positioning, order velocity, carrier cutoff schedules, and regional network design.

“You can [study] the behavior of the customer, but you do not know when they’re going to place the order,” Shah said. He added that companies increasingly must think beyond warehouse operations alone.

Inventory allocation is a key factor in the success of e-commerce. How far does the driver have to drive to get the package and then make the delivery, for instance. Speed and efficiency inside the warehouse is important, but where the product is located starts the ball rolling toward success.

Reliability may now matter more than speed

Another important theme emerging across the retail and home delivery sectors is the growing emphasis on reliability over pure delivery speed. For years, e-commerce competition largely revolved around shortening delivery windows. But several conversations at Home Delivery World suggested the industry may be recalibrating customer expectations, particularly for larger or higher-value products.

Shah said the importance of speed versus reliability often depends on the type of customer and product category involved.

“What we have seen is anyone who is in the service side … they are very much focused on cost and speed,” he said. “When we start talking with the brand, the cost and the speed is not the conversation. The conversation becomes more about quality and reliability.”

As brands focus more heavily on protecting customer experience and reducing operational friction, managing customer expectations is coming into clearer focus.

“So if we tell the customer it’s coming Thursday between 12 and 6, it needs to be Thursday, between 12 and 6,” Shah said.

That consistency, he argued, matters more than simply promising ever-faster delivery windows. It also leans into proactive communication with the customer, which Shah said is “just as important as cost or our reliability.”

Returns data is becoming operational intelligence

Returns management was another recurring topic throughout Home Delivery World, particularly as retailers attempt to reduce the financial and operational costs associated with growing return volumes. Shah suggested that returns data is increasingly serving as an intelligence engine for brands trying to improve inventory allocation, product quality, and operational planning.

“The returns data is also helping the brands to create a better inventory allocation, better product development and other items around the whole flow of the product,” he said.

That feedback loop allows companies to identify recurring issues tied to damaged products, incorrect shipments, packaging failures, or delivery execution problems. Importantly, Shah emphasized that reverse logistics itself is becoming nearly as operationally important as outbound delivery.

That shift reflects the broader reality that fulfillment networks are no longer simply outbound transportation systems. They are increasingly closed-loop operational ecosystems where returns, replacements, inventory repositioning, and customer communications all feed into ongoing execution decisions.

End-to-end data is reshaping fulfillment strategy

As more logistics providers, retailers, and fulfillment operators expand into integrated service models, they are gaining broader visibility into where failures occur and how those failures impact the end customer. How that data connects is becoming more important.

“Yes, it absolutely helps out,” Shah said when asked whether broader end-to-end visibility improves operational troubleshooting. “Now we are seeing the end users are telling us where the problems are.”

That data, he said, allows organizations to identify recurring issues, isolate operational bottlenecks, and proactively redesign portions of the fulfillment network. In one example Shah shared, delivery performance problems in specific ZIP codes were traced back to carrier execution issues.

“We talked to the carrier,” he explained. “We changed the carrier and we reset the program again and now we are flying them.”

The broader takeaway is that last-mile delivery is no longer simply about transportation execution. It has become a reflection of how effectively companies synchronize inventory, data, warehouse operations, customer communication, and fulfillment strategy across the entire supply chain.

Or as Shah put it, “It is a true data intelligence port.”

Digital transformation has been a priority for supply chain leaders for years. But in 2026, the conversation is shifting—from building visibility to enabling action.

Many organizations have invested heavily in control towers, data platforms, and integration tools. Yet a persistent gap remains between insight and execution. The challenge is no longer collecting and visualizing data—it’s turning that data into faster, more confident decisions across the supply chain.

In this session, we’ll explore how leading organizations are advancing their digital supply chain strategies beyond foundational visibility. From AI-driven decision support and digital twins to cloud-based platforms and API-enabled ecosystems, companies are building more connected, responsive, and scalable operations.

We’ll also examine the critical role of data governance, integration, and cross-functional alignment in making these technologies effective. What separates companies that are seeing real value from their digital investments from those still stuck in pilot mode?

Whether you’re early in your digital journey or looking to scale existing capabilities, this discussion will provide practical insights into how to move from fragmented tools to a truly connected, execution-driven digital supply chain.

Panelists:

Bill Benton, Co-Founder, GAINS; Allen Oleksak, Director of Product Management, Infios; Dan Heinen, President and CEO, Kleinschmidt; Tatyana Ventura, Director of Customer Success, RFgen

Executive takeaways

Editor's note: The SCM thesis Elucidating Import Container Flows: A Simulation Study of Port of New York/New Jersey was authored by Kevin Power and Yassine Lahlou‑Kamal and supervised by Dr. Elenna Dugundji (elenna_d@mit.edu) and Dr. Thomas Koch (thakoch@mit.edu). For more information on this research, please contact the thesis supervisors.

Examining inefficiencies at the Port of New York and New Jersey

Chronic inefficiencies at major seaports have impacts that ripple through the entire supply chain, driving up shipping costs and consumer prices. Our capstone project looked for opportunities to improve operational efficiency by examining bottlenecks at the Port of New York and New Jersey, the largest port on the East Coast. 

Our research identified three key challenges:

1. Limited system-level visibility that prevents stakeholders from fully understanding the downstream effects of their individual operational decisions
2. A lack of robust tools to test the potential impacts of proposed infrastructure investments, policy changes, or operational strategies before implementation
3. Insufficient granular, container-level insights, particularly regarding the characteristics of the cargo itself and how they influence flow patterns and dwell times

Model and insights

To address these challenges, we created a discrete-event simulation model of import container flows through the Port of NY/NJ to enable structured experimentation that could help us understand complex interdependencies and quantify the impacts of various interventions. The model integrated real-world data from AIS vessel tracking and ImportGenius shipping manifests, and we used port infrastructure data and rail schedules for calibration to ensure that the model reflected realistic terminal dynamics. We employed a fine-tuned BERT model to classify cargo by commodity, enabling us to analyze dwell times by cargo type and explore targeted interventions for specific commodities.

Our research revealed the following key insights:

1. Yard waiting time was the largest contributor to total dwell time, averaging nearly 59 hours (65% of total dwell time). This indicates that the interventions likely to yield the most significant reductions of dwell time are those that optimize yard operations, streamline container availability, and expedite inland transport (both truck and rail).
2. The Port of NY/NJ has untapped rail capacity; raising the outbound rail share from 15% to 25% reduces truck queues by 11% and median dwell time by more than 2.5% for refrigerated containers.
3. Extending gate hours by two hours could reduce median dwell time by more than 6% for refrigerated containers.
4. Identifying specific high-volume commodity groups that are suitable candidates for shifts to rail transport and investigating the reasons why some commodity types consistently experience longer dwell times (e.g., specific inspection requirements, specialized handling needs, less frequent pickup patterns) could improve resource allocation.

Conclusions

Our simulation model and quantitative results offer valuable guidance for port authorities, policymakers, and private stakeholders, providing a shared platform to explore scenarios, anticipate consequences, and collaboratively work towards a more efficient, resilient, and sustainable port ecosystem.

Executive takeaways

Artificial intelligence is reshaping the data center industry at a pace few anticipated. But beneath the headlines about GPUs, power demand, and hyperscale expansion, another transformation is taking place—one that is redefining how digital infrastructure is delivered.

The scale of infrastructure expansion underway is unprecedented. The International Energy Agency estimates that global electricity demand from data centers is projected to more than double by 2030 to roughly 945 terawatt-hours—equivalent to Japan’s current annual electricity consumption—with AI workloads serving as the primary driver of growth.

When it comes to data center development, logistics is no longer a downstream support function: it’s becoming its own strategic layer of infrastructure.

As AI workloads accelerate, the pressure points are no longer confined to compute availability or real estate. The new constraint is execution: how quickly organizations can coordinate supply chains, move equipment, deploy infrastructure, and continuously refresh assets across globally distributed environments.

That shift is pulling logistics providers much earlier into data center planning. What was once treated as a downstream transportation and warehousing function is evolving into a highly coordinated orchestration model that spans manufacturing, integration, deployment, commissioning, and lifecycle recovery.

In the AI era, logistics is the connective tissue of global infrastructure deployment.

Speed has become the primary constraint

The traditional cadence of data center development is under pressure.

Historically, infrastructure buildouts followed relatively predictable timelines, with procurement, construction, and deployment managed through sequential workflows. But AI has compressed those timelines dramatically.

Operators now face pressure to deploy capacity faster than ever while maintaining uptime, resiliency, and sustainability commitments. That compression exposes every weak link in the supply chain.

Equipment lead times remain volatile. Power infrastructure components are constrained. Specialized labor is limited in many markets. Meanwhile, hyperscalers are expanding into regions that often lack mature logistics ecosystems or transportation infrastructure.

In this environment, even minor disruptions can create cascading delays.

The challenge is no longer simply sourcing equipment. It is synchronizing thousands of moving parts across multiple continents while maintaining deployment velocity.

Organizations are responding by prioritizing faster deployment models, earlier supply chain visibility, and closer operational alignment across stakeholders.

The era of fragmented delivery models is ending

One of the clearest shifts emerging from AI infrastructure expansion is the move away from fragmented project delivery models.

Traditional data center development often relied on sequential handoffs between designers, manufacturers, contractors, logistics providers, and operators. That model struggles under the demands of AI-scale deployment, as tightened timelines leave little room for disjointed workflows or reactive coordination.

Instead, the industry is moving toward integrated execution models where planning, manufacturing, logistics, and deployment are coordinated far earlier in the process. This is particularly visible in modular construction and prefabrication strategies, which can accelerate deployment, improve consistency and reduce commissioning risk by standardizing systems offsite.

But modularity only works when it is paired with integration. Design assumptions, manufacturing schedules, transportation constraints, site readiness, and deployment sequencing need to be aligned before equipment begins moving.

That level of coordination requires logistics teams to operate much closer to the center of infrastructure planning, rather than at the periphery.

Standardization is emerging as a competitive advantage

AI infrastructure is also pushing the industry toward more standardized global deployment models.

Hyperscalers are no longer treating every data center as a bespoke regional project. To move faster, they are replicating playbooks across markets: standardizing how equipment is sourced, staged, integrated, commissioned, and supported.

The rationale is straightforward: speed improves when organizations stop reinventing the process for every build.

For logistics providers, this changes the nature of execution.

Success increasingly depends on the ability to replicate deployment capabilities globally—whether in North America, the Middle East, Southeast Asia, or emerging markets—while maintaining operational consistency across vastly different environments.

That consistency extends beyond transportation to include staging operations, rack integration, spare parts management, commissioning support, and onsite logistics coordination.

In effect, logistics networks are becoming the connective infrastructure behind global AI deployment.

Lifecycle logistics is mission-critical

AI is not only changing how data centers are built. It is changing how they are maintained, refreshed, and retired.

Historically, data center equipment followed longer replacement cycles. But with GPU architectures rapidly evolving, replacement cycles are shortening and equipment is turning over more frequently. That puts new pressure on operators to manage what happens after deployment, including decommissioning, secure asset recovery, refurbishment, component harvesting, recycling, and reuse.

The sustainability implications are substantial. Global e-waste generation now exceeds 60 million metric tons annually, of which only about 22% is formally recycled, according to the UN’s Global E-waste Monitor. This is intensifying pressure on operators to improve circularity and lifecycle recovery practices.

As operators face increasing scrutiny, extending asset life and improving recovery rates are becoming important levers for reducing environmental impact. The focus is shifting from “buy-use-dispose” models toward circular infrastructure strategies designed to maximize long-term value.

In this context, logistics providers are taking on a broader role beyond simply moving assets into data centers. They are helping manage the continuous flow of equipment through deployment, operation, refresh, and recovery cycles. In the AI era, logistics must support the full life of the asset, not just the moment it arrives on site.

Why global coordination will define AI infrastructure deployment

Perhaps the most important shift underway is conceptual.

AI is transforming data centers from isolated facilities serving regional demand into globally coordinated systems. A single deployment may involve components sourced from multiple continents, integrated across distributed manufacturing networks, transported through constrained global freight systems, and commissioned under compressed timelines in emerging markets.

The future of infrastructure deployment will not be defined solely by who can build the most compute capacity. It will be shaped by who can orchestrate global execution most effectively.

The organizations that succeed over the next decade will likely share several characteristics:

• Integrated planning across design, manufacturing, and logistics
• Standardized deployment models that are fungible and scalable globally
• Strong supplier coordination and long-term trusted partnerships
• Lifecycle strategies that prioritize circularity and asset recovery
• Operational models designed around speed, visibility, and adaptability

AI may be powered by compute, but scaling it depends on execution. As data centers become more global, modular and asset-intensive, logistics will play a larger role in determining which projects move from plan to operation quickly—and which ones lose time to fragmentation.

The industry is entering a phase where logistics is no longer adjacent to infrastructure strategy—it is infrastructure strategy.

About the author

Ya-Han Brownlee-Chen is Vice President - Data Center Strategy for DP World. She has spent more than a decade on the frontlines of data center development, helping design, scale and deploy infrastructure across global cloud environments.

Executive takeaways

The NextGen Supply Chain Conference has announced that Nitin Kapoor, vice president of technology at Wayfair, will join the Keynote lineup for the 2026 event.

Kapoor will participate in a fireside chat with Brian Straight, editor-in-chief of Supply Chain Management Review, during the conference, which takes place Oct. 21-23, 2026, at the W Nashville hotel in Nashville, Tennessee.

The session, titled “Building the Future of Home Delivery: Wayfair’s Logistics Evolution,” will explore how Wayfair has built and refined its logistics network to support speed, reliability, scalability, and customer experience in one of retail’s most demanding fulfillment environments.

As one of the world’s largest destinations for home goods, Wayfair operates a highly complex supply chain that manages everything from small parcel shipments to large and bulky furniture deliveries. The company has invested heavily in technology, fulfillment capabilities, transportation orchestration, and delivery operations to create a differentiated customer experience while managing the challenges associated with large-item logistics.

During the fireside chat, Kapoor will discuss the innovations driving Wayfair’s supply chain strategy, lessons learned from operating a complex home delivery network, and recent enhancements the company has made to its delivery offerings to improve customer service and operational performance.

“Nitin brings a unique perspective because he sits at the intersection of technology, logistics, and customer experience,” said Straight. “Wayfair’s journey offers valuable lessons for any organization looking to use technology to improve execution, scale operations, and better serve customers.”

Retail and fulfillment innovation remain major themes for NextGen 2026. Wayfair joins a growing lineup of supply chain leaders who are helping organizations understand how technology, talent, and operational excellence are reshaping modern supply chains.

Eli Lilly, Tractor Supply also to keynote

Mar Gimeno, associate vice president-U.S. Supply Chain for Eli Lilly, and Colin Yankee, chief supply chain officer for Tractor Supply Company, will also provide Keynote addresses during the three-day event in the heart of Nashville at the W Nashville hotel.

The NextGen Supply Chain Conference is a practitioner-driven event designed for senior supply chain, logistics, operations, procurement, and technology leaders. The 2026 conference theme, Innovate. Upskill. Transform., reflects the event’s focus on helping organizations understand emerging technologies, develop workforce capabilities, and transform supply chain operations to meet future business demands.

The conference is expected to attract approximately 250 senior supply chain executives, solution providers, consultants, and academics.

The 2026 agenda is organized around four industry focus areas:

• Logistics & Fulfillment
• Retail
• Food & Beverage
• Chemicals/Pharmaceuticals

Kapoor joins a growing list of confirmed speakers and industry leaders participating in the conference, including:

• Colin Yankee, EVP Supply Chain, Tractor Supply Company (Visionary Award recipient)
• Mar Gimeno, Associate VP, U.S. Supply Chain, Eli Lilly
• Carey Boone, VP Transformation-Americas, DP World
• Andy Moses, SVP Sales and Solutions, Penske Logistics
• Bijoy Sasidharan, Director of Analytics, Capacity Planning & Forecasting, Fanatics
• Jeff Kellan, Division President, Omnichannel Retail in AMAPAC, GXO Logistics
• Kristin Daihes, SVP Analytics, Digital and Data, Mars Snacking
• Eric Watts, VP Food Supply Chain Operations, Target
• Jay Di Sieno, Senior Supply Chain Manager, Berry Direct
• Debanshu Sharma, Senior Supply Chain Manager, Amazon
• Rahul Mittal, Head of Strategy & Innovations, Dr. Reddy’s Laboratories
• Dan Pellathy, University of Tennessee
• Norman Katz, Katzscan Consulting

Additional speakers and agenda announcements will be released throughout the summer.

Sponsors include Platinum sponsor Gather AI, Gold sponsors Cycle Labs, and Geek+, and Associate sponsor AutoScheduler.

Additional sponsorship opportunities remain available for organizations seeking to engage directly with a highly targeted audience of senior supply chain decision-makers. Gold level sponsorships include the opportunity to present a case study alongside a customer.

For more information on sponsorship packages, click here.

Registration is now open. Additional information on sponsorships, speakers, awards, and the conference agenda can be found at NextGenSupplyChainConference.com.

Executive takeaways

Despite growing headlines linking artificial intelligence investments to workforce reductions, new Gartner research suggests AI itself is not yet a major driver of layoffs—and supply chain leaders risk making strategic workforce mistakes if they overreact to the narrative.

That was one of the core messages delivered by Thomas O’Connor, VP Analyst with Gartner, during a recent Gartner Supply Chain Symposium/Xpo presentation examining the relationship between AI and workforce reductions. The research analyzed more than 1.1 million jobs impacted across 255 companies during the second half of 2025.

“Layoffs were not an AI-driven story [in the second half or 2025] and indications are it hasn’t accelerated year-to-date in 2026,” Gartner stated in the presentation.

AI layoff narrative lacks data

According to Gartner’s analysis, only about 1% of job losses studied were tied directly to AI productivity gains. Less than 5% were related to hiring restraint, where companies froze hiring or avoided backfilling positions as AI tools increased productivity among existing workers. The overwhelming majority of workforce reductions stemmed from broader macroeconomic factors or strategic repositioning within large technology firms.

The findings mirror conclusions reached by Gallup in separate workforce research. In a recent survey of workers who had experienced layoffs, only a small fraction identified AI as the primary reason for losing their jobs, suggesting that public concern about AI-driven job displacement remains significantly larger than the impact currently being measured in the labor market.

“We analyzed over 1.1 million jobs after 2025,” O’Connor said in an interview with Supply Chain Management Review following the presentation. “Within this, we basically said, okay, so what are the types of workforce reductions that we’re seeing?”

O’Connor said there were basically three types of reductions. Those include “reduce,” where companies directly eliminate jobs due to AI productivity gains; “restrain,” where organizations slow hiring because existing employees using AI can absorb more work; and “reposition,” where companies shift resources from slower-growth areas into AI-focused growth initiatives.

O’Connor said the repositioning category—particularly among large technology firms such as Amazon, Microsoft, Meta, and Block—is driving much of the public perception surrounding AI layoffs.

“When you see what Meta’s doing, what Block’s done, what Microsoft’s done, Amazon’s done, all these big tech folk, they’re repositioning to take those funds and stick it into their AI growth engine,” O’Connor said. “That’s the play that’s going on.”

The broader issue, he argued, is that many organizations and employees are extrapolating a future scenario from a relatively small set of highly visible examples.

“We’re extrapolating forward what we are worried could potentially happen,” O’Connor said. “That’s what the data tells us today.”

Why workers fear AI

That concern appears to be spreading faster than actual workforce reductions. Gallup research found that nearly one in five workers believes AI or automation could eliminate their job within the next several years, highlighting a growing disconnect between employee perceptions and current labor market realities.

At the same time, AI adoption continues to accelerate. Gallup reports that AI usage in the workplace has more than doubled over the past two years, with roughly half of employees now using AI in some capacity. Yet despite that rapid growth, large-scale AI-driven layoffs have not materialized, reinforcing Gartner’s view that organizations are still in the early stages of workforce transformation.

From AI layoffs to AI strategies

The Gartner presentation emphasized that organizations should avoid assuming AI-driven workforce reductions are inevitable or immediate. Instead, Gartner recommends that companies focus on developing AI talent strategies rather than AI layoff strategies.

“Recognize you likely don’t need an AI layoff strategy,” O’Connor advised in the presentation. “You need an AI talent strategy.”

That strategy should focus on prioritizing AI investments, retaining critical employees, creating new career paths, and accelerating employee experience development.

The hidden risk: experience starvation

O’Connor said one of the biggest long-term workforce risks may not be widespread layoffs, but rather “experience starvation” caused by companies reducing or slowing hiring for entry-level office-based roles.

Gartner’s research divides workers into four workforce archetypes: “keystones,” which include frontline operational roles; “stewards,” or experienced operational employees; “protégés,” or less experienced future leaders; and “maestros,” or senior leaders and decision-makers.

The concern, O’Connor said, is that AI increasingly enables experienced employees to perform work previously handled by junior workers, potentially reducing the traditional developmental pipeline organizations rely on to build future leadership.

“The maestros, those senior people who are used to dealing with complexity, increasingly they can use AI to do some of the stuff the more junior folk could do,” O’Connor said. “And so you’re thinking about it very much around how do we retain those people because if we lose them, it’s going to be a bigger loss now than it would have been in the past.”

The trend is already beginning to appear in broader workforce data. Gallup has found that managers and senior leaders tend to adopt AI tools more rapidly than frontline employees, raising questions about how organizations will continue developing future talent if experienced workers increasingly absorb tasks that once served as training opportunities for junior staff.

At the same time, Gartner argues organizations cannot simply eliminate traditional entry-level work without redesigning future career pathways.

“If we’re getting rid of our graduate programs, then we’ve got to be asking ourselves, what are we actually doing in terms of our future state?” O’Connor said. “There has to be a new opportunity that we’ve got to identify.”

O’Connor pointed to Procter & Gamble as one example of a company already restructuring planning roles around AI-enabled decision-making. According to O’Connor, the company is redesigning some planning positions into new roles such as “supply flow analyst” and “supply flow engineer” where employees increasingly focus on decision orchestration and analytical oversight rather than manual planning tasks.

Automation, not AI, is the biggest threat

While AI itself may not yet be driving major workforce reductions, O’Connor said automation remains a much larger source of actual supply chain job displacement today.

“The biggest challenge when it comes to jobs in supply chain of where jobs may actually be lost is automation,” he said. “When you’re putting in a new, fully automated distribution center or automated factory, and you’re replacing one that was not, that is absolute job loss.”

Still, Gartner cautioned against viewing the current environment through a purely dystopian lens. The company’s presentation repeatedly stressed that media narratives surrounding AI layoffs often fail to reflect the broader labor market reality. One Gartner slide noted that “misinterpreting current events will lead to a serious strategic error.”

O’Connor believes organizations should focus less on headline-driven fear and more on practical workforce evolution.

“We don’t see a massive reduction in workforces broadly across the economy,” he said. “We’re still hiring people into our business. We’re just having to re-profile what the job description looks like.”

Executive takeaways

For Tillamook, the challenge was not simply deploying new technology. It was redesigning how a historically regional dairy cooperative could scale into a national consumer brand while maintaining product quality, controlling spoilage, and managing increasingly complex inventory and fulfillment networks.

The Oregon-based cooperative, founded more than 100 years ago by dairy farmers, has spent the past decade steadily expanding beyond its traditional West Coast footprint into national retail markets. That growth created significant new planning complexity, particularly given the nature of Tillamook’s products.

“Implementing planning technology at Tillamook has dramatically lowered our operating cost and increased our service levels simultaneously. Broad-based business improvements like this are difficult to find today, so embracing the next implementation phase is a big priority for us,” Jake Anderson, vice president of supply chain at Tillamook, said in a statement to Supply Chain Management Review.

Tillamook’s core products rely on aged cheese, sometimes as long as eight years. That long aging cycle fundamentally changes the nature of supply chain planning. Unlike many consumer products that can be replenished relatively quickly, Tillamook must make production and inventory decisions years before products ultimately reach consumers.

“Their sharp cheddar [ages] for a year and a half,” said Erik Secan, vice president of sales for supply chain software firm Logility, told Supply Chain Management Review recently. “They’ve got to know how much they’re going to sell a year and a half from now.”

Historically, companies facing that level of uncertainty respond by buffering inventory to avoid out-of-stocks. But as Tillamook expanded nationally, that strategy became increasingly expensive and difficult to scale.

“There’s a tendency to want to really buffer that,” Secan said. “But that’s expensive, especially to store it for that long, and some products will go bad as well.”

Instead, Tillamook focused heavily on improving forecast accuracy and building greater confidence in demand planning across the organization.

Unified planning

According to Logility executives familiar with the project, Tillamook used demand planning, inventory planning, and supply planning technologies to establish a more unified planning structure and improve forecast visibility across the business.

The implementation resulted in an increased forecast accuracy of 85%, up from 70% prior to implementation. That improved forecasting capability became increasingly important as Tillamook’s distribution network expanded geographically.

Originally, the company primarily focused on aggregate demand forecasting. But national growth required more granular planning capabilities that accounted for regional demand variation, retailer-specific requirements, and inventory positioning across multiple locations.

“As they went national, they had to understand where the demand was going to be, where do they want to put the products,” Secan said. “So, they not only [improved] their forecast accuracy, but it also got more granular.”

That planning transformation produced measurable operational results.

According to statistics shared during the interview, Tillamook reduced spoilage-related losses by $4.2 million while simultaneously reducing finished goods inventory by 75%.

At the same time, the company maintained companywide fill rates of 99% across roughly 200 items spanning seven product categories.

Those improvements supported broader business growth as Tillamook expanded into new geographic markets and product categories, including ice cream and other dairy products.

Growth grows

Sanjiv Gupta, global head of Aptean Ascent (Aptean is the parent company of Logility) noted that Tillamook sustained approximately 8% compound annual growth over an 11-year period while gaining market share against significantly larger national competitors.

“This does not happen while just doing general business,” Gupta said. “Technology … was key to achieve that growth.”

According to Gupta, Tillamook’s market share in cheese increased steadily over the past decade even as portions of the broader category remained stagnant or declined.

The company is now entering another phase of supply chain transformation as its East Coast presence continues growing.

“They’re now looking at things like network design,” Secan said. “Continuous network optimization ... contract manufacturers, 3PLs; they’re building new plants … to service customers from.”

That next phase reflects the operational realities of becoming a national brand. What once functioned as a relatively localized dairy supply chain increasingly requires sophisticated inventory placement, transportation optimization, and production decisions across a distributed network.

Tillamook’s history itself mirrors that evolution. When it started, the challenge was moving product closer to the end markets. More than a century later, the company is still solving essentially the same challenge—just at a much larger scale.

The company’s conservative operational culture may also have played a role in the success of its planning transformation.

“They’re a farmer-owned cooperative, so they’re conservative [by nature],” Secan said. “Very conservative growth strategy, conservative investment approach.”

But according to Gupta, Tillamook leadership made an early commitment to supply chain planning technology as a strategic growth enabler rather than simply an operational tool.

“Management made some early bets on, ‘I’m going to use this piece and this will help us grow,’” Gupta said.

For Tillamook, that bet appears to be paying off.

Gartner released its 2026 Global Supply Chain Top 25 ranking, with Schneider Electric holding onto the top spot for the fourth straight year. NVIDIA finished second, while Walmart climbed 10 places to rank third.

The annual ranking recognizes companies that Gartner considers leaders in supply chain performance and leadership. Cisco Systems and AstraZeneca rounded out the top five, while Danone, Lenovo, L’Oréal, Johnson & Johnson and Microsoft completed the top 10.

“This year, leaders are differentiating themselves by building autonomous workforces, investing in network-centric strategies and orchestrating supply chains end-to-end across increasingly complex ecosystems,” said Laura Rainier, Senior Director Analyst with the Gartner Supply Chain practice. “Leading supply chains are embracing AI not simply to automate tasks, but to fundamentally redesign how work gets done between people and machines.”

Schneider Electric's top ranking comes as the company enters the final year of its three-year Impact Supply Chain transformation initiative. Gartner said the company has focused on integrating autonomous workforce capabilities and end-to-end resource orchestration across its operations while expanding its use of generative and agentic AI to support decision-making.

“Schneider Electric continues to demonstrate how organizations can balance bold transformation ambitions with disciplined execution,” said Rainier. “Its approach to AI-enabled orchestration, circularity, and workforce transformation exemplifies how supply chain leaders are preparing for the autonomous business era.”

The Gartner ranking also recognizes long-term performance through its Masters category. Companies must finish among the five highest composite scores for at least seven of the previous 10 years to earn and maintain that honor.

Amazon, Apple, Procter & Gamble, and Unilever retained their positions in the Masters category this year.

Three trends behind the rankings

Beyond the rankings, Gartner identified three themes shared by many of the Top 25 companies: autonomous workforce strategies, network-centric supply chain design, and end-to-end supply orchestration.

According to Gartner, leading companies are increasingly redesigning jobs around collaboration between employees and AI systems, while investing in training programs that prepare workers to manage and improve intelligent systems. The company also found that top performers are building more adaptable supply chain networks to respond to geopolitical uncertainty, tariff changes, climate disruptions, and supply shocks.

Gartner said the highest-ranked supply chains are also expanding planning and decision-making beyond their own organizations by collaborating more closely with suppliers and partners. Those efforts are helping companies gain better visibility into demand, inventory, and capacity while supporting long-term sustainability and circular supply chain goals.

This article first appeared on Supply Chain 24/7. You can it here.

Executive takeaways

Despite billions invested annually in transportation management systems, warehouse management software, AI platforms, and automation technologies, many supply chain technology implementations still fail to meet expectations. Often, that is not because the technology itself falls short, but because organizations rush into implementation before defining the business strategy the technology is supposed to support.

That was one of the central themes in a recent conversation with Tony Wayda of JBF Consulting. Wayda, principal, client advisory & partnerships with JBF, sat down with Supply Chain Management Review earlier this year at the Gartner Supply Chain Symposium to talk technology implementation, and what works, and doesn’t work, in today’s supply chain.

“The technology is just a tool,” Wayda said. “Don’t think you need a tool when you don’t know what your strategy is.”

Wayda said JBF recently completed a survey examining failed supply chain technology implementations and the reasons companies fail to achieve expected return on investment. According to the findings, roughly 89% of implementations realized less than 76% of projected ROI, while nearly 89% fell short on time, budget, or expected outcomes. It isn’t that the technology isn’t providing value, it’s just not providing the value expected.

Execution lapses

The biggest issue, Wayda said, is often not the software vendor itself, but the gap between vendor selection and implementation execution.

“What we’re seeing is companies that engage us reduce the risk of selecting the wrong product,” Wayda said. “People go to the top right corner of that Gartner Magic Quadrant for all technologies, but that’s not always the best technology for you.”

Wayda described a recurring pattern across implementations: organizations identify a technology category they believe they need such as a TMS, WMS, AI platform, or automation system without first clearly defining the operational problem they are trying to solve.

“So many people go like, ‘Oh, we need a TMS,’” he said. “What do you need the TMS to do? What is the purpose of it? What is your business objective? What problems are you trying to solve?”

That disconnect, Wayda said, frequently leads to companies selecting overly complex or poorly aligned systems that eventually require costly workarounds, reconfiguration, or even replacement.

“If you select your own technology and you’re implementing it and you figure it out too late, you’re going to either rip and replace or you’re going to spend a lot of time figuring out workarounds,” Wayda said. That negates the ROI.

Organizational gaps

The issue extends beyond the selection of the software and includes ancillary considerations.  According to Wayda, many organizations fail to properly account for internal resource allocation, governance, training, and change management before implementation begins.

He noted that many companies require employees to be involved with the implementation project, but also to continue handling their day job. That adds stress and resource allocation concerns.

Wayda said companies often focus heavily on software licensing and systems integrator costs while underestimating the broader organizational lift required to support a successful adoption. Internal IT resources, program management, governance structures, and role-based training are frequently omitted or underfunded in implementation planning.

“The first thing that gets cut all the time is change [management],” Wayda said. “Then the adoption doesn’t happen and then basically you don’t get to ROI.”

Wayda also argued that many implementations become disconnected from the operational business teams once vendor selection is complete. He noted that once the project is handed over to IT, it will get implemented but not necessarily in a way that works for the business.

A reset is underway

A shift seems to be occurring across the supply chain technology market, particularly within warehouse automation and AI deployments. Increasingly, technology providers themselves are encouraging companies to engage consultants and strategy specialists earlier in the process rather than simply purchasing systems based on market positioning or industry hype.

Wayda said some software vendors are beginning to recognize the limits of purely technology-driven implementations.

“They’re good at configuring their software. They’re good at getting their software up and running, but they’re not necessarily good at the change management side,” he said.

One example is role-based training. According to Wayda, many software vendors teach users how to navigate the software but fail to teach them how to use the technology to improve operational performance within their specific roles.

“They teach you how to use the software,” he said. “They don’t teach you how to make the software make you more productive and work for your company.”

The same concerns are now surfacing around artificial intelligence adoption, where many organizations feel pressure from executive leadership to “implement AI” without a clearly defined operational use case.

Wayda said AI can provide meaningful value in areas such as decision support, data analysis, and operational insight generation, but organizations still need strong foundational data governance and clearly defined business objectives before the technology can succeed.

“If you don’t have a strong data governance, master data plan in place, you better get one,” he said. “The cleaner your data is, the better and faster decisions you’re going to make.”

Ultimately, Wayda believes supply chain technology success comes down to organizational discipline rather than software capability.

“Companies that are successful are figuring out what problems AI can solve and how can I work it into my strategy and into my daily business process,” he said. “Those are the ones that are going to be successful.”

Executive takeaways

Patent cliff pharmaceutical launches expose a persistent supply chain gap. Organizations invest heavily in demand planning, scenario modeling, and S&OP processes but still miss revenue targets by 30% to 50% in the first six months post-launch.

The culprit is rarely forecast accuracy or manufacturing capacity. It is the absence of execution infrastructure: the operating system that converts supply visibility into prioritized allocation decisions, real-time tradeoffs, and accountable outcomes. For supply chain leaders managing high-stakes launches under constrained capacity, execution infrastructure determines whether market opportunity becomes realized revenue or slips to faster competitors. This article presents a practical framework supply chain leaders can implement before the next launch wave arrives.

The launch moment when plans meet reality

Every major pharmaceutical product launch begins with a solid supply plan. Demand forecasts are validated. Manufacturing schedules are locked. Safety stock levels are agreed upon. Distribution partners are briefed. The readiness checklist is complete.

Then the product launches, and within three weeks, the plan starts to unravel.

A competitor misses supply, and demand spikes 40% above forecast in one region. A strategic hospital system accelerates orders while another delays due to internal pharmacy committee approvals. A key distributor places a large order for opportunistic accounts while contracted Tier 1 customers wait for allocation. The supply team has full visibility. Dashboards show inventory by SKU and location. Forecast variance is still within the acceptable range.

Yet revenue starts falling behind projection.

The gap is not information. It is execution. Inventory exists but reaches the wrong customers at the wrong time. Allocation decisions stall in cross-functional reviews. Commercial priorities shift faster than supply plans update. Finance escalates margin concerns after product has already shipped. By week eight, high-value contracted accounts have an inconsistent supply and begin switching to competitors. The launch window closes. The revenue gap becomes structural.

I have observed this pattern across multiple pharmaceutical and healthcare product launches. The root cause is consistent. Organizations treat supply chain readiness as a planning problem when it is fundamentally an execution problem. They build forecasting capability without building the decision architecture, integration cadence, and accountability mechanisms required to act at launch velocity.

Why analytics without execution infrastructure fail

Most pharmaceutical supply chains are analytically mature. Teams can model demand scenarios, simulate capacity constraints, and project service level impacts with precision. During high-stakes launches, that capability is necessary but insufficient.

Explore more on this topic at the NextGen Supply Chain Conference

 

When Plans Fail: Building the Execution Infrastracture for Supply Chain Success

Most organizations invest heavily in forecasting, planning, visibility, and analytics, yet many still struggle when market conditions change and execution decisions must be made in real time. The challenge is rarely a lack of data. It is a lack of decision-making frameworks, accountability, and cross-functional alignment that turn insights into action.

In this special NextGen Supply Chain Conference session, Rahul Mittal, head of strategy & innovations for Dr. Reddy's Laboratories, will explore the concept of execution infrastructure—the governance, decision rights, operating rhythms, and integration mechanisms that enable organizations to respond quickly under pressure. Attendees will learn practical strategies for improving decision velocity, aligning supply with business priorities, and building supply chains that execute as effectively as they plan.

Register to attend today

 

Analytics answer what is happening. Execution infrastructure answers what we do next and who owns it.

Without execution infrastructure, insights do not convert to action. Allocation reports highlight imbalances but do not trigger reallocation. Supply risk dashboards flag constraints but do not resolve prioritization conflicts. Escalations reach leadership but stall because decision rights are unclear.

In launch environments, decision velocity matters more than analytical precision. A directionally correct allocation decision executed in 24 hours captures more value than a perfectly optimized decision that takes two weeks. Supply chains that win launches are not necessarily the most analytically sophisticated. They are the most decisive and the most tightly integrated with commercial and finance operations.

The 4 components of supply chain execution infrastructure

Execution infrastructure for supply chain teams consists of four interconnected elements that translate supply visibility into business outcomes.

1. Decision architecture: Clear ownership of allocation under constraint

When demand exceeds supply during a launch, allocation becomes the most critical supply chain decision. Many organizations avoid explicit prioritization, defaulting to proportional allocation across all demand to maintain perceived fairness.

Proportional allocation is expensive. It spreads constrained inventory across high-value contracted customers and low-margin opportunistic orders equally, satisfying no one fully and eroding both revenue and customer loyalty.

Decision architecture establishes three things before the launch:

1. Single decision owner. One leader, typically the head of supply planning or VP of supply chain operations, owns allocation decisions across customers, channels, and geographies. Not a committee. Not a consensus process. One accountable executive with clear authority to make tradeoffs.
2. Transparent prioritization framework. Allocation follows explicit criteria tied to business value. A typical framework might prioritize Tier 1 contracted Group Purchasing Organization accounts over non-contracted wholesale demand, customers with demonstrated compliance history over sporadic buyers, and strategic health system partnerships over transactional volume even when short-term margins are lower.
3. Defined decision inputs. The decision owner receives real-time data on contract tier status, customer margin contribution, compliance rates, competitive supply position, and strategic relationship value. Analytics provide the scoring. The owner makes the call.

The value of decision architecture is velocity and clarity. When a supply constraint emerges three weeks post-launch, the organization does not convene cross-functional debates. The decision owner reviews current data, applies the prioritization framework, communicates the allocation, and execution proceeds within 24 hours.

2. Real-time supply demand integration loops

Launch plans assume linearity. Launch reality delivers volatility. Competitor actions shift demand. Manufacturing yields vary. Customer ordering patterns concentrate unexpectedly. Without real-time integration between supply signals and commercial priorities, supply chains react too slowly to capture opportunity or mitigate risk.

Integration loops embed joint decision-making into daily operating cadence during launch windows. This typically takes the form of a 30-minute daily standup meeting with fixed participation from supply planning, commercial operations, and finance.

Supply planning updates inventory positions by product, location, and customer segment. Commercial operations provide order pipeline visibility and customer demand signals. Finance presents margin implications by channel and customer tier. Together, the group answers three questions. Where is demand shifting relative to plan? Where is supply becoming constrained? What reallocation decisions do we execute today?

No slides. No formal presentations. Data, diagnosis, decision.

This loop transforms supply planning from a weekly batch process into a continuous integration engine. Inventory moves with commercial opportunities, not static allocation plans locked weeks prior.

3. Structured operating rhythm and cross-functional governance

Patent cliff launches require coordination across supply planning, manufacturing, procurement, quality, logistics, commercial operations, finance, and regulatory. Without structured rhythm, coordination degrades into fragmented email threads, overlapping calls, and delayed decisions.

A Launch Supply Readiness Council provides the governing rhythm. The council meets weekly in the 12 weeks preceding and 12 weeks following each major launch. Membership includes supply chain leadership, manufacturing operations, commercial operations, finance, and analytics.

The agenda is consistent across every meeting. Review supply service levels by customer tier and product. Assess allocation adherence to the prioritization framework. Diagnose variances from the plan. Identify risks emerging in the next two weeks. Assign corrective actions with named owners and committed due dates. Track the closure status of prior actions.

The meeting runs 60 minutes. Decisions are documented in real time. Actions are tracked in a shared system visible to all stakeholders. Overdue actions escalate automatically to executive leadership.

Additionally, a Supply Allocation Forum meets twice weekly during active launches to make real-time tradeoff decisions. Should we fulfill a large order from a low-margin distributor today or reserve that inventory for a contracted high-margin health system ordering next week? The forum decides based on the pre-agreed allocation framework.

Structured rhythm eliminates the chaos that typically surrounds launches. Teams know when decisions will be made, who will make them, and how actions will be tracked.

4. Transparent accountability and execution tracking

Accountability in execution infrastructure means every supply decision and corrective action has a named owner, a committed due date, and visible status. Performance transparency means execution is measured and reviewed as rigorously as forecast accuracy.

For patent cliff launches, this includes several mechanisms.

Supply execution scorecards track on-time, in-full delivery performance by product and customer tier, backorder levels and aging, adherence to allocation prioritization framework, and supply plan accuracy versus actual. Scorecards are reviewed weekly in the Launch Supply Readiness Council and distributed to executive leadership.

Action closure dashboards display all open actions from governance forums, assigned owners, due dates, and current status. Closure rates become a team performance metric. High-performing supply chain teams close 85% to 90% of committed actions on time. Teams without execution discipline close 50% or less.

Allocation decision logs document every allocation decision made during constrained supply periods, including rationale, data inputs, decision owner, and subsequent outcome. This creates organizational learning. When an allocation proves effective, the logic is captured and applied to future decisions. When an allocation underperforms, the team diagnoses the gap and refines the framework.

Accountability is not punitive. It is clarity. When every team member knows who owns what and how success is measured, execution accelerates and finger pointing disappears.

Where supply chains lose value during launches

Across failed patent cliff launches, three patterns emerge consistently.

1. Allocation without strategic prioritization. Supply teams treat all demand as equivalent to avoid difficult conversations. High-value contracted accounts compete equally with low-margin opportunistic orders. The result is diffused inventory, unmet contracted commitments, and damaged customer relationships.
2. Slow integration between supply and commercial operations. Supply planning operates on weekly or biweekly cycles while commercial priorities shift daily based on competitor moves and customer pipeline changes. By the time supply reallocates inventory, the commercial opportunity has closed.
3. Ambiguous decision ownership. Allocation decisions create winners and losers, which organizations avoid by seeking consensus or deferring to committees. Decision velocity collapses. Value erodes to competitors who decide faster.

Supply chains with strong execution infrastructure avoid these failure modes. They allocate strategically, integrate in real time, and decide quickly. They treat supply chain execution as competitive advantage, not support function.

Measuring what matters in launch execution

Execution infrastructure should be measured by business outcomes, not process compliance. Three metrics reveal execution strength.

1. Allocation effectiveness. What percentage of constrained supply reaches high-value contracted customers versus low-margin opportunistic demand in the first 90 days post-launch. Strong performers direct 75% to 85% of launch inventory to priority accounts. Organizations without prioritization frameworks allocate 40% to 50%.
2. Decision velocity. How quickly does a supply constraint trigger a reallocation decision and execution? Best-in-class organizations complete the cycle within 24 to 48 hours. Organizations without decision architecture require 7 to 14 days.
3. Service level performance by customer tier. Are Tier 1 contracted customers experiencing materially better on-time, in-full performance than non-contracted accounts? This metric reveals whether allocation prioritization exists on paper only or drives actual execution.

What supply chain leaders should do now

If you are leading supply chain operations in pharmaceuticals, medical devices, or healthcare manufacturing and preparing for major product launches, four actions build execution infrastructure before the launch arrives.

1. First, assign a single allocation decision owner now. Define who makes the call when demand exceeds supply. Clarify decision authority, required data inputs, and prioritization criteria. Eliminate ambiguity before constraints appear.
2. Second, establish daily supply demand integration during launch windows. Create a 30-minute daily standup where supply planning, commercial operations, and finance review demand signals, supply positions, and make reallocation decisions in real time. No presentations. Just data, diagnosis, and decisions.
3. Third, build a structured launch operating rhythm. Launch a weekly Supply Readiness Council that meets during critical launch periods with a consistent agenda, clear decision rights, and disciplined action tracking. Treat the forum as a decision engine, not a status update meeting.
4. Fourth, measure allocation effectiveness and decision velocity, not just forecast accuracy. Track where constrained inventory actually flows and how quickly allocation decisions execute. Reward teams for strategic, timely tradeoffs, not just utilization or plan adherence.

Patent cliff launches are execution stress tests for supply chains. Organizations with strong execution infrastructure convert constrained supply into revenue capture and strengthened customer relationships. Organizations relying on good intentions and heroic effort watch value shift to competitors who execute faster and make clearer decisions.

Supply chain analytics tell you what is happening. Execution infrastructure determines whether you capture the value before the window closes.

About the author

Rahul Mittal is head, strategy & innovations for Dr. Reddy's Laboratories, Inc., a global pharmaceutical company producing over 190 medications for global clients.

Executive takeaways

As artificial intelligence dominates conversations across the supply chain sector, DHL Supply Chain is focusing less on AI hype and more on the foundational elements it believes will determine whether digital transformation efforts actually succeed: clean data, scalable automation, and operationally grounded use cases.

For Brian Gaunt, who leads digitalization efforts within DHL Supply Chain, the current wave of AI enthusiasm represents more of an acceleration of existing digitization efforts than a completely new direction.

“Digitalization has been kind of our bread and butter for a long time,” Gaunt said during a recent interview at the Gartner Supply Chain Symposium/Xpo. “AI is picking up the pace and making all the headlines and I think there’s a real good opportunity there. But for us, understanding the supply chain business is really understanding the data side of things.”

That focus on foundational data management is central to DHL’s broader digitization strategy. According to Gaunt, the company’s robotics deployments, analytics systems, and AI-driven operational tools all depend on having structured, normalized, and operationally useful data.

“Our people and our robotic solutions and our business generate a ton of data,” he said. “And how we’re able to use that to optimize our operations is really the core.”

DHL Supply Chain currently operates more than 8,000 robots across its supply chain operations globally, according to Gaunt. Those deployments range from autonomous pallet movement systems to mobile robotics used in piece-picking and warehouse fulfillment operations.

The company has also partnered with robotics providers including Locus Robotics and Robust.AI while integrating warehouse and transportation management systems such as Blue Yonder, Manhattan, and Oracle OTM into broader visibility and operational optimization initiatives.

But Gaunt emphasized that deploying technology at scale requires more than simply collecting large amounts of operational data.

“It starts to catalog your data,” he said. “What’s delivering value?”

Data analysis is key

According to Gaunt, one of the biggest challenges organizations face is determining which data should be retained at detailed transaction levels versus summarized into broader operational insights. While some information must be retained for compliance or customer-specific requirements, operational digitization efforts increasingly depend on data cleansing, normalization, and strategic structuring.

“We are constantly cleansing the data and rolling it up to the right levels to make sense for how we want to optimize and use it,” he said.

That becomes particularly important as companies attempt to scale automation and AI initiatives across large, multi-site operations.

“As an enterprise company, we don’t want to spend a lot of time on a solution for one site,” Gaunt said. “We want this to scale. Grab it, scale, and go from a scalability perspective.”

The company’s approach to robotics deployment also reflects a broader industry shift toward flexibility and scalability rather than highly rigid automation systems designed around fixed forecasts and stable operational assumptions. Gaunt said DHL evaluates automation opportunities based on customer product types, fulfillment requirements, service expectations, and long-term operational flexibility. Mobile robotics solutions have become particularly attractive because they can scale more easily than heavily fixed automation infrastructure.

“Mobile robotics really helps with that because we can scale those things up and down versus some of the really fixed infrastructure automations that are millions of dollars,” he said.

At the same time, DHL continues deploying more advanced automation systems where long-term customer forecasts and operational profiles justify the investment.

“When you’re going to make these big capital investments mutually with the customer, you need to understand that profile and the volumes,” Gaunt said.

The challenges of forecasting

Forecasting itself remains a major challenge across the logistics industry, particularly as social media trends, rapidly shifting consumer behavior, and disruption-driven volatility make demand planning increasingly difficult.

Gaunt acknowledged that DHL’s customers vary significantly in forecasting sophistication and data maturity.

“Some of our customers are very sophisticated in their demand planning and forecasting, and it’s right on,” he said. “Other ones, they’re playing to the market like everyone else.”

To manage that uncertainty, DHL designs operations with built-in flexibility thresholds that allow facilities to absorb varying levels of demand fluctuation before requiring major redesign or operational changes.

“We can quickly flex up to this, and then beyond this is going to be something different,” Gaunt said.

Controlled AI deployments

While AI remains a major focus across the industry, Gaunt warned companies against adopting AI simply to satisfy executive pressure or market hype.

“I do think you got to be careful not to fall in the trap of applying it just to apply it,” he said.

Instead, he encouraged companies—particularly smaller or less mature organizations—to experiment cautiously within controlled operational environments while focusing heavily on data governance and security.

“I think there needs to be a bit of exploration in a safe space,” Gaunt said.

Inside DHL, AI deployments currently focus heavily on operational efficiency, exception management, analytics, and labor optimization.

“It’s not something a person can’t do,” Gaunt said. “But a person can’t monitor 200 pieces of data sets and look for variance at the same time.”

The company still maintains a “human in the loop” operating philosophy where employees oversee exceptions, validate recommendations, and maintain operational accountability while automation handles repetitive monitoring and analysis tasks.

Looking ahead, DHL is increasingly exploring agentic AI models, including agent-to-agent communication structures capable of coordinating operational workflows across systems.

“We’re building agent models,” Gaunt said. “We will continue to expand our use of analytics tool sets, building agentic agents to drive decision making and make those smarter and smarter and have agent-to-agent kinds of structures where one agent’s calling another agent to get work done and to manage exceptions.”

For DHL, those investments are tied directly to long-term growth ambitions.

“We’re focused on doubling our business by 2030,” Gaunt said. “We want to do that with some efficiency.”

Executive takeaways

As supply chains face mounting pressure from disruption, geopolitical volatility, inflation, and changing customer expectations, companies are increasingly turning to artificial intelligence to improve decision-making and operational performance. But according to Blue Yonder’s Shri Hariharan, senior vice president-global solutions, the real opportunity is not simply applying AI to existing processes—it is fundamentally redesigning how supply chain work gets done.

“The problem isn’t technology,” said Hariharan, who has spent more than two decades at Blue Yonder in customer-facing and advisory roles. “The opportunity is how do you convert that technology and harness it by redefining work?”

Shifting demands

Hariharan said the role of supply chains inside organizations has shifted dramatically over the past several years, beginning with the COVID-19 pandemic and continuing through ongoing geopolitical and economic disruptions.

“The good news is that supply chains got a boardroom presence permanently,” he said.

But that visibility has also created new pressure on supply chain leaders to connect operational decisions to broader business outcomes, particularly as CFOs and boards increasingly scrutinize investments in AI and digital transformation.

Hariharan told Supply Chain Management Review in a meeting at the recent Gartner/Xpo Supply Chain Symposium that the issue is convincing the rest of the organization that a supply chain problem is impactful to the rest of the team, and then finding technological solutions to these problems.

Operational, financial disconnect

According to Hariharan, one of the biggest historical problems with supply chain technology deployments has been the disconnect between operational improvements and financial language understood by executive leadership.

Supply chain may know what it wants, but the the ROI doesn’t meet requirements expected by leadership. Hariharan argued that AI-driven supply chain transformation increasingly requires organizations to evaluate decisions not only through operational metrics, but also through their impact on revenue, margin, inventory, cash flow, and cost-to-serve.

“What does that total composite view look like to deliver business value?” he said. That includes helping companies understand the ripple effects of operational decisions across the enterprise.

As an example, Hariharan described how CFO-driven inventory reduction initiatives can unintentionally create downstream cost increases if organizations fail to evaluate the broader network implications.

“If I can improve customer fulfillment, can I do it by improving predictions so I make my forecast better and I can sense my demand better?” he said. “Can I reduce my expedited transfers? Can I reduce unplanned transfers?”

Scenario planning

To support those decisions, Blue Yonder is increasingly focusing on scenario-based planning and multi-variable optimization models that can evaluate hundreds of potential supply chain scenarios simultaneously.

Historically, Hariharan said, supply chain systems were not architected to evaluate complex trade-offs across multiple objectives at enterprise scale.

Cloud-native architecture and AI-enabled scenario modeling help companies analyze combinations of pricing, manufacturing, inventory, transportation, and distribution decisions while balancing operational and financial objectives.

“No human’s going to be able to run 300 scenarios in two days,” Hariharan said. “But what if technology could come to bear?”

But Hariharan said technology alone is not enough. One of the biggest challenges remains translating operational supply chain decisions into business language understood by executive leadership teams.

So how do supply chain organizations take what they are doing and convey that to the people who “don’t speak supply chain?”

Speaking CFO

Hariharan said Blue Yonder has increasingly focused on creating what he described as a “translation layer” that converts operational supply chain levers into enterprise business metrics.

“We’re converting very operational levers to what the business wants, which is what? Revenue, margin, cost to serve, cash to serve,” he said.

That focus on business outcomes is also reshaping how customers approach AI adoption itself. According to Hariharan, the market has shifted significantly over the past year from companies simply demanding AI capabilities to organizations asking where AI actually creates operational value.

“We’re kind of slowing down to go fast because everything looks like a nail right now,” he said.

Hariharan said many companies are beginning to recognize that accelerating broken or inefficient processes with AI does not necessarily improve business performance. “This can’t just be automation,” he said. “This has to be a recalibration of work because you can’t just speed up bad processes.”

One area receiving growing attention is what Hariharan described as autonomous sales and operations execution, or S&OE, where AI agents continuously evaluate operational conditions, monitor changes in demand and supply, and automatically generate updated trade-off analyses for planners and operators.

“What if you understood all the context factors of my business and you’re sensing for them and giving me automatic adjustment of my demand profile in the short term against real orders and inventory in the network?” he said.

Blue Yonder itself has also adjusted its internal strategy in response to those evolving customer demands. Hariharan said the company recently created a dedicated Supply Chain Advisory organization focused less on selling software and more on helping companies identify operational transformation opportunities.

“We saw the way the market was going, which is going from buying SaaS solutions to consuming SaaS solutions to driving business outcomes,” he said.

That includes embedding both product and domain experts directly with customers to evaluate how work is currently performed and where AI-enabled redesign opportunities exist.

“We don’t want to be a solution looking for a problem,” Hariharan said. “Everything looks like a nail and we got the hammer.”

Executive takeaways

In an insightful piece of reporting by the BBC’s Will Grant in an article on April 30, 2025, Americans may have been surprised to discover that the voices of call center personnel they hear for calls related to anything from election polling to customer satisfaction surveys are coming from Mexican call centers staffed by ex-gang members deported from the United States.

The call centers, one of which was founded by a deportee, can employ over 500 agents, most of whom are deportees. A background check (screening) is not performed. What’s needed, says one call center’s chief happiness officer, is fluent English and Spanish language skills and a dedicated work ethic. The call recipient in the U.S. has no idea that they are receiving a call from Mexico from a likely ex-gang member deported from the U.S.    

The call center agents work through their lists of U.S. telephone numbers. The calls they make can be related to sales promotions, debt collection, or refinancing. It seems almost certain that these call centers are performing an outsourced service for U.S. corporations. In all of the tariff turmoil of last year, I noticed that outsourced services was something that seemed to escape tariff targeting.

The call centers also serve a humanitarian role, helping deportees with the culture shock of being in a new country, find redemption for the errors of their past lives, build familiar relationships with others in similar situations, and provide a steady paycheck.     

Rather than returning to Mexico to continue a life of crime, these deportees are finding a new way of making a living through involvement and contribution. For some, their unfortunate mistakes early in life cost them dearly but they are discovering that they can be on a better path later in life.  Earning an honest paycheck—and sometimes a bonus—in a supportive environment is giving these individuals a chance for rehabilitation. They will realistically never return to the U.S., but they can make a better life for themselves and for their community where they are in Mexico now that they are better people.    

This news story just goes to prove that outsourced services are varied and everywhere. So, the next time you get a sales call you don’t want, it’s okay to reject it, but think twice about the person on the other end of the telephone line: that person may be someone who is on the long road to turning their life around.     

Executive takeaways

Most cloud ERP migration business cases get the headline numbers right. Infrastructure savings, license consolidation, reduced IT overhead. The numbers look clean, the project gets approved, and the migration begins.

What rarely makes it into the business case is procurement pricing. Not because nobody thought about it, but because the gap doesn’t surface until after go-live when changing course isn’t really on the table anymore.

The gap nobody warned you about

Oracle EBS had a dedicated advanced pricing engine built directly into purchasing. Not an add-on—core functionality. Supplier-specific price lists, category-based pricing, qualifier-based rules tied to contract purchase agreements, formula-driven pricing for complex scenarios, modifiers for discounts and surcharges on top of base prices. When a purchase order referenced the right contract and supplier, the system pulled the correct price. Buyers didn’t have to do anything.

Oracle Fusion Cloud doesn’t have that. Not as standard configuration and not as a settings toggle. The advanced pricing engine that lived inside Oracle Purchasing in EBS simply isn’t integrated with the procurement module in Fusion Cloud. Organizations that built their procurement operations around contract-linked price lists, qualifier-based pricing rules, or formula-driven pricing find that none of those structures survive the migration natively.

This isn’t speculation. Oracle’s own customer community has documented it for years—multiple open enhancement requests on Cloud Customer Connect, companies in manufacturing, distribution, and healthcare describing the same problem. The requests are still open.

I’m not making a case against Oracle specifically. Cloud ERP platforms are built for standardization and scalability, and they do that well. The tradeoff is that organizations often lose the sophisticated pricing capabilities that mature on-premise systems handled behind the scenes. Most organizations don’t find this out until after go-live, which is the worst possible time to redesign a procurement architecture.

What actually breaks

In EBS, a buyer creating a purchase order for a raw material supplier would reference the contract purchase agreement. The system pulled the correct price—simple unit price, formula-based calculation, qualified rate specific to that supplier and agreement. The PO went out with the right number. Approvals ran automatically.

In Fusion Cloud, that mechanism doesn’t exist in standard procurement. The price doesn’t default. The buyer has to enter it manually, or the PO goes out blank and gets flagged. Either way, someone has to intervene.

That intervention goes into an approval queue. In a complex enterprise, manual review doesn’t happen in an afternoon. In organizations I’ve worked with, approval cycles for purchase orders requiring manual price validation ranged from one day to 14—depending on approver availability, urgency, and what else was ahead of it.

Fourteen days. For a purchase order that should have been auto-priced and auto-approved.

Multiply that across raw material categories, across dozens of suppliers, across a manufacturing operation that can’t hold production while waiting for materials to be ordered, approved, manufactured, and shipped. The warehouse runs out of inventory on its own schedule. It doesn’t adjust for your ERP migration timeline.

The project was sold on efficiency. The reality is procurement teams running manual workarounds that cost more in operational overhead than the migration saved in licenses.

Why it keeps getting missed

Pre-migration assessments focus on what the new system can do. Vendor demonstrations show the platform at its best. Implementation partners scope around standard functionality. The people defining migration scope—usually IT and project management—often don’t have enough visibility into how procurement actually prices things to know what to ask.

The people who do know—category managers, the procurement leads who actually negotiate supplier contracts—usually aren’t in the room when scope is being defined. By the time the gap surfaces, the project is committed and the implementation partner is already billing.

This is a process failure. The information exists. It just doesn’t get asked for at the right time.

The question to ask before you sign

One question will tell you more about your pricing risk than any vendor demonstration:

What advanced pricing functionality from our current ERP environment will not be available after migration to cloud—and what is your recommended workaround for each gap—before we sign the contract?

Press for specifics. If the answer is “it’s on our roadmap,” ask when. “We recommend a third-party solution”—ask which one, what it costs, how it integrates, and who supports it when something breaks at 2 a.m. “Most customers don’t need that functionality”—ask to speak with a customer in your industry who migrated with complex supplier pricing and came out intact.

These aren’t difficult questions. They just get asked too late, or not at all.

How organizations close the gap

When standard cloud procurement pricing falls short, there are three realistic paths.

Accepting the limitation and redesigning procurement operations around what the cloud system supports natively. This works if your pricing is simple—a unit price per item, no formula logic, no supplier-specific rate structures. It stops working the moment contracts involve qualifier-based pricing rules, tiered rates, or formula-driven calculations that vary by agreement.

Third-party integration—connecting the cloud ERP to a specialized pricing engine or contract management platform. This can close the gap, but it introduces integration complexity, additional licensing costs, and a dependency on vendor support that almost always gets underestimated. The integration also needs to survive quarterly Oracle cloud updates, which creates a recurring maintenance burden most teams don't plan for.

Custom extension architecture—building the missing functionality directly inside the cloud ERP’s extensibility framework. When designed correctly, this keeps pricing logic inside the procurement workflow, preserves the platform’s upgrade path, and eliminates the overhead of managing a third-party integration. The tradeoff is that it requires deep Oracle Fusion Cloud technical expertise to build extensions that stay functional as the platform evolves and don’t create data integrity problems downstream.

The organizations that come out the other side without a crisis are, almost without exception, the ones that treated the gap-closure architecture as a first-class project deliverable—not something to figure out after go-live.

What to do before the business case closes

Pull a sample of your top 50 purchase orders by spend. For each one, figure out how the price was determined –simple unit price from a blanket agreement, formula-based calculation, qualifier-driven rate tied to a specific contract, or a manual override someone entered because nothing else worked. How many of those pricing structures exist natively in the system you’re migrating to? That number tells you more than the vendor demo.

Get procurement in the room before the vendor is selected. Category managers and procurement leads who negotiate supplier contracts know where the complexity lives. Their input during vendor evaluation is worth considerably more than their feedback during user acceptance testing, at which point the decisions have already been made and the budget has already been committed.

Get the gap list in writing before signing. Ask the implementation partner to document every functional gap between your current system and the target platform—including pricing—and document their proposed approach for each gap. A signed statement of work that doesn’t address known limitations isn’t a plan. It’s a transfer of risk onto the business.

A final word

Moving to cloud ERP makes sense for most organizations. I believe that. The maintenance advantages, the scalability, the integration ecosystem—those benefits are real and they compound over time.

But cloud doesn’t mean complete. In procurement specifically, the gap between what a mature on-premise system handles and what a current-generation cloud platform supports can be significantly wider than the business case assumed—and significantly narrower in the vendor documentation than in practice.

The organizations that navigate this without a crisis understood what they were giving up before they signed. They went in with their eyes open. That’s not a high bar. It just requires asking the right questions at the right time.

About the author

Anil Yellepeddi is an Oracle ERP procurement architect with 18 years of experience designing procure-to-pay solutions for global enterprises and international organizations, including engagements with the World Health Organization and the International Atomic Energy Agency.

Disclosure: The author works in an enterprise IT leadership role at a large U.S. manufacturing company. Views are the author’s own.

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 goal is to “digitally transform.” But digital tools cost money, and implementation can be resource-intensive and disruptive. So the goal should more accurately be stated as “to digitally transform in a way that delivers ROI.” But how do you really do it?

First, recognize that any company could choose to spend money on digital tools. Many companies believe that buying and installing tools is a transformation. To truly transform, however, the technology changes need to be broad in scope, not incremental. A related point is that digital transformations are becoming faster and cheaper to implement with the advent of more templatized “turnkey” solutions and AI-enabled lighter, faster technology.

However, spending money is not sufficient to transform. Companies that successfully digitally transform share at least these two factors in common: (1) they have high user adoption of the new digital tools, and (2) users leverage the new capabilities to drive materially improved results. This combination of adoption and improved results is what generates digital transformation ROI. Let’s look a bit deeper at both factors.

Why is adoption important?

If a company invests in new digital capabilities, a key assumption made is that users will adopt the new technology. But this assumption can be flawed. A sober assessment of the current state needs to be completed. Users are under pressure to get their work done, with limited “free time” to train and trial new software. Users are also typically comfortable and familiar with how things are done today. (see note at end of article) Change can be difficult, with some people simply resistant to change. Addressing this part of the user base takes work and reinforcement.

A key first step is confirming the standard work processes are congruent with the new technology, and if the work process is performed, results will improve (either better absolute results or parity results achieved with less effort). For all adjustments needed vs. current work processes, these should be clearly mapped in “as-is” and “to-be” visuals that people can follow. And if the new technology is not delivering better results, a selection or implementation error has occurred. Resources are being invested (dollars, organizational time) for no incremental value—this is negative ROI.

What it takes to deliver ROI

So, my people are adopting the new digital technology. I can relax now, start totaling up the ROI and head out for an early dinner, right? Sorry, no. Adoption is necessary but not sufficient. To get ROI, results must materially improve in a way that offsets the cost of the technology and implementation (people’s time, integration costs and any startup curves of lower capability). But merely offsetting costs is breaking even. To achieve ROI, results must improve in a clear, financially measurable way. This impact can affect either the top line (revenue) or bottom line (profit). For example, changes that enable higher-order fill rates or reduce new product innovation timelines can improve sales (top line). Technology that drives productivity or efficiency can improve profitability by reducing costs (bottom line). These measured results based on the financial metrics impact is how ROI from digital transformation is generated.

Benefits of getting ROI from a digital transformation: The financial benefit of recouping value greater than the investment is clear and obvious. However, there are additional benefits, including:

• Growing the organization’s digital savviness and openness to change. A successful transformation in one area can open the door to improvement in other areas.
• Growing the organization’s ability to successfully change. Change is not easy. Building the muscle memory required to successfully change can be a real boost.
• Improving morale. A successful digital transformation eliminates current work-process defects, removing employee dissatisfiers and enabling saved time to be reinvested in better results. This can directly impact attrition rates and employees’ perception of their company as “an employer of choice.”
• Increased competitiveness. Businesses do not operate in a vacuum. As competitors improve, transformation helps organizations keep up, stay ahead or close the gap.

Watch-outs: Unfortunately, there can be many intended or unintended barriers to achieving ROI from a digital transformation:

• Not achieving adoption or only superficial adoption (users’ fingers on new technology keyboards while defaulting to spreadsheets and transcribing into new systems). This may be caused by culture, change resistance, insufficient user engagement in the business case for change or technology that does not meaningfully improve the current state.
• Not converting adoption into better operational results.
• Not being able to measure operational improvements in a way that translates into financial gains.
• The potential ROI is relatively minor. Businesses operate in a world of choices, including competing investments. Often “hurdle rates” exist that transformations must clear to be worthwhile vs. selecting other potential investments.

Summary: How to get ROI from digital transformation

Many companies talk about digital transformation with the expectation that a clear “proven path” should exist, right? In reality, company starting points, business needs, scope of change and organizational readiness can vary significantly. What is more constant is the truism that digital transformation must involve a marked and significant change from the current state, users need to adopt the new tools and this adoption must drive financially measurable improvements in output results—with gains that more than offset the cost of buying and implementing the technology.

* Note: Some users will be skilled in the current process and tools, including any existing gaps, flaws, losses (extra steps, touches and time) they experience using the current tools. These users will often welcome improved technology as a vehicle to replace current-state defects with more value-added work. This change typically has a positive impact on morale: extra steps and touches are replaced by work that contributes more directly to improved results. If the technology improves their work process and results, these users can become the “change champions” who can show other users the benefits of change vs. the current state.

About the authors

Andrew Byer is a former P&G supply chain leader. Mike Dobslaw leads the EY Supply Chain Planning practice. To learn more about how EY and P&G team to support supply chain transformations, please write to michael.dobslaw@ey.com.

Executive takeaways

The U.S. industrial and distribution property market is continuing through a period of transformation that has direct implications for supply chain resilience, cost structure and long-term competitiveness. Rising labor costs, accelerating use of automation, shifting demand patterns and tightening infrastructure constraints are fundamentally changing how companies think about where and how they operate.

For supply chain leaders, industrial real estate is no longer a passive, back-office consideration. Property decisions now influence labor availability, throughput capacity, technology adoption and the ability to scale or pivot as market conditions evolve. A misaligned facility can lock in inefficiencies for years, while a well-planned site can become a strategic advantage.

Understanding today’s industrial property trends around leasing strategy, automation-enabled design, labor dynamics and regional market conditions is essential for leaders tasked with balancing cost, flexibility, and performance in an increasingly volatile environment.

Purpose-built facilities and automation

Automation continues to reshape industrial operations, placing greater importance on facility design. Many traditional speculative warehouse builds are not equipped to support advanced automation, leading to workflow constraints, inefficient layouts and higher long-term labor costs.

Purpose-built, built-to-suit facilities allow organizations to align physical space with operational requirements from the outset. Factors such as clear height, column spacing, floor flatness, availability of utilities and material flow can be optimized to support automation and reduce unnecessary movement. While upfront costs may be higher, these facilities often deliver meaningful gains in productivity, safety and cost efficiency over time.

This level of customization is also influencing broader market behavior. Because bespoke facilities typically require greater capital investment from both landlords and tenants—including specialized infrastructure, power capacity and structural modifications—they are increasingly associated with longer average lease terms. Extended commitments help justify the higher upfront expenditure while providing operational stability for tenants and predictable returns for property owners. As automation adoption grows, this shift toward longer-term, capital-intensive leasing structures is becoming a defining characteristic of modern industrial real estate strategy.

Labor costs and operational efficiency

Rising labor costs remain a central driver of industrial property strategy. As wages increase and labor availability tightens, the business case for automation and operational efficiency becomes more compelling. Even small inefficiencies in facility layout or workflow design can translate into significant labor expenses over the life of a building.

Thoughtful, operations-focused property planning helps mitigate these pressures by reducing manual handling, minimizing travel distances and enabling more consistent throughput. In this context, real estate decisions play a direct role in controlling labor-related costs and improving overall performance.

Market dynamics and vacancy trends

Industrial vacancy rates across the U.S. have fluctuated significantly in recent years. During the pandemic, surging e-commerce demand drove vacancy to historic lows, prompting rapid new development. As demand normalized, vacancy rates increased and today vary widely by region, asset type and proximity to population centers—often landing in the mid-to-high single digits in many markets.

For supply chain leaders, these regional dynamics are critical. Expansion or relocation decisions must balance space availability, rental costs, infrastructure readiness and operational fit. Markets with higher vacancy may offer short-term leverage but suitability ultimately depends on how well a facility supports long-term operational needs.

Key site selection factors

Several factors are emerging as especially important in industrial property decisions:

• Power availability: Automation, robotics and technology-enabled operations require reliable electrical infrastructure. In some markets, utility capacity or lengthy interconnection timelines can limit a site’s viability, regardless of location.
• Workforce access: Proximity to labor remains fundamental. Access to both skilled and entry-level workers—including alternative labor pools such as transitioning military personnel—can support consistent staffing and reduce turnover.
• Construction costs and timing: While construction costs have stabilized relative to recent peaks, uncertainty remains. Early planning, clear specifications and proactive negotiation are essential to managing risk and avoiding delays or cost overruns.

Best practices for supply chain professionals

When evaluating industrial property options, supply chain leaders should align real estate strategy closely with operational requirements. Workflow design, automation readiness, labor access and infrastructure capacity should be considered together—not in isolation.

Facilities should also be designed with adaptability in mind, allowing organizations to respond to evolving technologies and changing demand patterns. Equally important is an understanding of local market conditions, including vacancy trends, rental dynamics and construction constraints—all of which can materially affect both near-term decisions and long-term outcomes.

While built-to-suit facilities can offer meaningful efficiency advantages, customization should be balanced with cost discipline and future flexibility to ensure assets remain viable as operational needs evolve.

Looking ahead

Industrial property decisions are no longer defined solely by location or square footage. They are strategic supply chain decisions that influence labor costs, operational efficiency and the pace of technology adoption.

Organizations that proactively evaluate these factors—balancing flexibility, automation requirements and market conditions—will be better positioned to build resilient, efficient supply chains and compete effectively in a rapidly changing industrial landscape.

About the author

Aleks Leitmanis is an accomplished project management leader with over a decade of experience at TMX Transform across diverse industrial and commercial projects. Aleks joined TMX North America in September 2025 to again lead the Property and Project Management division, bringing a proven track record in property procurement through effective contract and design brief management and collaborative specialist advice. A key aspect of his experience has been delivering automated solutions to enhance client outcomes, including the design, procurement and implementation of a variety of automated systems.

Executive takeaways

Companies increasingly recognize that supplier integration into new product development (SINPD) is critical for innovation, speed to market, cost reduction, and competitive advantage. Yet many organizations continue to struggle with one fundamental issue: supply chain management (SCM) professionals are often expected to support strategic supplier collaboration without being fully prepared, empowered, or included early enough in the process.

Research involving 125 supply chain professionals across a broad range of industries suggests that organizations understand the strategic importance of supplier integration, but many are still not satisfied with the results they are achieving. More importantly, the findings suggest that organizational barriers and talent development gaps may be limiting the ability of SCM professionals to make meaningful strategic contributions to new product development initiatives.

Supplier integration is increasingly strategic

The research found that nearly 90% of respondents believe that developing and maintaining a technologically capable supply base is critical to their organization’s competitive success. In addition, approximately 70% of respondents indicated that their organizations plan to increase the use of collaborative supplier integration into new product development in the future.

These findings reinforce what many organizations are already experiencing firsthand. Suppliers are no longer simply providers of parts or services. They increasingly serve as sources of technical expertise, innovation, process improvement, and market responsiveness. As product development cycles accelerate and technologies become more complex, organizations are relying more heavily on suppliers to contribute ideas, technical capabilities, and specialized knowledge earlier in the development process.

This shift naturally places greater responsibility on SCM professionals. Supply chain personnel are often positioned between engineering, operations, suppliers, and business leadership, making them uniquely capable of facilitating collaboration across organizational boundaries.

The problem: Satisfaction with SINPD remains low

While organizations increasingly value supplier integration, many are not satisfied with the outcomes they are achieving. Only about 30% of respondents expressed a high level of satisfaction with their current collaborative SINPD efforts.

This gap between strategic importance and actual satisfaction raises important questions:

• Are organizations involving SCM professionals early enough?
• Do SCM personnel possess the technical and cross-functional skills needed to contribute strategically?
• Are organizational structures limiting collaboration?

The findings suggest that all three issues are present in many organizations.

Skill gaps continue to limit SCM contributions

The research identified significant concerns regarding the readiness of SCM professionals to participate strategically in supplier integration efforts.

Only about 45% of respondents believed their supply chain organizations possessed personnel with the skills necessary to evaluate the technical capabilities of suppliers for collaborative new product development. Similarly, only about 50% believed their organizations had the capabilities needed to assess supplier readiness for integration into NPD initiatives.

These findings suggest that many organizations expect SCM professionals to support increasingly technical and strategic initiatives without fully developing the associated competencies.

The rapid adoption of artificial intelligence, analytics tools, and digital supply chain technologies may further intensify these capability gaps. SCM professionals are now expected not only to coordinate supply activities, but also to interpret data, support technical evaluations, participate in cross functional decision-making, and contribute to long term strategic planning.

Organizational barriers are often the bigger issue

One of the most significant findings from the study was that many respondents believed SCM professionals may already possess valuable skills, but organizational structures often prevent those skills from being effectively utilized.

Several themes emerged repeatedly in the qualitative responses.

SCM is often viewed as tactical

Some respondents indicated that supply chain organizations are still viewed primarily as tactical support functions rather than strategic contributors. In these environments, SCM personnel may only become involved late in the process, often after major design or sourcing decisions have already been made.

As one respondent explained, “Supply chain is viewed as a support group that might help troubleshoot why a purchase order was not submitted correctly. They are not consulted about strategic decisions.”

When SCM is positioned primarily as a transactional function, organizations are less likely to recruit, develop, or empower supply chain professionals for strategic involvement in new product development.

Silos continue to limit collaboration

Other respondents highlighted organizational silos as a major barrier. Engineering, operations, procurement, and other business units often operate independently, limiting opportunities for collaboration during product development.

One participant noted that engineering departments sometimes “forget to involve other parties that will make the new product development successful.” Another respondent described a company culture that rewards narrow departmental performance rather than broader organizational alignment.

These silos can significantly reduce visibility, communication, and knowledge sharing across functions.

Training alone is not enough

Many organizations reported offering technical, leadership, and project management training. However, respondents repeatedly emphasized that training without opportunities for practical application often fails to create meaningful capability development.

Some organizations also indicated that existing training programs focus too heavily on narrow functional tasks rather than broader systems thinking and cross-functional collaboration.

In many cases, day-to-day operational pressures, manual work, and constant “firefighting” reduce the time available for employees to develop strategic skills.

How organizations are responding

The most common response identified in the study was increased and more targeted training. Respondents indicated that organizations are increasingly utilizing professional organizations, consultants, MBA programs, analytics training, and cross-functional development opportunities to enhance SCM capabilities.

Several respondents emphasized that technical skills alone are insufficient. Communication skills, collaboration skills, and leadership capabilities were also identified as critical components of effective supplier integration.

Some organizations reported hiring engineers into SCM roles and then providing business and supply chain training. Respondents generally believed that teaching business concepts to engineers may sometimes be easier than teaching highly technical product knowledge to traditional SCM personnel.

At the same time, respondents stressed that long-term improvement will require cultural change. SCM professionals must increasingly be viewed as strategic business partners rather than simply operational support personnel.

Implications for industry and higher education

The findings suggest that preparing SCM professionals for future supplier integration challenges will require a multi-pronged approach involving industry, universities, consultants, and professional organizations.

Organizations may need to:

• involve SCM earlier in product development,
• reduce cross-functional silos,
• create opportunities for practical application of skills,
• and invest more heavily in analytics, technical, and collaborative capabilities.

Universities may also need to reconsider how they prepare future SCM professionals. The study suggests that stronger exposure to engineering concepts, analytics, systems thinking, communication, and cross-functional collaboration could help better position graduates for future roles in new product development environments.

As supplier integration continues to evolve, the strategic role of SCM professionals will likely continue expanding. Organizations that successfully develop these capabilities may position themselves to achieve stronger innovation outcomes, improved supplier collaboration, and greater competitive advantage.

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 sourcing, operations, and supply chain risk management.

Dr. Thomas V. Scannell is a professor of supply chain management at Western Michigan University. His research and teaching focus on supply chain, operations, quality management, and supplier integration in new product development. He previously worked in electronic design, systems engineering, and program management.

Executive takeaways

When a major supply chain transformation program kicks off—a network redesign, a new ERP, a nearshoring initiative, a supplier consolidation—who is in the room? In most organizations, the answer is operations, IT, procurement and strategy. Finance, if it appears at all, shows up later, asked to validate numbers generated without its input and constrained by decisions it had no hand in making.

This is a strategic error and it is one that supply chain finance leaders have both the standing and the obligation to correct.

Every operational decision in a transformation program has a financial shadow: a network redesign changes inventory positioning and working capital requirements; a supplier consolidation alters payment terms across dozens of relationships; a nearshoring initiative shifts cost structures, currency exposures and financing needs simultaneously. That shadow can be managed proactively or discovered reactively, usually at the worst possible moment.

Drawing on my experiences leading supply chain finance teams, including partnering on programs of significant organizational and financial scale, this article offers a practical five-phase framework for supply chain finance leaders who want to step into a genuine co-authoring role in transformation.

Phase 1: Get in the room before the blueprint is drawn

Finance must be present at program inception, before operating model options are evaluated and before cost-benefit analyses are commissioned. At this stage, the finance leader’s role is not to approve or reject a direction but to map the financial architecture of each scenario under consideration: building working capital models (not just P&Ls) for each option, identifying supplier financing implications, and flagging liquidity or covenant risks that operational modeling will miss.

In practice, this often requires advocating for that seat rather than waiting to be invited. The framing that works: every CFO understands that a transformation program creating unforeseen working capital stress is a program that will underdeliver.

Key actions:

• Request a seat on the program steering committee, not just the finance sign-off workstream
• Build scenario-based working capital models for each transformation option under review
• Map supplier financing relationships that will be affected and assess refinancing risk early
• Establish the financial KPIs the transformation will be held accountable to before the program begins

Phase 2: Design a financial architecture for the new operating model

Once a transformation direction is chosen, finance must design the working capital and supplier financing model that will sustain it—not inherit the one that existed before. Most programs make the mistake of carrying legacy financial structures into a transformed operating model: the same payment terms, the same supplier financing programs, the same cash conversion cycle targets. But a new operating model often requires a fundamentally different financial architecture to function at its intended efficiency.

A shift toward fewer, deeper supplier relationships may create the conditions for a supply chain finance program that was not viable at the prior supplier count. A nearshoring initiative may require renegotiated payment terms that reflect new lead times and inventory profiles. A digitization program may unlock the data transparency needed to introduce risk-tiered supplier financing for the first time.

Key actions:

• Design payment terms and working capital targets specific to the new model, not inherited from the old one
• Assess whether the transformation creates new eligibility for supply chain finance programs (reverse factoring, dynamic discounting, inventory financing)
• Engage banking partners early to structure financing solutions aligned with the new supplier and inventory profile
• Create a financial transition plan that bridges the legacy and target models during the implementation overlap period

Phase 3: Manage financial risk actively through implementation

Implementation is the highest-risk financial period of any transformation. Inventory buffers built ahead of system cutovers, supplier relationships in transition, payment process disruptions, and overlapping working capital models create a period of elevated exposure. This is not a failure of planning—it is an inherent feature of transition. The question is whether finance has anticipated it.

The key is building what I think of as “financial signal towers:” real-time dashboards that track working capital, payables, and supplier financing utilization against transformation milestones. These tools allow finance to distinguish between expected volatility within the plan and unexpected stress requiring intervention, without waiting for month-end close to surface problems that needed attention weeks earlier.

Key actions:

• Build a real-time financial monitoring framework specific to the transformation, separate from standard reporting cadences
• Pre-negotiate liquidity buffers or credit facility headroom before implementation peaks
• Establish clear working capital deviation thresholds that trigger escalation to program leadership
• Protect supplier financing program continuity—disruptions during volatile periods can accelerate supplier financial distress at exactly the wrong moment

Phase 4: Lock in financial value after go-live

Transformation programs frequently declare victory at go-live. Finance must ensure the working capital and supplier financing improvements the program was designed to generate are actually realized—and made permanent. This is where many transformations quietly fail: the operational improvements are real, but within 18 months the working capital profile of the “transformed” organization begins to resemble the one that justified the transformation in the first place. Process disciplines erode. Exceptions accumulate. Value leaks.

Finance’s role here is value custodian: tracking benefits against the business case, identifying where value is leaking, and working with operational leaders to close gaps before they become structural.

Key actions:

• Establish a post-implementation value tracking dashboard aligned directly to the transformation business case
• Set 90-day, 180-day, and 12-month financial benefit milestones and hold program owners accountable to them
• Embed new working capital and payment term disciplines into standard operating procedures—not as temporary program measures
• Report realized vs. projected financial benefits to program sponsors and the CFO at regular intervals for at least 12 months post-go-live

Phase 5: Build capability for continuous transformation

In an era of continuous disruption, the goal should not be to manage one transformation well—it should be to build the organizational capability to manage transformation as a permanent operating condition. This means developing finance teams that are fluent in supply chain operations, building modeling infrastructure that can rapidly scenario-plan new initiatives, and creating the cross-functional relationships that earn finance a seat in strategic conversations rather than requiring it to argue its way in each time.

Key actions:

• Invest in cross-functional training so finance team members can engage credibly with operational and procurement counterparts
• Build modular working capital and supplier financing models that can be rapidly adapted to new transformation scenarios
• Establish a standing transformation finance function within the supply chain finance team, rather than treating it as an ad hoc responsibility

Finance belongs at the center

Supply chain transformation is, at its core, a financial challenge as much as an operational one. A network redesign that creates working capital stress it was never designed to absorb is not a transformation; it is a trade-off that was not fully understood. A supplier consolidation that triggers financing gaps the organization scrambles to cover is not a success; it is a near-miss.

The five-phase framework presented here asks supply chain finance leaders to do something that requires both skill and organizational will: to show up as strategists, architects, risk managers, and value custodians across the full lifecycle of transformation, not just as validators at the end.

The organizations that get this right build supply chains that are not just more efficient, but more resilient and more consistently capable of delivering the outcomes that transformation programs promise. Finance leaders have a central role to play in building that capability. The invitation to step into it is open. The question is whether finance will claim it.

About the author

Masha Chandrasekaran is senior finance leader with experience spanning post-merger integration, value-chain transformation and supply-chain finance across global consumer goods operations. Her work focuses on translating large-scale operational and organizational change into sustainable financial outcomes, particularly in areas where strategy often breaks down during execution and that is what the attached article focuses on.

Join Jacob Gorm Larsen, Founder, Moneyball CPH and experts from Coupa for a practitioner’s guide to building a connected intelligence loop that turns strategic intent into mathematical execution.

• Well-informed and Implementation Ready Category and Suppler Strategy: Move beyond "Hero Culture" with AI-guided workflows that transform consulting frameworks (Kraljic, Five Forces) into executable sourcing and supply chain constraints.

• Multi-attribute Sourcing Optimization Solve the "Quadrillion Permutation Problem" using combinatorial optimization and game theory to evaluate millions of award scenarios across cost, risk, and ESG.

• Operational Reality through Supply Chain Optimization: Ground your awards in operational reality by using Digital Twins to model network constraints and "what-if" disruptions before they hit production.

Procurement, sourcing and in-bound supply chain and logistics executives will learn about real-life customer examples across multiple industries.

The Result: An adaptive procurement operation where every sourcing decision is strategically informed, mathematically optimized, and operationally resilient.
 

SPEAKERS:

Jacob Gorm Larsen, Founder, Moneyball CPH; Nari Viswanathan, Head of Product Marketing, Supply Chain and Direct Spend, Coupa Software and Andrew Speck RVP, Category Management Center of Excellence, Coupa Software

Executive takeaways

The push for supply chain artificial intelligence investments is not facing a lot of pushback from the finance office, but it is facing scrutiny on the return being generated.

“The COO is getting money from the CFO using the word AI,” Al Mendoza, U.S. & Americas supply chain leader for EY, told Supply Chain Management Review in an interview at the Gartner Supply Chain/Xpo Symposium last month. “Now the CFO is coming back and saying, ‘Where’s my return on investment?’”

AI investment is similar to any other investment where the finance leaders expect to see a return on investment. That could be cost savings, streamlined operations, or transformational change. But, while companies are investing aggressively, they are still falling short on documenting the transformational gains they initially expected.

“What we’re finding is … 96% are still finding value,” Mendoza said. “It’s just not the transformational value they thought there was going to be.”

Investment is high, but clarity is not

Despite continued macroeconomic and geopolitical uncertainty, supply chain remains a top priority in the C-suite. Mendoza pointed to strong investment trends, noting that a significant share of companies are committing more than $10 million to artificial intelligence initiatives. At the same time, he described a market still searching for direction.

“I think there’s an overall lack of clarity exactly where the market is going,” he said. “It’s very clear that it’s going to be more digitized and more AI, but how that translates into value is still evolving.”

That uncertainty is leading many organizations into what Mendoza described as a fragmented approach—pursuing isolated use cases rather than cohesive transformation.

Technology alone is not the differentiator

For companies chasing competitive advantage, Mendoza said the technology isn’t often the answer.

“The moat isn’t that I have the best technology because that can be replaced in five minutes,” he said. Instead, the differentiators are less flashy and more difficult to build.

“It’s really, do I have the culture? Do I have the management systems? Do I have the data? Do I have the standardization?” he said, emphasizing that these foundational elements are what allow companies to sustain performance and continuously improve.

This is where many organizations fall short. While nearly all are investing in new tools, fewer are investing with the same intensity in the operating model required to make those tools effective.

The rise of the “value blueprint”

To address that gap, Mendoza described a shift in how EY is working with clients—moving from isolated use cases toward what he called a “value blueprint” approach.

“It’s really hard for companies to backdoor into transformation,” he said. “If you have a lot of very interesting use cases, you’re going to get stuck in very interesting use case land.”

The alternative is to step back and rethink processes end-to-end. Mendoza described it as “opening up the aperture.” That means reimagining workflows such as forecast-to-fulfillment or order-to-cash from a zero-based perspective, aligning supply chain, finance, and technology into a unified strategy.

“Here’s my big vision,” he said. “How do all my use cases enable me to be moving toward that?”

AI success starts with the problem

Even with the momentum behind AI, Mendoza reinforced a principle that continues to surface across the industry: the most successful companies are those that start with the problem, not the technology.

Referencing a well-known Einstein quote, he added: “I spend 95% of my time thinking of the problem and 5% solving it.”

That mindset is critical in avoiding what he described as underachieving AI programs that deliver incremental improvements but fail to move the business in a meaningful way.

“If your solve is very nuanced, very small, it’s not going to bring that transformational value that you need,” he said.

Workforce reinvention

One of the impacts of AI adoption is its impact on the workforce. Mendoza pushed back on the idea that automation is primarily about replacement, instead framing it as a shift in skill requirements. “What we’re going to create are users that need to have breadth of their skillset and real expertise,” he said.

That shift requires companies to rethink how they train and develop employees, particularly as labor shortages persist across supply chain functions. Leading organizations, he noted, are investing in upskilling rather than relying solely on external hiring. They are also capturing institutional knowledge and tracking where human intervention occurs so they can use that data to digitize and automate decision-making over time.

The foundation problem

If there is a central point to AI investment, it is that the most important work in a transformation is the least visible work, and that makes it harder to justify to the CFO.

“It’s so much cooler to design the house than to pay for the foundation,” Mendoza said.

That foundation includes standardized processes, clean data, and management systems that empower employees to execute consistently. It is also where many transformation efforts stall, particularly when faced with short-term financial pressures.

“There will be something with higher ROI that you can pick from,” Mendoza acknowledged. “But sustainable ROI is a different way to look at it.”

Supply chain’s opportunity

The current environment is marked by disruption, cost pressure, and shifting demand, but it has elevated supply chain’s importance within organizations. Mendoza noted that the vast majority of CEOs now see supply chain as materially impacting financial performance. Yet despite that visibility, supply chain leaders still face a challenge: translating operational impact into strategic influence.

“We have gotten invited to the boardroom,” he said. “We’ve got to add value when we’re there.”

That means moving beyond cost discussions and demonstrating how supply chain contributes to growth, customer experience, and speed to market.

“We need to bring in how we are part of the organic growth mindset that the C-suite really has,” he said.

The bottom line

AI may be driving urgency and unlocking budgets, but it is not, by itself, delivering transformation. The companies pulling ahead are those willing to do the harder work: aligning strategy, investing in people, and building the operational foundation that allows technology to scale.

Or, as Mendoza put it, the real challenge isn’t accessing AI, it’s building something that can return measurable value.

Executive takeaways

Part 1: The room goes quiet

A few weeks ago, I showed a colleague a demo of a supply chain planning application I had built. A sharp, experienced supply chain planner who has been navigating demand forecasting, capacity management, and supplier risk for over a decade. She is exactly the kind of person who should be excited about AI.

“That’s cool,” she said politely when I opened the app. Then I clicked through the tabs, demand forecasting with manual overrides and natural language input, capacity planning by resource and plant, a bottleneck analysis with cost scenarios, a bill-of-materials navigator, an executive S&OP summary with assessment of three capacity scenarios, a supplier risk map, and more. Her tone changed: “Wait. Did you build this yourself?” When I told her it took about 30 hours, she went quiet for a moment. Then: “I had absolutely no idea this was possible.”

That reaction is something I have now seen several times. In boardrooms, in workshops, in informal conversations with supply chain professionals at every level. And the discussions that follow are always similar:

• “But how? I thought you needed a whole development team for something like this.”
• “We’ve been using ChatGPT for writing emails. This is... completely different.”
• “Do we still need that APS implementation we’ve been scoping for the last 6 months?”
• “Can I show this to our head of supply chain? To our COO? Today?”

The gap between what most people believe AI can do today and what it actually can do is enormous. I am not talking about theoretical futures or research papers. I am talking about right now. A planner with deep domain knowledge can build functional business applications in days, not months. I have to say: this genuinely blew my mind.

I want to be direct about one thing before we go further: the app I built does not replace a full-scale Advanced Planning System (APS). A production-grade SAP IBP, Kinaxis, Blue Yonder or o9 implementation comes with years of validated data integration, enterprise-grade scalability, regulatory compliance, and dedicated change management. My 30-hour prototype does not have those things, and it is not trying to.

It is all about AI literacy

What I wanted to prove is what happens to the conversation when a supply chain leader or a planner has built something like this, even as a prototype. AI literacy fundamentally changes your ability to challenge software vendors and to ask hard questions about what genuinely requires a multi-million-EUR implementation versus what could be solved with a smart, purpose-built tool. The organizations that have spent years and fortunes on custom-specific APS configuration deserve better advocates. People who understand what is actually hard and what is not.

I built the test company in about 10 hours and the app itself in roughly 30—all through conversation with an AI, no traditional coding. I will go into the full details in Part 3.

Part 2: First, I needed a supply chain to plan

You cannot build a planning application in a vacuum. You need a supply chain to plan.

So the first thing I did was create a fictional electronics manufacturer I called ElectroTech Industries. This part took around 10 hours. I designed and vibe-coded the structure of the company, including the BOM, historical demand, and full P&L, and validated the consistency in several interactive steps.

ElectroTech operates three plants, near Lyon, France; Karlsruhe, Germany; and Berlin, Germany, feeding two European distribution hubs and a portfolio of 10 finished goods across B2C and B2B channels. Smart home hubs, industrial controllers, and power management modules. Products with real-world complexity: multi-level bills of materials, shared components, long-lead sub-assemblies, seasonal demand patterns, and spare parts demand on finished product and component level.

Figure 1: The physical structure of the test company ElectroTech.

Figure 2: The Bill of Materials (BOM) of the test company ElectroTech.

I vibe-coded realistic master data, products, resources, routings, suppliers, historical demand, inventory postions, cost rates and structured it into a relational database. This became the foundation on which everything else was built.

When I showed the experienced planner the app, her first instinct was practical: she started thinking out loud about which of her real planning challenges it could address. The demand override feature, which lets you type a command like “increase all products in the B2C channel in November by 10%,” and have the app apply it across the forecast, made her lean forward. “That’s exactly the kind of quick adjustment we do every month. Except right now it takes half a day with our mix of Excel, e-mail, and our system.”

Then she said something that stayed with me. She realized, watching the app respond to prompts and questions, that interacting with generative AI is not like using a search engine or a reporting tool. It is closer to working with a new colleague who is extremely capable but has just walked in the door. They do not know your company, your terminology, your edge cases, your unwritten rules. You have to onboard them. You have to give context, explain constraints, describe what matters and why, the same mentoring and coaching you would invest in any talented person new to the role. If you just throw a task at it without that investment, you get something generic (or wrong).

“You have to treat your GenAI engine like a smart colleague on their first week, not a search engine, not a calculator.”

That is one of the most honest and useful descriptions of working with generative AI I have heard. The organizations getting real value from these tools are the ones treating onboarding seriously.

Part 3: Building the app—what I actually learned

The application runs as a Flask web app with a SQLite database and a JavaScript front end. It has eight tabs: Demand Planning, Supply Planning, BOM Navigator, Capacity Solutions, Executive S&OP Summary, Flow Analysis, Suppliers & Risk, Planner Knowledge, and Analytics. I built it almost entirely through conversation with Claude Code, describing what I wanted, reviewing what came back, refining. Around 15 to 20 substantive sessions in total.

Learning 1: The result was impressively close to what I had in mind without over-specifying

The demand planning tab should display the statistical forecast, allow the demand planner to update it, track those changes and enabling coaching. The visuals created were impressive, and after asking to add a text field to enter the changes in natural language, I was blown away.

Figure 3: Demand Planning tab of the S&OP app showing KPIs, forecast, where to focus (scatter of FC Accuracy over revenue), manual update, and the possibility to enter a normal text to change the forecast.

The supply planning tab shows the unconstrained and constrained views, helping you understand bottlenecks and solve them. I also brainstormed and implemented a chain of bottleneck resolution paths to define priorities.

Figure 4: Supply Planning tab of the S&OP app - first unconstrained view, then optimizing considering constraints and additional capacity.

Learning 2: Domain knowledge is not optional—it makes the solution really cool

“Design an S&OP app” would have given me something. But it would have been a generic dashboard with some charts and a table or two. What made this actually useful was knowing the questions S&OP is trying to answer.

I knew that capacity planning needs to work at the resource level, not just the plant level, that overload scenarios should show not just utilization percentages but the revenue at risk. A planner needs to see whether a Saturday shift or a third shift would actually resolve the overload and what it would cost. All of these were vibe-coded into the app, with real MRP, capacity planning, pull-forward, etc.

None of that came from random prompting but from years of supply chain experience. The AI is a very powerful team of builders that work for you, challenge you, and add meaningful details. The domain expert must always maintain control.

I wanted to be able to navigate the BOM—not in the way you would do with traversing MD04s, but meaningful and efficient. GenAI did design a BOM navigator that not only shows the structure, but also KPIs like supplier (in case of sourcing), lead time, inventory, and color coding the reliability of the supplier. I was amazed.

Figure 5: The BOM navigator - expands the BOM and shows relevant KPIs like inventory, reliability of the supplier, and others.

Learning 3: You do not need to specify every single detail—let the GenAI surprise you

When I was building the supplier risk map, I asked Claude to “use realistic maritime shipping routes on the map.” I did not specify the routes. What came back routed the Lyon plant’s sea freight through Marseille, and the Karlsruhe plant’s shipments through Rotterdam. Correct, contextually appropriate, completely unprompted. It understood the geography.

Figure 6: The Supplier & Risk tab with correct maritime routes.

When I asked for capacity scenarios covering a Saturday shift and a third shift, I added: “Please consider the additional cost for these options.” I did not provide the numbers. The AI researched the relevant shift premium rates—in Germany, the legal Feiertagszuschlag (public holiday premium) is 100%, and the third-shift night premium is around 25%—and incorporated them into the cost calculations. In a real application, those premiums would need to be checked and maybe further adjusted, but always in conversation with the GenAI, not as code.

The pattern I found: the broader the creative or contextual judgment needed, the more latitude you can give. The more specific the business rule, the more precisely you need to describe it. AI fills the gaps intelligently when the gaps are genuinely a matter of judgment. It cannot fill the gaps when the answer is specific to your company, your contracts, or your legal context.

Learning 4: Iteration beats specification—in outcome and speed

I did not write a requirements document nor a technical specification. I described my ideas, checked what was wrong or missing, and described the next improvement.

The capacity table started as a flat list of 68 rows, one per resource per month. After building it, I could see it was unusable at that scale. So I asked for collapsible grouping by resource. The BOM navigator started without any pegging capability. Once I had the basic tree, I could articulate what was missing: “I want to enter a quantity and a target date and see what I need to order, and by when.” The AI created an overview of the pegged quantity, indicating the bottlenecks. You cannot write that requirement clearly until you have seen what it would replace.

This is how experienced planners and supply chain professionals should be thinking about AI-assisted development. You engage in a series of increasingly detailed conversations, each one building on what the last one produced.

The scenario calculation for the exec S&OP meeting

Most S&OP processes I assessed produce only one solution, preventing trade-off discussion and decision-making in the exec S&OP meeting. We can only accept the solution; rejecting it is not an option. My S&OP app should consider different options. I created an overload and asked for additional capacity to resolve it. The Capacity Solutions tab shows, for every overloaded resource in the planning horizon, four options: Saturday shift, third shift, combined, or working on public holidays. For each option, it shows the added capacity in minutes, the cost in euros, whether it fully resolves the overload, and the resulting new utilization percentage. There is a cost-per-resolved-minute comparison at the bottom and a KPI strip showing total revenue at risk across the planning horizon.

Figure 7: Solving the capacity shortages by adding Saturday, 3rd shift, and bank holidays - including color coding whether it worked or not yet.

This, along with the overview in the exec S&OP tab, makes a real-scenario discussion possible. I also added a conversational assistant—the chat button in the bottom-right corner—to ask questions like “how much more expensive is the Saturday shift” etc.

Figure 8: Showing the scenarios with impact on cost, inventory and secured margin gives a clear recommendation on what to implement

Closing remarks

I am not arguing that every supply chain team should go build its own planning software. I am arguing that the people who understand supply chains should understand what is now genuinely buildable and how quickly.

That knowledge changes how you evaluate vendors. It changes how you scope projects. It changes what you ask for and what you are willing to pay for. And occasionally it means that the right answer is to build something targeted and functional yourself, rather than spending 18 months configuring a system designed for the average company rather than yours.

If you are curious about the test company, ElectroTech Industries, with its full master data, database, routing tables, demand history, and cost structure, I am happy to share it. It is a useful sandbox for anyone who wants to experiment with planning applications, AI-assisted analysis, or simply learn what realistic planning data looks like up close.

Send me an email if you would like to get access, have questions, or want to compare notes on what you are building.

About the author

Knut Alicke is a supply chain expert, keynote speaker, and professor at the University of Cologne, KIT, Karlsruhe, and SKEMA. He is a Partner emeritus at McKinsey & Company and works at the intersection of supply chain strategy, planning systems, and AI.

Reactions, pushback, and war stories from your own planning implementations: knut@alicke–scm.com. Check out www.alicke-scm.com.

Executive takeaways

Global business leaders can no longer treat AI regulation as a secondary issue. Regulatory divergence is reshaping supply chains by imposing uneven compliance obligations that extend across supplier networks.

At one end are stringent, risk-based models such as the European Union AI Act, which impose detailed compliance, documentation, and governance requirements. These requirements extend deep into supplier networks and shape how AI systems are designed and deployed. At the other end are pro-innovation approaches in the UK, Japan, Singapore, and the UAE, which emphasize flexibility and rapid adoption.

These regulations, grounded in principles such as safety, transparency, fairness, accountability, and human oversight, are no longer abstract policy considerations. They directly influence cost, speed, scalability, resilience, and competitiveness across global supply chains.

For leadership teams, the implication is clear: AI regulation must be treated as a permanent operating condition. Organizations that build flexible governance models and design systems to operate across diverse regulatory environments will sustain performance. Those that do not will face rising costs, delayed deployments, and increasing fragmentation.

Existing regulations

AI regulation varies significantly in its operational impact, with the most stringent regulations shaping how systems are designed, deployed, and governed across supply chain ecosystems. At the most restrictive end are the EU’s AI Act and China’s generative AI regulations.

The EU AI Act introduces a four-tier risk classification system that determines legal, technical, and governance obligations. AI systems used in supply chain operations require formal documentation, continuous risk management, and mandatory human oversight. Critically, accountability extends across suppliers and partners, placing end-to-end responsibility on the enterprise. These requirements increase time to deployment and apply to any organization operating within the EU, with penalties of up to €35 million or 7% of global revenue.

China’s approach imposes different but equally material constraints, requiring localized AI architectures and alignment with national regulatory frameworks. This requires global firms to adapt infrastructure and partnerships, increasing cost and operational complexity.

A second category includes frameworks that impose governance requirements while allowing greater flexibility. This includes evolving U.S. federal and state-level initiatives, as well as regulations such as India’s Digital Personal Data Protection Act.

In the United States, state-level fragmentation is increasing complexity. California emphasizes transparency, disclosure, and bias mitigation, while Colorado applies a risk-based framework like the EU. These requirements directly affect procurement, workforce management, and third-party AI usage.

India’s data protection regime introduces constraints on data collection, processing, and cross-border transfer, limiting the scalability of global analytics platforms.

At the most flexible end are pro-innovation models in the UK, Singapore, Japan, and the UAE. These frameworks prioritize guidance over mandates, enabling experimentation through mechanisms such as regulatory sandboxes. This supports faster iteration, lower compliance costs, and accelerated adoption.

Several jurisdictions, including Canada, Australia, and Brazil, remain in transition, creating ongoing uncertainty. This reinforces the need for leaders to continuously monitor developments and maintain flexible operating models.

Supply chain impact (demand, people, technology, and risk)

In highly regulated environments, organizations face increased costs associated with compliance infrastructure, including documentation, auditability, traceability, and ongoing monitoring. These requirements demand investment in legal, technical, and operational capabilities while slowing deployment timelines and limiting scalability.

To respond effectively, companies must align supply chain, IT, legal, and procurement functions to meet varying regulatory requirements. Leading organizations will maintain efficiency in restrictive jurisdictions while strategically leveraging more flexible environments to accelerate innovation.

AI has the potential to significantly enhance demand forecasting and supply chain responsiveness. However, stricter regulatory environments can constrain these capabilities by limiting data access, restricting automated decision-making, and requiring human oversight. As a result, organizations operating in less restrictive environments are better positioned to fully leverage AI, creating a widening performance gap and reinforcing competitive asymmetry.

Talent requirements are also evolving. Demand is increasing for professionals who can bridge supply chain operations and AI expertise within a regulatory context, driving higher labor costs and intensifying competition for specialized talent.

At the same time, AI and advanced analytics are improving visibility, agility, and operational strength. However, organizations in stricter regulatory environments must manage more complex, modular technology architectures designed to meet regional requirements for data localization, transparency, and accountability. These investments are significant. Gartner projects global spending on AI governance will exceed $1 billion by 2030, up from $492 million in 2026.

Risk management is also becoming more complex. While AI enhances the ability to identify supplier vulnerabilities and anticipate disruptions, it introduces new risks, including algorithmic bias and more sophisticated cyber threats. Supply chain leaders must balance rapid innovation with strong governance and control.

Strategic actions for supply chain leaders

To prepare for the obstacles created by AI regulations, DSCI recommends that leaders:

1. Invest in compliance infrastructure early. Build scalable systems to address documentation, auditability, traceability, monitoring, and human oversight before requirements become more restrictive and costly.
2. Design modular and adaptable technology architectures. Enable rapid adjustment to changing regional regulations, data requirements, and governance standards without major operational disruption.
3. Balance speed and governance. Foster rapid AI innovation while maintaining accountability, transparency, and risk control.

Seizing competitive advantage

The widening spectrum of global AI regulation is becoming a direct operational and competitive issue for supply chains. From highly restrictive frameworks that add friction to flexible models that enable rapid innovation, regulation is reshaping how supply chains are designed, managed, and optimized.

For supply chain leaders, the implication is clear: organizations must develop the capability to operate effectively across multiple regulatory environments simultaneously. Companies that treat compliance as an integrated business capability, rather than a reactive legal function, will be better positioned to scale AI adoption without slowing decision-making or operational performance. This requires strong coordination across legal, technology, operations, procurement, and risk management.

Regulatory asymmetry creates differences in cost structures, responsiveness, talent requirements, and technological scalability. Organizations that maintain compliance in high-friction environments while strategically leveraging more flexible regimes to pilot innovations and optimize operations will gain advantages in speed, resilience, and innovation capacity.

About the authors

Dravida Seetharam, is a fellow at the Center for Global Enterprise. Sarah Lahti is the director of operations and program management for the Digital Supply Chain Institute.

Executive takeaways

After four years and more than 22 startup pilots across Asia Pacific, PepsiCo is changing the focus of its Greenhouse Program from experimentation to operational deployment as the company looks to integrate sustainability technologies deeper into its supply chain network.

The company recently announced the 2026 “IMPACT Edition” of its Greenhouse Program in Asia Pacific, an initiative designed to move promising startup technologies beyond proof-of-concept and into day-to-day operations across PepsiCo’s regional supply chain.

The shift reflects a broader evolution occurring across the supply chain industry as companies increasingly move toward technologies capable of delivering measurable operational and financial outcomes.

“What encouraged the shift was the recognition that, after four years and 22 pilots, the next important question was no longer whether these solutions had potential, but how to scale the ones that were proving their value,” Ashley Brown, sustainability vice president for Asia Pacific at PepsiCo, told Supply Chain Management Review.

Operational evolution

PepsiCo originally launched the program in Asia Pacific in 2023, working primarily with early stage sustainability startups focused on areas such as logistics optimization, sustainable agriculture, packaging circularity, and emissions reduction. But according to Brown, both the startup ecosystem and PepsiCo’s own operational maturity have evolved significantly since then.

The seven-month program pairs startups with cross-functional PepsiCo mentors and an expanded partner network. The partners across venture capital, agriculture, and innovation support project development, help enable market access, and, where relevant, explore potential investment opportunities as solutions mature. These partners include Artesian, AgFunder Asia, SAIL (Nanyang Technological University Singapore), and AgriFutures growAG, alongside returning partners Circulate Capital, GC Ventures, and CM Venture Capital.

“We began in 2023 by working with earlier-stage innovators, and over time both the program and the cohort have matured,” Brown said. “By 2026, the focus had naturally moved toward later-stage integration, where the challenge is less about identifying promising ideas and more about embedding them into actual operations.”

While many companies have experimented with startup-driven sustainability technologies over the past several years, the number that have successfully moved into operational workflows is much fewer.

Brown said PepsiCo learned quickly that moving from pilot to deployment requires much more than proving a technology works technically. “That requires much more than a successful trial,” she said. “It needs access to the business, the right internal sponsorship, and a practical route to adoption.”

Ready to scale

The 2026 program includes five returning startup participants selected specifically for their readiness to scale across PepsiCo’s operational footprint. Those companies span multiple areas of the supply chain, including transportation optimization, regenerative agriculture, recycling infrastructure, and emissions reduction technologies.

Among the participating startups is Australia-based Adiona, which provides AI-powered logistics optimization tools designed to improve route planning and fleet efficiency while reducing Scope 3 emissions across transportation networks.

Other participants include Takachar, whose biochar technology converts agricultural crop residue into soil-enhancing material; Beijing AIForce Tech, which develops electric agricultural machinery and automation systems; Bali Waste Cycle, focused on low-value plastics recovery and recycling infrastructure; and X-Centric, a digital soil analytics platform supporting regenerative agriculture initiatives.

For PepsiCo, those technologies are increasingly viewed as operational infrastructure supporting long-term resiliency and efficiency goals.

“Through our PepsiCo Positive (pep+) transformation, sustainability is embedded through our business as we aim to create resilient operations for the future,” Brown said. “That means the technologies and solutions we are investing in are not just there to test new ideas or support reporting, they are helping shape how we run supply chains, manage procurement, guide decisions and improve operational performance.”

Cross-functional collaboration

Operational integration is one of the more significant changes occurring within large enterprise sustainability programs. Historically, sustainability initiatives often operated separately from core supply chain and procurement functions, but PepsiCo is taking a different approach.

“One of the biggest shifts has been the move to cross-functional squad teams, where supply chain, procurement, R&D and operations come together much earlier to define the outcomes that matter as we invest in these new innovations,” Brown said.

As a result, sustainability and commercial performance discussions now occur simultaneously rather than independently.

“Conversations about sustainability and commercial value are happening together from the start,” Brown said, “which makes these solutions far more practical, scalable and relevant to core operations.”

Transportation has become one of the clearest examples of that operational convergence. For global food and beverage companies, Scope 3 emissions—those generated throughout the broader value chain—remain among the most difficult sustainability challenges to address. Logistics and transportation networks represent one of the few areas where companies can directly influence both cost and emissions performance simultaneously.

“Transportation optimization and sustainability need to be part of the same conversation,” Brown said. “Transportation optimization is not just about efficiency, service levels or cost; it is also a key part of how organizations make progress on sustainability goals.”

Brown added that companies often miss opportunities when transportation efficiency and sustainability initiatives operate independently.

“When transportation is managed as part of a broader operational workstream, companies are better able to design solutions that improve network performance while also reducing emissions,” she said.

The company’s broader goal is focused on building a repeatable operational model for scaling external innovation into enterprise supply chains. According to Brown, one of the most important lessons from PepsiCo’s first 22 startup pilots was that technical innovation alone rarely guarantees operational success.

“The 22 pilots gave us a much clearer view of what it really takes to move from experimentation to scale,” she said. “They showed that strong ideas on their own are not enough; solutions need cross-functional buy-in, operational feasibility and a clear connection to the biggest sustainability challenges facing food and beverage players.

“Ultimately, the success of this program is measured by the impact that these technologies can have on business [to] help to deliver sustainability ambitions, productivity and efficiency, and process improvements,” she added.

Executive takeaways

The NextGen Supply Chain Conference has announced that Mar Gimeno, Associate VP, U.S. Supply Chain at Eli Lilly and Company, will join the keynote lineup for the 2026 event.

Gimeno will speak at the conference taking place Oct. 21-23, 2026, at the W Nashville hotel in Nashville, Tennessee.

The NextGen Supply Chain Conference brings together senior supply chain, logistics, operations, procurement, and technology leaders for three days of practitioner-led discussions focused on real-world execution, workforce development, and operational transformation.

The 2026 theme, Innovate. Upskill. Transform., reflects the conference’s focus on helping organizations understand emerging technologies, develop the workforce skills needed to operationalize those technologies, and transform supply chain operations to meet future challenges. The event is expected to attract approximately top-level senior supply chain executives, solution providers, consultants, and academics.

• Register to attend. Click here.
• Explore sponsorship opportunities to engage directly with a highly targeted executive audience. Click here.

As one of the world’s leading pharmaceutical manufacturers, Eli Lilly operates a highly complex global supply chain supporting the production and delivery of critical medicines. The pharmaceutical industry continues to face increasing pressure to balance growth, resilience, regulatory requirements, network expansion, and technology adoption, making supply chain leadership more important than ever.

Read more: Tractor Supply to receive NextGen Supply Chain Visionary Award

Gimeno joins a growing list of speakers and industry leaders participating in the 2026 conference. Earlier this year, the conference announced that Tractor Supply Company will receive the 2026 NextGen Visionary Award, with Colin Yankee, EVP of Supply Chain, participating in the Visionary Keynote fireside chat.

The 2026 agenda is organized around four industry focus areas:

• Logistics & Fulfillment
• Retail
• Food & Beverage
• Chemicals/Pharmaceuticals

Confirmed speakers include:

• DP World: Carey Boone, vp-transformation-Americas
• Penske Logistics: Andy Moses, SVP of sales and solutions
• Amazon: Debanshu Sharma, senior supply chain manager
• GXO Logistics: Jeff Kellan, Division President Omnichannel Retail in AMAPAC
• Fanatics: Bijoy Sasidharan, director-capacity planning and forecasting
• Tractor Supply: Colin Yankee, CSCO
• Berry Direct (Edible Arrangements): Jay Di Sieno, senior supply chain manager
• Mars: Kristin Daihes, SVP of Analytics, Digital and Data
• Target: Eric Watts, VP of food supply chain operations
• Evonik: Santosh Yersuri, senior supply chain manager
• Johnson & Johnson: Ron Volans
• Eli Lily: Mar Gimeno, Associate VP, US Supply Chain
• Dr. Reddy’s Laboratories: Rahul Mittal, Head of strategy and innovations
• Katzscan Consutling: Norman Katz, president and CEO
• University of Tennessee: Dan Pellathy, Director of Corporate Programming in Supply Chain Management, University of Tennessee

In addition to keynote presentations, attendees will participate in interactive small-group breakout sessions designed to foster candid discussion around implementation challenges, lessons learned, and measurable business outcomes.

Read more: NextGen Supply Chain Conference returns to Nashville in 2026 with focus on innovation, talent, and transformation

Additional speakers and agenda details will be announced in the coming months.

Registration for the 2026 NextGen Supply Chain Conference is now open. For more information on registration, speaking opportunities, sponsorships, and the conference agenda, visit www.nextgensupplychainconference.com.

Executive takeaways

Competitive advantage in supply chain may no longer come only from buying better software. It may come from leaders who can prototype the software and decision systems they wish they had.

From business requirements to working prototypes

There was a time when knowing Excel was a differentiator for supply chain professionals. Today, pivot tables, visualizations, Solver, and macros are standard business school curriculum. Excel is simply a tool of the trade and background infrastructure in supply chain. Few people understand the internal calculation engine that executes when a user types “=SUM(A1:A4)” in cell A5 and presses return. In fact, they do not need to. Excel became powerful because it let business users create useful logic without becoming software engineers.

Coding agents may do something similar for software and decision system prototypes.

Every enterprise system hides a “post-return chain of events”: the business logic, integrations, calculations, approvals and workflows triggered when a user clicks, enters, submits or approves something. In supply chain, those hidden chains process purchase and sales orders, forecast demand, plan replenishment, manage inventory levels, optimize routes, allocate slotting capacity, and design distribution networks. Users of ERP, CRM, TMS, or WMS usually do not see those chains of events. They simply get a hopefully correct output.

In most organizations, however, that invisible chain represents years of engineering effort, integration decisions, and embedded business logic. Historically, changing it required formal requirements, cross-functional alignment, development resources, validation, and time.

AI coding agents such as Anthropic’s Claude Code or OpenAI’s Codex alter this structure. They reduce the cost of turning domain expertise into working software prototypes. Someone who could not code “Hello World” can now produce a working prototype persuasive enough to test, critique, and hand to technical teams.

This is the opportunity and the risk. Coding agents allow business users to draft their own “post-return chain of events.” This is a major shift because the hidden logic of operations can now begin as a managerial draft rather than an IT request. But those users may not understand testing standards, software architecture, cybersecurity, integration requirements, or long-term maintainability. They may know exactly what the business needs, while still not knowing what the software requires.

And that’s okay. Not every supply chain leader needs to become a software engineer. The more important question is: What is the value of domain experts being able to prototype the systems they wish they had?

Consider a seasoned operations and planning executive who wants a dashboard to monitor inbound purchase orders, flag late shipments, explain the inventory impact, and recommend whether the team should expedite, substitute, reallocate, or wait. Traditionally, building such a dashboard could take weeks or months. The process might include multiple meetings to align expectations, approve design mockups, translate business needs into technical specifications, and iterate toward a deliverable. Meanwhile, the executive does not want another meeting. They just want to see whether the idea works.

Coding agents create another option: the executive can prototype it.

Why domain expertise matters

This creates a useful paradox. A supply chain executive may not understand the technical details of the system, let alone best design practices, internal software policies, or cybersecurity requirements. But they do understand the business problem, the operational tradeoffs, the decision context, and the workflow. Coding agents make it possible to convert that knowledge into a Minimum Viable Product (MVP) grounded in domain expertise.

The workflow might look like this. First, the executive instructs an AI assistant: “Interview me as if you are a software developer helping me build this solution, and produce a Product Requirements Document (PRD).” The assistant asks structured questions about functional requirements, data inputs, user interface expectations, performance constraints, and users. The executive then gives the resulting PRD to a coding agent such as Claude Code or Codex in a sandbox environment using synthetic data, approved samples or properly governed internal data. The coding agent writes the code, asks clarifying questions along the way, and produces an MVP that reflects the executive’s intent.

The difference between MVPs and enterprise systems

Coding agents do not eliminate engineering work. They change where the engineering work begins.

An MVP produced in this manner is not a production-ready system. Code generated by an AI agent must be verified, audited, secured, and aligned with enterprise architecture standards, integration protocols, and internal policies. The prototype may have business validity before it has technical validity. Even if the coding agent has access to the organization’s technical documentation, professional review is non-negotiable. Developers and engineers remain essential, but their role shifts. Instead of translating abstract requests into possible systems, they can begin with concrete prototypes and focus on hardening, integration, scalability, and deployment.

Building a culture of experimentation

There is an additional important implication: the premium on rigorous experimentation increases.

When the cost of building prototypes drops, the temptation is to deploy quickly. This is a mistake and precisely where leaders should slow down the process. A prototype that recommends expediting freight should be shadow-tested before it changes carrier spend. A replenishment rule should be simulated against demand volatility, service levels, and stockout risk before it touches inventory policy. A routing heuristic drafted with an AI agent should be compared against existing planning logic before it changes delivery commitments. These tools should be treated as hypotheses, not complete and validated systems.

Leaders should define performance metrics in advance, compare outcomes against existing baselines, and conduct controlled pilots where possible. Shadow testing new logic alongside current processes, simulating disruption scenarios and documenting where new prototypes fail becomes even more important. Agentic coding accelerates idea generation, but managerial discipline determines which ideas should scale.

Taken together, coding agents shift the boundary between domain expert and software creation. System design does not have to begin only with a formal request to IT. It can begin with a domain expert drafting, testing, and refining a working prototype. For some organizations, that will feel unsettling. For others, it will be empowering.

The strategic questions may not change: What should we buy, make, move, store, allocate, or promise? What changes is how quickly leaders can turn those questions into working systems that can be tested. To leverage these new capabilities, supply chain executives must move beyond describing ideal systems and begin drafting them.

Supply chain leaders can start today with three actions. Identify one recurring decision that is managed through spreadsheets, email or manual judgement. Convert that decision into a plain language requirements document. Then build and test a sandbox prototype against historical cases before asking IT to industrialize it.

The next generation of supply chain leaders will not be distinguished by their ability to bypass technical teams. They will be distinguished by their ability to turn operational judgment into prototypes, test those prototypes rigorously, and refine the best ideas with technical teams.

Competitive advantage will belong to those who can prototype, test, and industrialize responsibly.

Executive takeaways

As global networks face compounding shocks, from climate volatility to infrastructure congestion, the bottleneck is no longer analytical capability. It is the Information Gap. Most supply chains operate in a 7-to-21-day “blind spot” between an event occurring on the ground and that event manifesting as a data point in a terrestrial ERP system.

Recent disruptions in the Middle East have made this gap impossible to ignore. As tensions escalated around key maritime chokepoints like the Strait of Hormuz, shipping flows slowed dramatically, forcing carriers to reroute around the Cape of Good Hope. This added 10 to 14 days to transit times and drove double-digit increases in freight costs.

The physical signals, vessel deviations and congestion buildup, were visible via orbital sensing days before they appeared in enterprise systems. By the time most companies realized there was a disruption, the cheapest decisions were already gone. The cost curve had moved against them.

This is the economic penalty of delayed visibility, and it is exactly what Lead-Time Compression is designed to solve. In my previous research (Doshi, SCMR 2025), we established that while digital twin models optimize the speed of response, this evolution extends their value by improving when disruptions are first detected. By integrating orbital data directly into digital twin architectures, a concept explored by researchers like Dmitry Ivanov (2021) as a means to achieve true "viability," resilience is redefined by the ability to shift decision rights before a disruption enters your network.

The 14-day advantage: Anchoring the data

This approach is not starting from zero. In a series of 2024 stress-test simulations conducted in collaboration with logistics operators across the electronics and automotive sectors, the integration of Earth-observation (EO) data and AIS-based tracking delivered measurable shifts in performance:

• Early warning: As noted in industry reports by Spire Global (2024) regarding satellite AIS impacts, space-derived signals preceded conventional logistics alerts by 10 to 14 days for port congestion and up to three weeks for climate-driven disruptions. In practice, this is the difference between rerouting a shipment while options are still open and paying a premium to fix the problem after the bottleneck has already materialized.

• Recovery speed: Augmenting digital twins with satellite variables reduced Mean Time to Recovery (MTTR) by 22% to 35%.
• Financial protection: Under high-volatility scenarios, Conditional Value-at-Risk (CVaR) exposure was lowered by 15% to 28%. In practice, this allowed a U.S.-based industrial manufacturer to reduce its emergency air-freight spend by identifying maritime bottlenecks three weeks before they peaked.

The executive checklist: Real-world applications

1. Dynamic inventory positioning. During the 2024 Panama Canal drought, firms using orbital imagery of water levels and vessel queues were able to preemptively shift inventory to Gulf Coast ports. This transformed safety stock from a static “just-in-case” cost, as traditionally managed in frameworks popularized by David Simchi-Levi (HBR 2014), into a dynamic, risk-adjusted asset.
2. Tactical network design. Instead of reacting to climate disasters, managers are using long-term orbital imagery to model infrastructure degradation. In representative scenarios based on observed patterns in Mexican manufacturing hubs, satellite data identifying early-stage soil saturation allowed logistics leads to move finished goods to higher ground 48 hours before local flooding shut down highway networks.
3. The governance gap. Why space law matters. As we become space-dependent, the supply chain is no longer just terrestrial; it is orbital. Current frameworks, primarily the Outer Space Treaty of 1967 (governed by the UN Office for Outer Space Affairs), were not designed for the commercial data-dependency we see today. Supply chain leaders must account for Data Sovereignty Risk: the possibility that a nation-state could restrict satellite imagery over a conflict zone, creating a “data blackout.”

To mitigate this, executives should start by auditing which satellite constellations their data providers rely on and whether those assets are subject to single-jurisdiction licensing (such as NOAA in the U.S. or ESA in Europe). If your visibility depends on a single constellation, your resilience is an illusion.

Implementation: The 12-week pilot

The barrier to entry is lower than it appears. Companies do not need to launch hardware. The data is already available via APIs from providers like Spire, Planet, and BlackSky.

In most cases, this does not require new infrastructure, only new data inputs and decision rules. Most organizations can start by layering satellite-derived signals into existing weekly planning cycles rather than overhauling their entire technology stack. This can typically begin delivering ROI in under 12 weeks by focusing on one high-value corridor.

Conclusion: The Monday morning reality

The transition from terrestrial to orbital intelligence is not an IT upgrade; it is a strategic necessity. For the supply chain executive, the mandate on Monday morning is simple: Audit your latency. If your disruption alerts are coming from your carriers, you are already too late.

The companies that win the next decade won’t be the ones that respond fastest, but the ones that see first and act before the disruption becomes visible to the rest of the market. Competitive advantage has moved from who has the best model to who has the earliest signal.

About the authors

Akshat Doshi is a supply chain professional and researcher (M.S. Supply Chain Analytics, Rutgers). His work focuses on AI and digital twins for global supply chain resilience. Rijuka Jain is a UK-based space law professional, specializing in satellite governance and the regulatory foundations of the space economy.

Executive takeaways

In the high-stakes theater of 2026 global trade, the supply chain has transitioned from a back-office logistics function into a front-line financial weapon. As geopolitical volatility stretches transit times and interest rates remain stubbornly high, the traditional “ship it and forget it” mentality is a recipe for bankruptcy. Today, the most successful chief supply chain officers (CSCOs) aren’t just looking at carrier schedules; they are solving a massive, multi-variable math problem centered on one thing: capital velocity.

A SCM framework that generated a 4.2x increase in operational profit suggests that the industry has been looking at efficiency through the wrong lens. If you’re still focusing on the cost per container, you’re already losing the war. You should be focusing on the cost of the dollar.

Part I: The 91-day liquidity trap and the financial pivot

The old way of doing business in industrial imports is built on a fundamental structural flaw: 100% prepayment before production. In any other sector, this would be called a subprime loan. In logistics, it’s been called standard procedure.

While critics might point to the favorable “stock-and-ship” terms seen in retail or generic commodities, the high-spec industrial sector operates under a harsher mathematical reality. Because production is strictly project-based and units are built-to-order, factories cannot hold inventory for a fluctuating pipeline; the assembly line doesn’t move until the wire clears. In this specialized niche, 100% prepayment before the first bolt is turned remains the immovable industry standard, making the “prepayment gap” an inherent structural bottleneck that only advanced financial engineering can solve.

When an importer pays 100% upfront for a 45-day production cycle, followed by a 46-day transit and clearance window, they are essentially handing the manufacturer an interest-free loan for a full quarter. This is the 91-day paradox. While the goods are sitting on a ship or stuck in a production queue, that capital is effectively dead. It’s not just waiting; it’s eroding your EBITDA.

Financial Drag =V ×W ACC× Daysexposure365=

=100 000 ×0,15× 91365 ≈USD 3,740

If you’re moving 100 containers a year, that’s $374,000 in pure air—money that could have been reinvested into R&D or market expansion.

The Sharapova framework I created shatters this cycle by shifting the financial “point of gravity” to Day 59—the precise moment the vessel hits the destination port. By decoupling production from the initial cash outlay, the firm slashes its capital exposure from 91 days to just 32 days.

This tactical shift fundamentally re-engineers the days payable outstanding (DPO). By the time the invoice is due, the goods are nearly on the warehouse floor, ready to be converted back into cash. This is the essence of financial arbitrage in SCM: using the supplier’s production timeline to finance your own growth.

Part II: The physics of margin—Why “heavy load” is the ultimate yield strategy

While the finance team is busy optimizing the DPO, the operational team needs to address the physical “empty space” in the supply chain. In the industrial sector—think batteries, metals, and heavy machinery—the industry is paralyzed by a psychological barrier: the fear of the overweight surcharge.

Most shippers cap their 20-foot general purpose containers at 21 tons to avoid local road penalties. This is a classic example of penny-wise, pound-foolish. By refusing to pay a $5,000 or $100 local surcharge, companies are leaving six figures of ocean freight efficiency on the table.

The math for a 50-ton industrial battery shipment tells a violent truth about modern margins. Let’s look at the landed cost variance between a safe approach and the heavy load model:

The math of density: A comparative logic flow

To visualize the systemic savings of the heavy load model, we must look at the total landed cost variance for a 50-ton industrial shipment.

The conventional model (low-density, high-cost)

Configuration: 3 x 20-ft. GP containers (capped at 16.6 tons each)

Logistical friction: $6,000 in total freight + $90 in triple-set administrative fees.

Result: $121.80 per ton.

The verdict: You pay for the safety of standard loading with a massive drain on margin.

The Sharapova framework (high-density, high-yield)

Configuration: 2 x 20-ft. GP containers (pushed to 25 tons each)

Tactical surcharge: $100 overweight fee for road transport.

Logistical efficiency: $4,000 in base freight + $60 in reduced administrative fees.

Result: $83.20 per ton.

The verdict: a calculated $100 penalty unlocks a $1,930 efficiency dividend.

The efficiency delta: By shifting the focus from container count to mass-to-volume ratio, the framework achieves a 31.7% reduction in total landed cost.

∆Cost=Conventional Model- Sharapova FrameworkConventional Model =

= 6,090-4,1606,090 ≈31.7% Savings

By pushing container density to the absolute limit—25 tons per 20-ft. GP—the framework achieves a reduction in landed costs that no carrier negotiation could ever match. Furthermore, by reducing the total number of equipment units by 33%, you aren’t just saving on freight; you are reducing the attack surface for administrative friction. Two containers mean fewer sets of documents, fewer chances for customs delays, and a significant reduction in the risk of demurrage and detention charges.

This is the CSCO’s new mandate for 2026: Stop playing defense with your logistics budget. Start using the laws of physics and the principles of corporate finance to engineer a supply chain that doesn’t just deliver products, but delivers massive, compounding liquidity.

Part III: Algorithmizing the chaos – The 50/50 hybrid model for category

If Part I and II were about the when and how of moving goods, Part III is about the what. Specifically, the nightmare of every supply chain planner: Category Z inventory. We’re talking about SKUs with a demand volatility exceeding 80% to as much as 100%. In a traditional just-in-time environment, Category Z is where margins go to die, buried under the weight of either massive stockouts or dead capital anchored in dusty warehouse corners.

The conventional wisdom says you can’t forecast chaos. The framework we analyzed rejects that premise, replacing guesswork with a 50/50 hybrid allocation model designed to kill the Bullwhip Effect at its source.

The self-financing loop

The genius of this model lies in its synergy with the Day 59 payment pivot. By splitting a shipment into two distinct risk profiles, the CSCO creates a self-liquidating asset:

• The “back-to-back” engine (50%). These are goods pre-allocated to firm, confirmed orders. In our 2026 model, these units convert back to cash within 10 days of warehouse arrival (Day 101).
• The “predictive buffer” (50%). Strategic stock based on aggressive historical surge analysis. This is your high-risk, high-reward play.

Here is the liquidity breakeven formula that makes the CFO smile:

If M is the gross margin and Q is the total shipment value, the cash generated by the safe half of the container must satisfy:

0.5 ×Q ×1+M≥Qpayable

Because the payment to the supplier was deferred to Day 59, the cash inflow from the first 50% (arriving at Day 101) arrives just in time to cover the accounts payable for the entire shipment. You are effectively using your customers’ deposits to pay for your warehouse buffer. This isn’t just inventory management; it’s inventory-backed financing without the bank fees.

Part IV: The global pivot—SCM as the ultimate competitive moat

As we navigate the geopolitical minefield of 2026, the global supply chain has reached a tipping point. Military conflicts in key shipping lanes and the redrawing of trade maps have made the cheap and slow model a liability. In this environment, resilience is no longer a buzzword; it is a function of liquidity and density.

From reactive logistics to financial dominance

The industry contribution of this framework represents a fundamental shift in the CSCO’s mandate. We are seeing the death of “reactive logistics” where you respond to a crisis by paying for air freight, and the birth of proactive financial SCM.

• Inversion of responsibility. By securing Day 59 terms, the importer successfully pushes the risk of transit delays and production hiccups back onto the supplier. In a 2026 world where a canal closure can add 20 days to a voyage, having the supplier finance that delay is the difference between a profitable quarter and a massive write-down.
• The strategic moat. When you reduce your unit logistics costs by 31.7% via heavy load density, you aren’t just saving money, you are creating a price ceiling that your competitors cannot touch. They are stuck fighting over pennies in freight negotiations while you have re-engineered the very physics of your cargo.

Final thought: The velocity of the dollar

The 4.2x profit growth identified in this case study serves as a masterclass for the modern executive. It proves that the most powerful tool in your supply chain isn’t a faster ship or a bigger warehouse; it’s a calculator.

By solving for capital velocity—minimizing the days capital is frozen (DIO + DSO – DPO) and maximizing the mass-to-volume ratio—the supply chain function stops being a cost center and starts being the most aggressive profit generator on the balance sheet.

In the 2026 landscape, the winners won’t be the ones with the most inventory; they’ll be the ones whose dollars move faster than the ships carrying their products.

Catherine Sharapova, FCILT, is a supply chain manager with the Prometheus Group, an enterprise asset management solutions provider. She can be reached via LinkedIn or email at catherine.charapova@gmail.com.

Exeutive takeaways

In boxing, there is an old adage claiming to explain almost every knockout: You don’t lose to the punch; you lose because your feet were in the wrong place to absorb it. Looking back at a chaotic spring and summer of 2025, small and medium-sized enterprises (SMEs) took an unprecedented barrage of punches. In a matter of months, the U.S. administration released over 70 announcements proclaiming, imposing, pausing, or changing tariffs. For supply chain managers, the rules of global trade weren't just shifting; they were being rewritten one “Truth” at a time. If tariffs are no longer dominating the conversation, it’s not because they have disappeared. It is because brighter, bigger supply chain fires consume all the oxygen in the room, and uncertainty only continues to grow.

When Fortune 500 heavyweights face a regulatory or market shock, they can raise their gloves in high guard and mostly absorb the blow. With market leverage, they can stand their ground, strong-arming their suppliers into suffering the new costs. SMEs, however, are the light- and welterweights of the global economy. They do not have the mass to stand and block. As one SME leader we interviewed last year bluntly summarized:

“We are a very, very small business; we have no sway. We get the letters: ‘Hey, you’re getting a 15% tariff increase on your bearings.’ We can’t negotiate, we don’t have the clout.”

Muhammad Ali didn't beat George Foreman by out-punching him; he won through superior movement and the Rope-a-Dope. The 2025 SCMR article “Rumble in the Supply Chain: Knocking out the Barriers to True SC Costing” highlights several cues that supply chain managers can take from Ali the Great. Here, we focus on one of them: When you don’t have the clout to dictate terms, survival comes down to your footwork.

The last five years have ushered in an era of “permacrisis,” where volatility has shifted from a rare risk to a structural baseline. Based on over 40 in-depth interviews with SMEs and trade experts throughout 2025, we found that the companies navigating this permanent turbulence effectively did not simply collaborate “more.” They laid the groundwork for collaboration. They could not count on anyone to “conduct” the chaos and lacked the clout to command their networks into alignment. Instead, they engaged in what we call supply chain choreography: the routines, trust, visibility, and shared rhythm that allow independent partners to move together when the rules keep changing. Here is how the most resilient mid-market companies improved their footwork through technical discipline and purposeful transparency.

Technical footwork and the safety net

When the tariffs first hit, the default posture for many SMEs was decision paralysis. Firms were caught flat-footed, freezing as they tried to parse the legal ambiguity of origin rules and the retaliatory risks. But agility requires movement, and movement requires a safety net. The most agile SMEs treated their supply chains the way performance troupes like Cirque du Soleil (see SCMR 2020, “Thriving on the Supply Chain Highwire”) treat a live show: they built environments where their teams can take calculated risks without fear of a fatal fall. Their safety net was not an elaborate supply chain control tower or a full-time compliance team; it was a set of practical routines. Rather than treating customs compliance as a rigid, back-office task, they took the initiative to bring the internal team together with brokers, suppliers, and logistics partners to proactively revisit Harmonized Tariff Schedule (HTS) engineering, recalculate landed costs, and explore tools such as bonded warehouses. Further, they used basic agile scenario planning, usually in Excel, to rehearse responses before disruptions fully landed. While companies we spoke with, like most SMEs, lacked access to fancy simulation tools, full-time data analysts, or HTS consultants, their deliberate groundwork to build trust, establish decision routines, develop shared language, and build partner readiness gave them the flexibility to adapt the footwork, and enabled them to make the most of the data they could get a handle on, which took them most of the way.

This agility extended to their pricing. Rather than burying tariff costs into “stealthy” list-price hikes, a move that destroys customer trust, the best performers decoupled their invoices. They separated the base price, tariff surcharge, and freight into distinct line items. By treating the tariff as a highly visible pass-through cost, they proved to their customers they weren’t price-gouging. It was a technical pivot to protect their margins while preserving trust.

Purposeful transparency and the value proposition

For decades, the buzzword in academia and industry has been “collaboration,” but collaboration is an outcome, not a method. You cannot just command two companies to collaborate; you have to create the conditions that make collaboration possible. One key barrier to these conditions is psychological. Many supply chain leaders resist sharing information because once information leaves the firm, it may be misread, misused, or weaponized. Like any relationship, supply chain partnerships require some vulnerability. If neither side is willing to reveal constraints or pressure points, the relationship remains transactional. Yet leaders are right to be cautious. Transparency can feel like losing control, handing over IP, or giving a ruthless negotiator ammunition.

To overcome this, the SMEs that thrived last year practiced what we call “purposeful transparency.” This doesn’t mean leaving the company vault unlocked and the window open. It means making a strategic, calculated choice to share specific operational realities, such as severe margin compression, long manufacturing lead times, or shifting demand forecasts, to jointly address a chokepoint. Crucially, these exemplars didn’t wait until they were desperate to share this information. Instead of approaching partners with a crisis plea, they initiated conversations centered on a shared value proposition. They demonstrated competence by saying, “Here is a mutual challenge, and here is how sharing this data helps us both win.” One mechanical manufacturing CEO captured the profound ROI of this approach:

“We have had two suppliers in Italy and Germany, [who] said, ‘We’re going to cut your price to help out’ because they value the relationship. We treat suppliers like customers, we’re not the ones that go beating on the supplier.”

When you replace the reflexive secrecy with purposeful transparency, you give trusted partners enough context to stop acting like distant vendors or adversaries. In the best cases, partners act as safety nets: drop-shipping to alternative assembly plants, adjusting payment terms to preserve working capital, or helping absorb temporary shocks because they understand the problem and value the relationship. This is the payoff of treating relationships as partnerships rather than transactions.

Setting the stage to finding your rhythm

The post-2025 operational landscape has cemented a hard truth: you cannot orchestrate an ecosystem you lack visibility into and control over. The traditional supply chain model, in which companies seek to flexibly fulfill customers’ requests while “running” their upstream supply chain as rigidly as possible, doesn’t work for SMEs in permacrisis, who have no visibility, leverage, or resources to command partners into alignment. If collaboration is the desired outcome, imposing mandates will only heighten resistance. Forced collaboration is an oxymoron. If coordination aligns activities and collaboration creates joint problem-solving, choreography is the groundwork that makes both possible when no single firm has enough power or visibility to command the system. Supply chain choreography is about creating the awareness, the shared rhythm, and the environmental trust required for independent entities to move together without colliding. It is about laying down the footwork drills in rehearsal so that when a supplier goes bankrupt, a tariff doubles, or a canal runs dry, your network instinctively knows how to pivot. Survival in a permacrisis requires having learned to move together with partners on highly unpredictable stages. It requires purposeful transparency, discarding rigid blueprints, and learning to dance with disruption.

Do these ideas resonate with you? Or do you think they are just academic idealism that would never work in practice? We want to hear your story and take on how to achieve collaboration in the supply chain in practice. Please contact us to share your insights. We will treat your data in complete confidence and use it only in an anonymized, aggregated form. We look forward to hearing your collaboration failure or success story. To submit your story, please out this form.

About the authors

Dr. Sebastian Brockhaus is an assistant professor in the Operations and Supply Chain Management Department and the Graduate Program Director of the Master of Business Administration (MBA) at Cleveland State University’s Monte Ahuja College of Business. He can be reached at s.brockhaus@csuohio.edu.

Alina Marculetiu is an assistant professor of management & marketing at Youngstown State University. She can be reached at amarculetiu@ysu.edu.

Executive takeaways

Supply chain automation is funded like a factory, which is fixed, static, and amortised over a decade. But infrastructure volatility has turned logistics into a trading floor where optionality is the only hedge. This article advances the kinetic balance sheet framework by introducing the Volatility‑Adaptive Automation Portfolio (VAAP). Using a forward‑looking volatility index (VIX‑SC) and a continuous flexibility score (Δ‑auto), CFOs can rebalance automation assets dynamically, just as a hedge fund manages delta exposure. The framework includes automation swaps which are derivative contracts that let companies flex robotic capacity by plus/minus 300% on short notice. Volatility becomes a priced, hedgeable variable, not a budget‑breaker.

Introduction

Imagine walking into a hedge fund’s trading desk and watching the manager commit $12 million to a single, illiquid asset with a 10‑year lock‑up period and zero ability to adjust position as market conditions change. You would call that reckless, perhaps even foolish. Yet that is precisely how most CFOs fund supply chain automation today.

A $12 million automated storage and retrieval system (AS/RS). A fixed network of conveyors bolted to a warehouse floor. A multi‑year robotics‑as‑a‑service contract with no flexibility on volume. Each of these decisions is evaluated using the same tools i.e., discounted cash flow, internal rate of return, net present value (Manzoor, 2026). The models produce neat, reassuring numbers. And then reality intervenes. A new trade corridor opens 50 miles away. A port shifts its primary berth. A regulatory change reroutes half the region’s freight. The infrastructure upon which that gleaming automation depended no longer serves you. Your fixed asset becomes a stranded asset (Manzoor & Malhotra, 2026).

This is not a Black Swan event. It is the ordinary weather of 21st‑century logistics. The GEP Global Supply Chain Volatility Index, produced with S&P Global and covering roughly 27,000 businesses, stood at 0.57 globally in April 2026, firmly in stretched territory with Asia at 1.16, indicating significant capacity strain (GEP, 2026). McKinsey estimates that supply chain disruptions can erode up to 45% of one year’s EBITDA over a decade (GTR, 2025). And yet the financial models used to approve automation projects have not changed in forty years.

What if CFOs approached supply chain automation the way a hedge fund approaches a portfolio? Not as a collection of static assets to be depreciated, but as a dynamic set of positions to be rebalanced continuously against a measurable index of volatility. Not with a single ROI calculation, but with a delta score that changes every quarter. Not with a binary go/no‑go decision, but with a range of option‑like contracts or automation swaps that let you increase or decrease robotic capacity on two weeks’ notice at a pre‑agreed price.

This is not a metaphor. It is a practical framework called the Volatility‑Adaptive Automation Portfolio (VAAP). It rests on three pillars. First, a forward‑looking volatility index for each logistics node, a continuous flexibility score (Δ‑auto) for every automation asset, and a dynamic rebalancing rule that forces the portfolio to become more flexible when volatility rises—the opposite of what most companies instinctively do.

The conversation in the boardroom needs to change. The question is no longer “What is the ROI of this automation project?” It is “How does this investment change our ability to adapt when the infrastructure beneath us shifts?” Until CFOs can answer that question with confidence, the hesitation will remain. This article shows you how.

Why the spreadsheet lies

The problem is not that CFOs are poor financial analysts. The problem is that the analytical tools at their disposal were designed for a world that no longer exists. Traditional capital budgeting assumes a static environment. Discounted cash flow models project steady‑state operations into the indefinite future. Hurdle rates are set at the enterprise level, treating a warehouse in a geopolitically stable region the same as one in a rapidly shifting logistics corridor. And the concept of flexibility does not appear anywhere in the calculation.

This matters because not all automation investments carry the same risk profile. A fully automated facility with fixed conveyors and proprietary software is a very different asset from a fleet of autonomous mobile robots operating on a robotics‑as‑a‑service contract. Traditional finance treats them identically. It ignores the vast difference in their resale value, their re-deployability, and their vulnerability to infrastructure shocks.

Related: The kinetic balance sheet: Why supply chain automation is a CFO’s problem

A growing body of academic research is beginning to address this blind spot. In 2026, recent working papers demonstrate that treating resilience actions as sequential real options enables managers to evaluate investments based on probability‑weighted performance outcomes rather than worst‑case scenarios (Trepte et al. 2025). Critically, the authors found that option‑based sequencing creates probability‑weighted resilience outcomes 3.6 times lower in expected loss than worst‑case analysis. This gap between worst‑case and plausible outcomes, they argue, explains the boom‑bust cycles in which organizations overinvest based upon improbable tail events, then retrench when projected benefits fail to materialize.

This is not merely an academic distinction. It is a practical framework for rethinking how supply chain automation appears on the balance sheet.

Introducing the Volatility‑Adaptive Automation Portfolio (VAAP)

The VAAP framework rests on three operational pillars, each designed to be implemented by a finance team without exotic software or consultants. See Figure 1.

Pillar 1: The Volatility Index (VIX‑SC)

For each major logistics node (warehouse, port gateway, cross‑dock), calculate a forward‑looking volatility score on a 0–100 scale. This VIX‑SC combines three inputs:

• Internal forecast error: The standard deviation of your own demand and lead‑time forecasts over the past 12 months.
• External infrastructure risk: Public data on port congestion, government project delays (scraped from procurement portals), and political risk ratings.
• Market‑implied volatility: The GEP Global Supply Chain Volatility Index for your region.

The result is a single monthly number. A VIX‑SC of 20 signals a calm environment. A score of 80 signals severe strain.

Pillar 2: The Flexibility Score (Δ‑auto)

Every automation asset existing or proposed receives a Δ‑auto score from 0 (fully rigid) to 1 (fully flexible). Table 1 provides a simple, finance‑friendly calculation.

Table 1: Δ‑auto calculation checklist

Δ‑auto = (Number of “Yes” answers) / 5

Source: Author’s analysis based on industry asset classification and real‑options valuation principles.

For a fleet of autonomous mobile robots on a month‑to‑month RaaS contract, Δ‑auto will be 1.0. For a custom‑designed, bolted‑down conveyor system with proprietary software and a 10‑year lease, Δ‑auto will be 0.0.

Pillar 3: The Dynamic Rebalancing Rule

Here is the counterintuitive heart of VAAP. The target portfolio flexibility is set directly by the volatility index:

Target Δ‑auto (portfolio) = VIX‑SC / 100

When volatility is low (VIX‑SC = 20), you target 20% flexible assets and 80% rigid, efficient assets. When volatility is high (VIX‑SC = 80), you target 80% flexible assets and only 20% rigid assets.

Table 2 shows how this works using real April 2026 data from the GEP Index.

Table 2: Target Portfolio Flexibility by Volatility Environment (April 2026 Data)

If your actual portfolio Δ‑auto deviates from the target by more than plus/minus 0.15, you rebalance. This means selling rigid assets on secondary markets, converting fixed leases to flexible terms, or acquiring spot capacity through automation swaps.

Figure 1

Figure 1: The Volatility-Adaptive Automation Portfolio (VAAP): A visual representation of Real Options Valuation in supply chain automation. Contrasting the static capacity of traditional fixed automation against the dynamic, delta-hedged approach of the VAAP framework under demand volatility.

The financial innovation: Automation swaps

An automation swap is a contract between a shipper and a robotics‑as‑a‑service provider. The shipper pays a fixed monthly fee in exchange for the right to increase or decrease robotic capacity by up to 300% with two weeks’ notice, at a pre‑agreed variable rate. This is not a lease. It is a derivative. The provider takes the volume risk, and the shipper pays a premium—the “swap spread” priced—based on the VIX‑SC. For a CFO, this turns supply chain volatility from a budget‑breaker into a traded risk with a transparent market price. You can now hedge your warehouse automation exposure just as you hedge fuel or foreign exchange. See Figure 2. The structural mechanics of a Volatility-Adaptive Automation swap. The shipper secures dynamic capacity by paying a baseline option premium, retaining the right to scale capacity with execution costs tied to a Supply Chain Volatility Index (VIX-SC).

Figure 2

Figure 2: How an Automation Swap Works

Implementing VAAP: A four‑step roadmap for CFOs

The framework does not require a system overhaul. It requires a change in capital allocation discipline.

Why this matters now

Recent surveys highlight a systemic barrier. A 2025 global study of 350 CFOs found that only 15% view supply chain as a priority knowledge area, creating systematic obstacles to technology investment approval. This disconnect is compounded by the fact that 67% of CFOs report current digital investments underperform expectations, making supply chain automation proposals face increased scepticism (MHI, 2025). VAAP directly addresses this credibility gap by providing a common language volatility, delta, optionality that resonates in the finance suite.

The companies that thrive in the coming decade will not necessarily be those with the fastest automation or the lowest unit costs. They will be those whose CFOs have learned to see flexibility as a financial asset, optionality as a hedge, and volatility not as a threat to be managed but as a variable to be priced.

The conversation in the boardroom needs to change. The question is no longer “What is the ROI of this automation project?” It is “How does this investment change our ability to adapt when the infrastructure beneath us shifts?”

Until CFOs can answer that question with confidence, the hesitation will remain and the spreadsheet will continue to lie.

References

Manzoor, R. (2026), The kinetic balance sheet: Why supply chain automation is a CFO’s problem”, Supply Chain Management Review. https://www.scmr.com/article/the-kinetic-balance-sheet-why-supply-chain-automation-is-a-cfos-problem

Manzoor, R. and Malhotra, G. (2026), When the State Rewires Logistics: A Framework for Automation Strategy in Infrastructure-Shifting Environments, California Management Review (Insights), https://cmr.berkeley.edu/assets/documents/pdf/2026-03-when-the-state-rewires-logistics-a-framework-for-automation-strategy-in-infrastructure-shifting-environments.pdf

GEP (2026). GEP Global Supply Chain Volatility Index. https://www.gep.com/knowledge-bank/global-supply-chain-volatility-index

Trepte, K., Klibi, W., Rice, J. B., & Ducq, Y. (2026). Option-Based Framing and Valuation of Supply Chain Resilience Investments (Working Paper, hal‑05498741). https://hal.science/hal-05498741

GTR (2025). Supply Chains: From Just in Time to Just in Case.https://www.gtreview.com/magazine/the-supply-chain-issue-2025/supply-chains-from-just-in-time-to-just-in-case/

MHI (2025). New MHI and Deloitte Report Focuses on Orchestrating End-to-End Digital Supply Chain Solutions. https://www.mhi.org/content/2/2285545/new-mhi-and-deloitte-report-focuses-on-orchestrating-end-to-end-digital-supply-chain-solutions

Executive takeaways

If supply chain leaders are still waiting for stability to return, they may be waiting a long time. “The new normal is chaos and change forever,” said Dan Cicerchi, gm of transportation management solutions business unit of Descartes Systems Group.

In an interview at the recent Gartner Supply Chain/Xpo Symposium in Orlando, Cicerchi said the market is increasingly being shaped by ongoing geopolitical disruption, shifting trade dynamics, and structural changes in transportation capacity, conditions that are forcing companies to rethink how they plan, operate, and invest in technology.

Volatility is reshaping global supply chains

“It creates complexity, urgency, and problems for different participants,” Cicerchi said, noting that these disruptions are forcing companies to make faster, more informed decisions about sourcing, routing, and transportation strategies.

In the U.S., those pressures are compounded by changes in the trucking market. Capacity is tightening, particularly as regulatory and labor dynamics reshape the available driver pool. “We’re certainly seeing truckload capacity leave the market, which ultimately is raising the rates,” Cicerchi said.

While higher rates may create short-term cost challenges for shippers, he noted that they could also stabilize the carrier market.

“Putting carriers in a better spot to actually run ultimately profitable businesses is good for everybody,” he said.

The ripple effects of disruption

Despite the turbulence, not every disruption has a cascading impact across the entire supply chain. For example, while port congestion and pricing can influence routing decisions, trucking spot rates alone are unlikely to drive major shifts in global sourcing strategies, Cicerchi said.

Instead, broader economic and demand trends are playing a larger role in shaping trade flows. One emerging factor is the rapid expansion of data center infrastructure, driven by AI adoption. “The data center buildup has had a dramatic effect on what’s getting imported and what’s getting managed domestically,” he said.

That shift is influencing not just freight volumes, but also the types of goods moving through global supply chains.

AI: Separating hype from value

Like most areas of supply chain technology, artificial intelligence is generating significant attention, and, in some cases, confusion.

“Unfortunately, more people [are] saying, ‘My CEO says we need to do AI. Tell me what you do with AI,’” Cicerchi said.

For Descartes, the approach is to redirect those conversations toward business outcomes rather than technology for its own sake. “Let’s go back to the conversations on business value,” he said. “Really good AI projects start with the outcome and then AI is a technical piece of the solution.”

That perspective reflects a broader industry shift. While AI has long been embedded in optimization tools such as routing, transportation management, and telematics, the latest wave of generative and agent-based AI is being applied to more targeted operational tasks.

One example is Descartes’ use of AI agents within its MacroPoint visibility platform.

“They’re replacing manual activities [such as] … validating that the driver’s arrived and reminding the driver to get proof of delivery,” Cicerchi said.

The impact has been measurable. “We’ve seen a million and a half phone calls [eliminated] already talking to drivers and carriers,” he added.

The key, he emphasized, is focusing AI on high-volume, repeatable tasks where automation can deliver immediate operational benefits.

Planning in an unpredictable world

For supply chain leaders, the challenge isn’t just reacting to disruption, it’s planning for a future that remains highly uncertain. As companies begin developing their 2027 strategies, Cicerchi argued that traditional static planning models are no longer sufficient.

“Having a plan that includes technology to enable you to make the right decisions as the pricing dynamics change almost hourly is important,” he said.  That means building more dynamic supply chain networks, both in terms of logistics partners and geographic sourcing options.

“How do we get more dynamic in our ecosystem of logistics partners and be more agile to go to another region?” he asked.

Recent tariff shifts and trade policy changes have reinforced that need, exposing the risks of overly rigid supply chain strategies. “[It was] a real gut punch to companies that really had more static planning and tools,” he said.

From data to decision-making

At the center of this shift is data, not just access to it, but the ability to operationalize it in real time. “It’s operationalizing the tools into your process so when changes happen, is just different insights from the data that you have,” Cicerchi said.

That requires tighter integration between technology and day-to-day workflows, ensuring that insights translate directly into action.

“Things are changing more now than ever and that’s the new normal,” he said.

Supply chains are no longer operating in cycles of disruption followed by stability. Instead, volatility has become a permanent feature of the landscape. For companies, that reality demands a shift in mindset from optimizing for efficiency under stable conditions to building systems that can continuously adapt.

It also requires a more disciplined approach to technology investment. Cicerchi said to start with the outcomes and find the technology that works.

In a world defined by constant change, the companies that succeed won’t be the ones with the most technology, but the ones that use it to make better decisions, faster.

Executive takeaways

The warehouse automation market is entering a new phase, defined less by rapid adoption and more by reflection. After a surge of investment in the years following COVID-19, many companies are reassessing their approach, driven by a growing realization that automation alone doesn’t guarantee results.

“There were a lot of missing expectations on the performance side of things,” said Alex Haines of Toyota Automated Logistics, during an interview at the recent Gartner Supply Chain/Xpo Symposium in Orlando.

The gap between expectation and execution is now shaping how companies evaluate their next automation investment.

Toyota Automated Logistics (TAL) recently launched, although the company is no stranger to the warehouse automation space. Under the umbrella of Toyota Industries Corporation, TAL is a global partner for integrated warehouse automation that combines the three companies—Bastian Solutions, Vanderlande’s Warehousing business and viastore—under one brand as an integrated automation hub to deliver scalable systems, intelligent software and lifecycle service.

From excitement to skepticism

In the early wave of automation adoption, speed was the priority. Companies rushed to deploy robotics, conveyors, and automated systems to address labor shortages and meet rising e-commerce demand, but those rapid deployments didn’t always hit the mark.

“There’s a lot of conversation where people [were] just burned, frankly, from projects that were not engineered properly or executed well,” Haines said.

As a result, trust has become the defining issue in the market.

“Eighty percent of the conversations I had were like, ‘If we’re going to do something else, how do I make sure I can trust what you’re delivering?’” he said.

That skepticism is slowing decision-making and forcing solution providers to rethink how they engage with customers, moving from selling technology to proving outcomes.

Flexibility becomes the new requirement

At the same time, volatility across supply chains is reshaping how companies think about automation design. With product mixes changing, demand shifting, and network strategies evolving, rigid systems are becoming a liability. Haines said designing for flexibility is now a priority.

“I want to make sure I have flexibility for growth and scale because one day it’s this, the next day it’s this,” Haines said, describing customer concerns.

There is no single solution to that challenge. Instead, companies are taking a more nuanced approach by combining different technologies and design strategies to balance efficiency with adaptability.

That includes:

• Running extensive scenario and sensitivity analyses during design
• Combining rigid and flexible automation systems within the same facility
• Phasing deployments to allow for incremental scaling over time

“It’s planning for five years but building for two,” Haines said.

The rise and reality of digital twins

Digital twins are emerging as a key tool in managing that complexity and more companies are exploring their potential. A digital twin allows a company to simulate and test automation strategies before deployment. But, Haines said companies should investigate thoroughly before jumping into the digital twin universe.

“For 90% of customers, they don’t need that level [of visibility],” he said. “They need the level of confidence of a simulation.”

Full digital twins, particularly those maintained over time, are still largely limited to high-scale, high-risk environments due to cost and complexity. That said, the trajectory is clear.

“The industry’s going full digital twin for most systems going forward at some point,” he said.

Execution is the risk

If there is a consistent lesson emerging from recent automation projects, it is that failure is rarely about the technology itself, Haines said. Instead, breakdowns occur in design, integration, and execution.

“It’s not necessarily that the technology [is] bad, it’s just if you don’t have a very methodical approach, people have had some bad experiences,” Haines said.

The biggest mistake companies make when considering an automation investment is rushing, Haines said. Rather than assuming automation is the answer, companies are being urged to step back and evaluate whether and where it truly makes sense.

“A lot of people just assume they need to do something to automate,” he said.

Automation doesn’t stop at the four walls

Another emerging lesson: automation decisions cannot be made in isolation. Systems that optimize warehouse performance can create bottlenecks elsewhere in the supply chain if not aligned with upstream and downstream operations.

“If you don’t have that [analysis], then what we’re doing upstream doesn’t make any sense,” Haines said.

That is driving greater collaboration across supply chain partners, from network design to transportation to store operations, earlier in the process. Haines said engaging partners early in the process improves the chance of success.

The bottom line

Warehouse automation is no longer in its early-adoption phase. It is entering a period of maturity where success depends less on deploying technology and more on executing it effectively. That shift is forcing both customers and providers to rethink their approach.

For customers, it means slowing down, asking harder questions, and prioritizing flexibility. For providers, it means proving outcomes, building trust, and designing systems that can evolve alongside the business.

Haines said it means resetting expectations to get the most out of your investment.

Executive takeaways

For years, supply chain technology has focused on the single goal of visibility, with companies investing heavily in tracking systems, dashboards, and control towers designed to show where products are, when they will arrive, and what might go wrong. But according to Mahesh Rajasekharan, CEO of Cleo, visibility may not the biggest problem facing organizations.

“What is missing is we lack coordination,” he Supply Chain Management Review during an interview at the recent Gartner Supply Chain/Xpo Symposium in Orlando. “What we [need to] do is the coordination and synchronization across systems, across partners, across the SLAs and how to deliver on it.”

More platform providers are moving to an end-to-end approach, and Cleo is no different.

From data to context

At its core, Cleo’s approach is built on aggregating data from across what Rajasekharan calls the “multi-enterprise supply chain”—customers, suppliers, logistics providers, and internal systems—and transforming it into a real-time operational view.

“We essentially get disparate information across the multi-enterprise supply chain… and we bring it all together,” he said.

But aggregation alone isn’t the differentiator. The key, he argues, is building context. A context layer continuously ingests signals from orders, shipments and inventory and interprets them within the broader flow of an active transaction. Instead of analyzing performance after the fact, the system identifies risk while orders are still in motion.

“You’re in the middle of an active fulfillment … and in the middle of an active cycle, you identify what the risks are,” he explained.

Moving from reactive to proactive execution

That shift from retrospective analysis to real-time intervention is central to true visibility. Today, too many companies operate in what Rajasekharan described as a reactive model where issues surface only after they have already impacted performance.

“Most people live on the concept of deductions and chargebacks that hit them after the fact,” he said, noting that the alternative is a proactive model where supply chain teams can anticipate disruptions and act before they cascade into penalties, missed service levels, or strained customer relationships.

“We’re shifting the world from the reactive supply chain to proactive management where you’re essentially seeing these signals and making sure you’re not violating your performance metrics,” he said.

Why context matters more than connectivity

The industry has made significant progress in connecting systems such as ERPs, WMS, and TMS, but Rajasekharan argued that connectivity without context still leaves companies struggling to act effectively.

“Right now, humans are chasing information across different systems and trying to put it together,” he said. “Almost always they miss things.”

A unified data model offers what Rajesekharan describes as “process choreography”—an understanding of how orders, shipments, and transactions should flow across the supply chain.

“We know an order is an object. We know inventory as an object, and based on signals, we combine deterministic and probabilistic data,” he said. That combination allows the system to not only identify what has happened, but also predict what is likely to happen next and prioritize which issues require immediate attention. The most critical information is funneled to a human for decision-making.

The financial impact of inaction

While the technology conversation often centers on operational efficiency, Rajasekharan put in into financial terms. He pointed to industry estimates suggesting that supply chains lose trillions annually due to inefficiencies in what he called the “informational supply chain.”

“Any typical shipper loses between 2% to 5% in chargebacks and short pay,” he said.

For a $1 billion business, that translates to $20 million to $50 million in lost revenue. “Two to 5% essentially takes your profit margin down a third to a half,” he added.

Beyond direct financial losses, poor execution can also limit growth opportunities, particularly in retail environments where supplier performance directly influences expansion potential.

Where AI fits—and where it doesn’t

In a market saturated with AI-driven solutions, Rajasekharan took a more nuanced stance on the role of artificial intelligence. He said that 95% of the time, the supply chain works just fine, but it’s that 5% where AI offers tremendous value. Cleo is using AI to manage exceptions by identifying disruptions, prioritizing actions, and enabling faster recovery.

At the same time, he cautioned against overusing AI where simpler approaches would suffice.

The next phase: orchestration

Rajasekharan framed Cleo’s broader vision as part of an emerging category: supply chain orchestration. To achieve more autonomous operations, he outlined a layered approach that goes beyond integration.

“You need integration but what is missing is a context layer and an intelligence layer before you put agents on top,” he said.

Without that foundation, he warned, many AI-driven solutions will struggle to handle the complexity of real-world supply chains, particularly the exceptions that drive most operational risk.

“The trick is in the exceptions,” he said.

Supply chains are not short on data. They are not short on AI solutions. But they continue to be short on visibility. That happens when data, AI and execution are coordinated into a context-driven solution.

AI will help organizations get there, but it still needs human guidance.

Executive takeaways

Every supply chain disruption starts the same way. Something changes faster than the operation can respond. Freight arrives early. A carrier cancels. A port backs up. A seasonal spike exceeds every projection. And a warehouse or operations manager who was running a tight operation yesterday is now in reaction mode, making expensive decisions under pressure with limited time.

What happens next determines whether that disruption becomes a manageable event or the beginning of a cycle that is very hard to break.

After years of working with warehouse and operations managers across retail, automotive, manufacturing, and distribution, I have observed that most supply chain problems at the operational level trace back to one of four root causes. I call them pressure points. They are not new phenomena. But the frequency and severity with which they are now occurring has changed the stakes considerably for managers who have not yet built operations designed to flex under pressure.

This framework is not a cure for macro disruption. It is a diagnostic tool. Its value is in helping managers identify which specific failure mode is driving their problem before they apply a solution because the most common and most expensive mistake in supply chain operations is solving the wrong problem with confidence.

Pressure point one: Space pressure

Space pressure is the most visible of the four and the most frequently misdiagnosed. When a warehouse manager runs out of room, the instinct is to frame it as a square-footage problem and pursue a square-footage solution—a new lease, an additional facility, a building expansion. That framing is often wrong.

Space pressure is more accurately described as a flexibility problem. The question is not whether you have enough square footage in aggregate. It is whether you have the right capacity, configured correctly, available at the point in time when you need it. A facility with 80,000 square feet can still experience acute space pressure if its dock capacity, racking configuration, or aisle layout cannot accommodate the specific freight profile arriving in a given week.

The more fundamental issue is structural. Most warehouse infrastructure is designed around average or projected volume. It is sized for what management expects, not for what actually arrives. In an environment where demand swings of 30% to 40% within a single quarter are no longer unusual, infrastructure sized for expectations will be wrong in both directions—over-capacity during slow periods and under-capacity during peaks.

The managers who navigate space pressure most effectively have made a conceptual shift: they treat their fixed infrastructure as a baseline layer sized for their average throughput, and they access flex capacity to cover variance above that baseline. This approach does not require owning or leasing more. It requires building access to capacity that can be added and removed as conditions change.

The operational failure mode to watch for: using permanent, long-term solutions to solve variable, short-term problems. A 10-year lease signed at the top of a demand cycle is one of the most common and most damaging errors in warehouse capital planning.

Pressure point two: Flow pressure

Flow pressure is the subtlest of the four and the one most frequently attributed to something else. When throughput slows and operations start to back up, managers typically diagnose the visible symptoms: the dock is congested, so they add dock staff; the floor is falling behind, so they extend shifts; delivery windows are being missed, so they blame the carrier. The diagnosis often misses the actual problem.

Flow pressure occurs when the movement of product through an operation—inbound receipt, staging, storage, retrieval, and outbound dispatch—loses coherence. The components of the operation are working but they are not working in sequence. Inbound arrives faster than it can be unloaded. Staging areas fill before putaway can keep pace. Outbound builds up because empty capacity is not available at the right dock at the right time.

The diagnostic question that most reliably identifies flow pressure is this: is your team working harder than usual while your throughput is declining? If the answer is yes, you almost certainly have a flow problem. Effort is not the constraint. Movement is.

If your team is working harder than usual while throughput is declining, effort is not the constraint. Movement is.

A Tier 1 automotive supplier facing a production spike illustrates the point. Their existing dock and staging configuration could not keep pace with the volume of inbound parts required to maintain assembly line schedules. Adding labor would not have solved it. The constraint was not the number of people. It was the point at which parts transitioned from inbound transport to production-ready staging. Addressing that specific handoff—the actual bottleneck—resolved the flow problem without a significant labor investment.

The operational failure mode to watch for: applying solutions upstream or downstream of the actual constraint. Adding labor, technology, or capacity at the wrong point in the process is expensive and ineffective. The discipline required is to locate the actual point of constraint before committing resources.

Pressure point three: Cost pressure

Cost pressure in warehouse operations almost always traces back to a structural mismatch between the variability of revenue and the rigidity of the infrastructure that generates it. This is not a P&L management problem. It is an asset strategy problem.

Most warehouse operations are built on a cost structure that made sense at a specific volume level at a specific point in time. Leases were signed. Equipment was purchased. Headcount was hired. Those commitments were rational when volume was predictable. When volume becomes variable—and for most operations it now is—those fixed commitments become a persistent drag in low periods and an inadequate foundation in high ones.

The math is unforgiving in both directions. During slow periods, the operation carries overhead for capacity it cannot fill. During peak periods, the fixed base is insufficient and the cost to access incremental capacity on short notice is high. The average of two bad outcomes is still a bad outcome.

A manufacturing operation's experience with overseas container detention fees illustrates the cost structure problem in concrete terms. When parts shipments became erratic, incoming containers were sitting and accruing detention fees at rates that compounded quickly across multiple containers over multiple weeks. The cost per container per day was more than 25 times the per-day equivalent of the alternative storage cost available to them. The savings from restructuring that one cost element ran into six figures within a matter of months—not from a capital project or a renegotiation, but from identifying a mismatched cost structure and correcting it.

The operational failure mode to watch for: treating cost pressure as a rates problem when it is a structure problem. Renegotiating vendor rates on a fixed-cost model yields incremental savings. Restructuring the ratio of fixed to variable costs in the underlying model yields structural improvement.

Pressure point four: Resilience pressure

Resilience pressure is distinct from the first three in an important way. The first three pressure points represent operational conditions that can be measured, tracked, and addressed proactively. Resilience pressure is a latent condition—it is not visible until the disruption arrives, at which point the organization discovers how prepared it actually was.

The diagnostic question for resilience is straightforward: how many single points of failure does your operation have? A single point of failure is any dependency—a carrier, a facility, a vendor, a system, a key employee—whose failure would move the operation from functional to crisis within 24 hours. Most operations have more of these than their managers realize, because the dependencies were never catalogued. They accumulated over time as the operation optimized for efficiency rather than flexibility.

Efficiency and resilience are in tension. An operation optimized purely for efficiency eliminates redundancy by definition. The most efficient supply chain is a single thread. A single thread breaks.

The contrast case is instructive. Operations that recover from disruptions quickly almost always share one characteristic: they have already tested their contingency options before they needed them. They have used their backup carrier on a routine shipment. They have accessed their flex capacity provider during a moderate volume peak. They have walked through their recovery scenario in a planning session rather than in a live crisis. The difference between paper resilience and operational resilience is practice.

The operational failure mode to watch for: confusing a documented contingency plan with actual resilience. A plan that has never been tested against reality is a hypothesis, not a capability.

Applying the framework

The Pressure Point Framework is most useful as a diagnostic discipline rather than a remediation checklist. The value is not in knowing that these four conditions exist—most experienced supply chain practitioners recognize them. The value is in using them as a structured lens before committing to a solution.

In practice, that means asking four questions before acting on any significant supply chain problem. Is this a space problem, a flow problem, a cost structure problem, or a resilience problem? Which of the four is the primary driver? Are multiple pressure points compounding, and if so, which one is upstream of the others? And is the solution I am considering addressing the root cause or the visible symptom?

The most common pattern I observe is that space pressure and flow pressure are the presenting symptoms, while cost pressure and resilience pressure are the structural conditions that make those symptoms worse. An operation with a well-matched cost structure and genuine contingency options will absorb a space or flow disruption with significantly less damage than an identical operation carrying the structural vulnerabilities of the third and fourth pressure points.

Supply chain operations in the current environment are not going to stop encountering disruption. The macro conditions that generate pressure—demand volatility, transportation constraints, global sourcing complexity—are not temporary. What is within the control of warehouse and operations managers is the structure of the operation they are running and its capacity to absorb pressure without converting it into crisis. That is the work the Pressure Point Framework is designed to support.

About the author

John Brooks is the CEO of Warehouse on Wheels, an on-demand mobile industrial storage and logistics solutions company operating across more than 37 locations in the United States, Canada, and Mexico. Before founding the company, Brooks worked as a warehouse manager and logistics operator. He can be reached via email at jbrooks@warehouseonwheels.com

Executive takeaways

EDI (electronic data interchange) remains a top technology offered by 3PLs (third-party logistics) providers as a service to their customers. For shippers who are retail or grocery vendors, EDI and barcode labeling are the two critical supply chain technologies that are not negotiable in terms of capability and reliability.   

Whether you are on the lookout for a 3PL fulfillment partner, or perhaps your company already has one or more 3PL partners, EDI ability should be at the top of your requirements list. You did your due diligence (or you think you did) when you asked about the EDI capability of the 3PL when you were interviewing them prior to signing the contract. Are you satisfied with the EDI service your 3PL is providing? If not, perhaps you didn’t go far enough with your initial inquiry. Not all 3PLs have the same EDI capabilities.

Is their EDI an embedded part of their warehouse and fulfillment software or integrated to it?  Does your 3PL outsource its EDI, or does it develop EDI software in-house? How dedicated to your 3PL is this outsourced partner, and are they focused on just EDI or are they also maintaining other software such as the warehouse management system? Remember: in supply chain, it’s also about knowing your supplier’s supplier. If your 3PL outsources its EDI, find out from who and research the company. (How far backlogged is the in-house EDI team if there is one?  Where are they located?  And what’s the backlog of the outsourced team too?) There’s nothing wrong with outsourcing software—it’s all the fashion these days—and there’s also everything right with making sure you fully know the expectations and risks before you sign the contract.

Depending upon how you and your 3PL intend on exchanging transactions, you may need your 3PL to send the EDI856 ASN (Advance Ship Notice) to your customers on your behalf. The industry requirement for retail and grocery is that the EDI856 ASN should be sent within one hour of the shipment leaving the facility. The question here is: can the 3PL send the EDI856 and make it look like it came from your company? This “spoofing” (normally a term associated with fraud, I’m just putting it to other use here) of the communication identifiers by a 3PL to make the EDI transaction look like it was sent by its customer and not the 3PL is an EDI capability not all 3PLs have. If this is something that you need, make sure you inquire about it upfront. If your 3PL cannot do this, you’ll need an EDI provider to get involved unless you have your own EDI software and expertise in-house.

Selecting the right 3PL is a significant task. One must examine the 3PL’s locations, operations, and technologies. This requires more than just ticking boxes on a checklist: there has to be deeper considerations for each and every requirement. Sometimes, a technical capability will be associated with an operational functionality and the two must be considered together. EDI isn’t just one of those requirements that can be ticked off as a “Yes” or “No”.

Ultimately, your choice of 3PLs will directly impact your ability to execute.  Your customers don’t want to deal with a disruptive vendor, and they don’t care about your 3PL problems.  To stay competitive, ensure that you’ve partnered with a 3PL you know and can hopefully grow with.        

Executive takeaways

Wholesale inflation is flashing a warning sign for supply chain leaders. U.S. producer prices rose 1.4% in April from the prior month, the largest monthly increase since March 2022. Meanwhile, energy prices are projected to surge at least 24% this year, and prices for base metals including aluminum, copper and tin are also expected to reach all-time highs.

At the same time, demand remains uneven. Some sectors are slowing, while others, especially those tied to AI infrastructure, data centers, electrification and renewable energy, continue to pull hard on materials, power and logistics capacity.

That leaves CSCOs on the front line of a second-half cost challenge. According to Gartner research, 71% of CSCOs are focused on controlling costs in the short term, yet only 21% are confident they can deliver against cost management expectations over that same horizon.

Today, many CSCOs see cost management as a delicate dance of managing relationships with peers and expectations from executive leadership. They do their best to build consensus by being accommodating to preserve goodwill. Those instincts matter. No supply chain leader succeeds by alienating peers. In the current market, however, consensus alone can become a polite route to poor outcomes.

Gartner research points to a more durable model: the assertive advocate CSCO. These leaders collaborate with their peers, while also building process guardrails and demonstrating the P&L impact of different business decisions. Assertive advocates are more than twice as likely as consensus-builders to drive sustained cost excellence.

For CSCOs facing historic cost increases, the objective is clear: sustained cost excellence requires more than influence. It requires challenging unrealistic cost targets, reshaping decisions before costs are locked in, and making trade-offs visible before the business pays for them.

Why consensus breaks down under pressure

The consensus-builder model often fails because it accepts the cost target before the trade-off has been understood. Sixty-one percent of consensus-builder CSCOs accepted aspirational or aggressive CFO-set targets in the past 12 months to be a “team player,” compared with only 21% of assertive advocate CSCOs, according to a Gartner survey.

That difference changes enterprise decisions. Consider an industrial manufacturer that depends on aluminum, copper and energy-intensive components. Input costs are rising just as customers are resisting price increases. Commercial leaders may want to preserve every service promise to protect revenue, while finance pushes for lower working capital and operations is asked to reduce inventory or consolidate shipments. Each request may be reasonable in isolation. Together, they can create stockouts, missed delivery windows or expedited freight costs that erase the intended savings.

An assertive advocate CSCO forces that discussion into the open. If inventory is reduced, which service commitments become harder to meet? If shipments are combined to reduce transportation costs, which delivery windows stretch? The goal is to make these consequences visible before the decision is made.

Cost authority starts with the P&L

A CSCO cannot credibly challenge a cost target with operational anecdotes. They must make this argument in the language the CEO and CFO already use: the P&L. To drive impact, CSCOs need to show their contribution beyond cost avoidance by connecting performance to margin.

For example, a supply chain organization managing ocean freight this year may not be able to claim a simple year-over-year reduction if rates remain elevated by fuel surcharges, route disruption or capacity uncertainty. It can separate finance-recognized savings from avoided costs though.

If, for example, comparable shipping lanes become more expensive because of bunker fuel, insurance or Red Sea-related disruption, but the company’s negotiated rates increase less than the market, the CSCO can show how procurement and network decisions helped protect margins.

Put supply chain expertise where decisions happen

Many cost problems stem from areas outside of the supply chain function’s direct control. A sales team may promise expedited delivery to hold revenue in a softening segment. A product team may approve a design that increases exposure to copper or aluminum at an inopportune point in the commodity cycle. A regional business may commit to a supplier that looks cheaper on unit price, but requires longer lead times, higher safety stock or more expensive transportation. By the time supply chain absorbs the cost, the decision has already hardened into a customer promise, product spec or sourcing commitment.

Assertive advocacy inserts supply chain knowledge directly into these cross-functional decision points. An organization could create a rule for new customer contracts: any delivery model that requires nonstandard fulfillment must be reviewed against transportation and inventory implications before the deal is approved. The value is a guardrail that prevents hidden costs from being priced too late.

The fear among CSCOs is that assertiveness will add burden to teams already stretched thin. Gartner’s research suggests the opposite can happen when guardrails are designed well. Decision tools and scenario models can reduce firefighting by helping teams see trade-offs earlier.

The next phase of cost leadership

As cost pressures build heading into the second half of the year, CSCOs will need to do more than manage costs. They will need to help protect profits before rising input, freight and energy costs erode margins.

The assertive advocate CSCO brings evidence, decision discipline and P&L visibility into the enterprise before costs are locked in. That posture may be the difference between absorbing the next cost shock and preventing it from becoming a margin crisis.

Benjamin and other Gartner analysts are providing further analysis on this topic at the Gartner Supply Chain Symposium/Xpo, taking place this week in Barcelona, Spain, May 18-20. Follow news and updates from the conferences on X using #GartnerSC.

About the author

Benjamin Jury is a director analyst for Gartner’s Supply Chain Practice. He leads and contributes to research projects that address chief supply chain officers’ (CSCOs’) key priorities.