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.

Executive takeaways

Editor's Note: The SCM thesis AI-Powered Warehouses: A New Era of Sustainable Inventory Management was authored by Kyungmin Kook and Elisa Ruiz Mugica and supervised by Dr. Josué Velázquez Martínez (josuevm@mit.edu) and Dr. Miguel Rodríguez García (miguelro@mit.edu). For more information on this research, please contact the thesis supervisor.

Warehouse operations are often overlooked as contributors to greenhouse gas (GHG) emissions in the logistics sector. Our capstone project set out to measure emissions reductions from improved inventory management. Along with our sponsor company Verity—a provider of AI-powered inventory management systems—we partnered with a global logistics provider to assess the environmental impact of implementing Verity’s indoor drone system in a U.S.-based fulfillment warehouse.

Our research explored how drone-based inventory automation impacts total GHG emissions across Scopes 1, 2, and 3 and which operational levers—labor, equipment, or waste—experience the greatest emissions changes due to automation. (Note: Scope 1 refers to emissions directly produced by an organization; Scope 2 refers to indirect emissions resulting from an organization’s energy use; Scope 3 refers to indirect emissions produced throughout an organization’s value chain.)

Constructing the study

To assess the environmental impact of drone-enabled inventory automation, we developed a mathematical model using real operational data, integrating activity-based emissions modeling with lifecycle assessment (LCA) to estimate emissions changes across Scopes 1, 2, and 3. We collected operational data from both pre- and post-deployment periods, including cycle count records, inventory composition, equipment usage, and staffing levels. When direct data was unavailable, we supplemented it with structured interviews, industry benchmarks, and peer-reviewed literature.

Key operational variables included forklift energy use, inventory write-offs, employee commute distances, and drone charging requirements. Each was mapped to a corresponding emissions scope using standardized emissions factors from sources such as the U.S. Environmental Protection Agency and Verity.

Using the model, we evaluated three main levers to understand the drivers of emissions reduction:

• Inventory accuracy improvements: reduction in inventory write-offs due to more frequent and precise cycle counts enabled by autonomous drone scanning
• Labor efficiency gains: decrease in employee commuting emissions from reduced staffing required for inventory tasks
• Equipment utilization changes: reduction in forklift usage and a decrease in the total number of forklifts required, reducing both energy consumption and lifecycle emissions

We also included lifecycle emissions (Scope 3, LCA) associated with the manufacturing and transport of drones and forklifts. To test robustness, we conducted a sensitivity analysis using three drone coverage scenarios:

• Scenario 1: 64% drone coverage (current)
• Scenario 2: 90% drone coverage (target)
• Scenario 3: 100% drone coverage for scannable locations

The benefits of drone automation

We found that drone automation significantly reduced emissions. At 64% drone coverage, emissions decreased by approximately 49.5% compared to the manual baseline. This reduction was driven by reduced inventory write-offs (Scope 3), reduced forklift usage (Scope 3 and LCA), and reduced commuting by staff (Scope 3). Benefits tapered off beyond 90% drone coverage; increasing drone coverage to 90% led to an additional 33% reduction in emissions relative to the 64% baseline, but further increases yielded diminishing returns, indicating that the majority of benefits are realized before full coverage.

Our findings contribute to a growing body of evidence that warehouse automation, when implemented thoughtfully, can serve as a critical enabler of corporate decarbonization strategies.

Executive takeaways

The response to the 2026 NextGen Supply Chain Conference awards program has been strong, and organizers are extending the submission deadline to provide companies additional time to complete their submissions for this year’s revamped recognition program.

The new deadline for award submissions is now June 1, 2026.

Conference organizers are also extending the deadline for speaker submissions to June 1, allowing practitioners and end users additional time to submit proposals focused on real-world supply chain challenges, execution strategies, and measurable business outcomes.

The 2026 NextGen Supply Chain Conference, taking place Oct. 21–23, 2026, at the W Nashville in Nashville, Tennessee, has revamped its awards program this year to place greater emphasis on operational execution, measurable transformation, and real-world deployment of AI, automation, and digital technologies.

“The level of engagement and early interest we’ve seen across both the awards program and conference agenda development has been extremely encouraging,” said Brian Straight, technical director of the event and Editor in Chief of Supply Chain Management Review. “Extending the deadlines ensure organizations that are currently finalizing submissions or coordinating internally still have an opportunity to participate.”

A stronger focus on execution

The updated awards program reflects the changing realities of modern supply chains. AI, automation, and digital transformation are now operational imperatives. The 2026 awards are designed to recognize companies moving beyond pilots and theory into scaled execution and measurable business impact.

As in previous years, the conference will recognize both End User organizations and Solution Providers.

End User awards recognize organizations that have successfully operationalized technology and transformation initiatives in production environments. Solution Provider awards honor firms delivering measurable customer impact through deployed technologies and services.

Both end users and solution providers will be recognized in the following categories:

• Intelligent Transformation Award: Recognizing organizations embedding AI and advanced technologies into day-to-day supply chain operations at scale.
• Autonomous Operations Award: Honoring organizations deploying robotics and automation to drive measurable operational improvements.

Special recognition awards

Additional recognition categories include:

• Startup Award: Spotlighting emerging companies demonstrating innovation, differentiation, and market traction.
• Partnership in Execution Award: New for 2026, recognizing collaborative success between end users and solution providers or consulting partners delivering measurable business outcomes together.

The Partnership in Execution Award reflects a reality that transformation increasingly depends on successful collaboration across organizations, technologies, and operational teams.

All award winners are required to attend the conference and present as part of the conference agenda.

Expanding participation across the conference

In addition to the awards program, the conference agenda continues to take shape with several keynote presentations and featured sessions already confirmed.

Among the announced keynote presentations is the 2026 Visionary Keynote from Tractor Supply. Colin Yankee, chief supply chain officer for Tractor Supply, will accept the Visionary Award and sit for a fireside chat to discuss the company’s approach to redesigning its supply chain.

The Friday morning keynote will be delivered by Dr. Mar G. Gimeno, associate vice president of U.S. Supply Chain and Global Launches for Eli Lily.

Additional practitioner-led sessions and industry speakers among the conference’ four focus areas (logistics and fulfillment, retail, food & beverage, and chemicals/pharmaceuticals) are being added daily. Among the companies confirmed to be speaker are Amazon, Fanatics, Mars, DP World, Penske Logistics, Target, Evonik, and Johnson & Johnson.

The conference continues to prioritize end-user-driven discussions centered on execution, workforce transformation, operational resilience, AI deployment, automation, and supply chain strategy.

The conference is expected to host approximately 250 senior-level supply chain, logistics, procurement, manufacturing, and operations executives.

How to get involved

Organizations interested in participating in the 2026 NextGen Supply Chain Conference can:

• Submit for an award (new deadline: June 1). Click here.
• Apply to speak by proposing a session focused on operational execution and measurable outcomes (new deadline: June 1). Click here.
• Register to attend. Click here.
• Explore sponsorship opportunities to engage directly with a highly targeted executive audience. Click here.

The 2026 conference theme—Innovate. Upskill. Transform.—reflects the continued focus on technology deployment, workforce evolution, and operational execution shaping the future of supply chain management.

Executive takeaways

A phrase often cited in discussions on supply chain strategy and performance is “being in the business of supply.” But what does that really mean?

A supply chain supports the business. If there are no products or services to provide, there is no need for a supply chain. As a result, supply chain design and operating strategy must align with the goals and priorities of the business. It’s typical to cast the role of supply chain as producing and shipping while managing cost and cash. This classic view, however, limits the supply chain’s ability to contribute to stronger overall business results. A supply chain designed and operated in line with business goals can amplify financial performance and outperform competitors where it matters most to the business. Achieving this level of integration requires a culture of being in the business of supply.

Why is the culture of being in the business of supply important? 

In a company that manufactures and distributes product, the supply chain organization, spanning manufacturing, procurement, logistics, planning, customer service, engineering, and quality, is often the function with the largest number of employees, sometimes by a significant percentage. The supply chain organization also typically controls a majority of a company’s spend and costs. For these reasons, it’s very important for the supply chain organization to be synchronized with overall business objectives and plans.

However, systems like annual work plans and measures cascading down through the organization can make it all too easy for large supply chain organizations to focus on siloed functional metrics. For example, imagine a scenario where manufacturing wants high OEE and planning wants low inventory. By being in the business of supply, the supply chain organization’s mindset can shift to ensure that functional metrics are in lockstep with the needs of the business.

Often, this alignment requires trade-offs. For example, inventory control is a typical supply chain metric. But if the business chooses to enter new markets, channels, or categories to spur growth, inventory levels may need to increase tied to anticipation builds and higher uncertainties. In this instance, inventory targets may need to be adjusted to align supply chain focus with the business need. 

Another example is when the business depends on frequent new product introductions to succeed. In that case, the supply chain design and operations capabilities must support rapid change, which may make cost a secondary objective. As a result, the emphasis shifts from a traditional cost-first focus to delivering rapid new product introductions in a cost-effective way.  

Another reason the mindset of being in the business of supply is important is that business strategies are rarely stagnant. Factors such as market and consumer changes, competition, and new inventions can drive changes in business strategy and plans. That being the case, the supply chain needs to stay closely connected to the overall business needs, adapting at the pace of the business. These changes can require new supply chain targets, new suppliers, new capabilities, and a new focus.

It’s often stated that for a business to be consistently successful, the supply chain needs a seat at the table alongside the multifunctional business leadership team. When in this seat, supply chain leaders can listen and process what they hear, translating those discussions into implications for supply chain functional work. The result may be adjustments to supply chain strategies or the creation of multiple strategies as needed to support the business. In practical terms, this effort can include determining which targets may need to be raised or relaxed, whether resource deployment is appropriate, whether new suppliers are needed, and whether capacity is likely to be sufficient. Supply chain leaders then bring this updated business understanding back to their organization to make adjustments as needed, ensuring the supply chain is set up to best support overall business objectives. 

Benefits of the culture of being in the business of supply: Clearly, having supply chain—often the largest function in a company—hardwired to what the business needs to succeed is a competitive advantage. Potential impacts include:

• increased sales revenue and profit
• reduced costs
• improved capability and speed for new product introduction
• increased ability to pivot in sync with business strategy shifts (new markets, channels, categories)
• internal development of both supply chain and business leaders from within the supply chain organization.

Watchouts: Unfortunately, there can be many intended or unintended barriers to developing a culture of being in the business of supply, including:

• Some supply chain leaders may be excellent at operating the supply chain and delivering functional results, but unable to speak and listen in the language of the business. Being “bilingual” (i.e., able to speak both the language of business and that of supply chain) is a critical skill needed to determine whether new targets or operational changes are required.  
• Other leaders are great at engaging with leaders and other contacts, but less effective at maintaining functional expertise. While the business may require increased focus in certain areas or new capabilities, the supply chain is still expected to operate well in its core functional domains (e.g., making, shipping, quality, cost). 
• There is a risk of paralysis if business needs and plans change frequently. The supply chain organization may be reluctant to embrace changes while waiting to see if they’ll stick.
• Misaligned KPIs are also common, especially if the supply chain organization is dispersed across other functions with differing goals. People ultimately respond to their incentives, even local ones. 

How to develop the mindset of being in the business of supply?

Supply chain leaders need to consistently talk about business needs and objectives with their organization. But more than talk, they need to show how those business needs connect directly to functional work and capabilities (and, where no clear connection exists, carefully evaluate whether that work is still required and why). People at all levels of the supply chain organization should be able to see a clear line of sight between overarching business needs and objectives and their individual work plans and measures. This dialogue needs to happen regularly and consistently to instill the culture of being in the business of supply.

About the authors

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

Executive takeaways

The logistics sector has undergone a fundamental transformation since the turn of the decade, primarily driven by the “last mile” challenge: the final, most expensive, and operationally complex segment of the supply chain moving goods from distribution hubs to the consumer’s doorstep. As we reach the mid-point of the 2020s, last-mile delivery (LMD) has evolved from a logistical hurdle into the primary determinant of both operational profitability and customer retention.

Recent research indicates that LMD now accounts for between 41% and 53% of the total cost of shipping. This economic weight is exacerbated by the structural differences between the middle mile and the last mile. While the middle mile benefits from full truckload (FTL) efficiencies and predictable hub-to-hub transit, the last mile is characterized by high fragmentation, small drop sizes, and significant “not-at-home” failures. In urban environments, drivers spend an average of 9 minutes per stop simply searching for parking and may walk nearly 5 miles per day to complete their routes.

Comparative strategies: Amazon vs. Walmart

The U.S. market is currently defined by the intensifying rivalry between Amazon and Walmart, two entities representing polarized origins of retail dominance now converging on an omnichannel equilibrium.

Amazon: The platform and density model

Amazon’s strategy is rooted in economies of density and the vertical integration of logistics as a core product. By 2021, Amazon had achieved a U.S. e-commerce share of approximately 40%, a position it leveraged to incentivize third-party vendors to use its fulfillment and logistics services.

• Infrastructure: Amazon transitioned from centralized to decentralized fulfillment centers to bring inventory closer to high-demand counties.
• Labor: Its Delivery Service Partner (DSP) program facilitates thousands of small, independent delivery businesses.
• Performance: By 2025, predictive modeling allowed Amazon to achieve a 40% cost reduction in its operations, reaching 98% on-time accuracy through the use of Scout 2.0 robots in over 50 cities.

Walmart: The retail-led proximity model

Walmart utilizes its existing physical footprint—specifically its 5,000-point physical network—as a distributed warehouse system that rivals cannot easily replicate.

• Infrastructure: Walmart’s store-as-fulfillment-hub model leverages brick-and-mortar proximity to the consumer.
• Labor: The Spark Driver platform, which reached 84% of U.S. households by 2022, serves as its primary crowdsourced logistics (CSL) engine. Research indicates that nearly three-quarters of Walmart’s delivery orders are fulfilled by these independent contractors.
• Performance: Walmart has narrowed the logistics gap using AI-human hybrids, achieving a 45% increase in delivery speed by 2025.

AI implementation: Two paths to efficiency

As of 2026, artificial intelligence (AI) has moved from a supporting tool to the cornerstone of last-mile operations. The industry has diverged into two primary implementation directions:

1. Dynamic routing and gig-labor management

This direction focuses on managing the two-sided uncertainty of fluctuating customer demand and unpredictable gig-driver availability.

• The Walmart approach: Utilizing stochastic programming and survival regression modeling, Walmart reduced driver idle time by 55%.
• Algorithmic advances: Researchers have successfully deployed an Improved Partheno Genetic Algorithm (IPGA) using a rolling-horizon approach to manage dynamic environments. Numerical experiments show the IPGA reduces total service costs by 10% to 16% compared to traditional methods.

2. Autonomous hardware and robotics

This direction seeks to remove the high cost of human labor from the most congested segments of the delivery chain.

• The Amazon approach: Amazon’s Scout 2.0 sidewalk robots and Prime Air" drones represent the push toward autonomous delivery.
• Performance impact: Sidewalk robots navigating with enhanced AI vision have reduced the cost of urban delivery by 35% compared to traditional van-based methods.

Behavioral economics: The smarter last mile

A critical shift in 2023 research was the move from purely operational solutions to behavioral interventions. Logistics leaders are now using social sustainability nudges to shift consumer behavior toward less expensive channels, such as store pickup.

Studies demonstrate that sustainability-oriented information labels—highlighting neighborhood traffic, noise, and road safety—are far more effective than monetary discounts. A combined labeling approach has been shown to result in a greater than 40% shift from home delivery to store pickup, while simultaneously increasing customer satisfaction.

Future opportunities and the reverse last mile

As the distinction between warehousing and delivery continues to blur, several emerging opportunities define the future of the supply chain:

• In-home logistics: Walmart’s InHome 2.0 and Amazon’s Key services allow AI-powered access to kitchens or garages. Interestingly, research suggests that marketing in-home returns is the most effective gateway to building the trust required for in-home delivery.
• Hyperlocal fulfillment centers (FMCs): These neighborhood pods are projected to grow at a 31% CAGR, reaching $31.6 billion by 2030.
• Underground freight networks: Exploration of urban freight tunnels using autonomous pods offers a potential solution to bypass surface-level urban congestion.
• Quantum logistics: Post-2025, the integration of quantum computing is expected to solve real-time route optimization for complex, multi-modal chains involving drones, robots, and vans.

Conclusion

The U.S. last-mile landscape is no longer a competition between a website and a store, but a competition between two highly automated, AI-driven logistics networks that sell products as a secondary function. Success in this final frontier will be determined by which firms can best manage labor and demand uncertainty while navigating increasing social demands for safety, equity, and sustainability.

This article was researched and written with the assistance of generative AI.

About the author

Walter Salek is an assistant professor of business and economics and the program director of the Supply Chain Master’s Program at Elmhurst University. He leads a holistic, AI-embedded supply chain master’s program and more information can be found at: Supply Chain Management Master's Degree | Elmhurst University.

Supply chain complexity is a reality every business faces — but it doesn't have to be a barrier to growth.

In this webinar, Amazon's Mike Schaffer, Principal Tech BD on the Multichannel Commerce & Fulfillment team, introduces Amazon Supply Chain Services (ASCS): an end-to-end logistics solution built on the same network and technology powering Amazon's own operations, now available to all businesses and channels.

Attendees will get a clear picture of how ASCS transportation, fulfillment, and delivery services work together — using AI and automation to help businesses reduce complexity and scale with confidence, plus a real-world look at how one brand doubled YoY revenue without adding headcount and a first look at the new console that enables businesses to manage their ASCS account.  

What you'll learn:

• The story behind ASCS and how it works
• The technology that sets it apart
• How brands are leveraging ASCS to grow without added overhead
• How to get started with the ASCS console

Executive takeaways

For many organizations, strategy is not the issue. Leadership teams dedicate significant time and energy to defining ambitious, multi-year plans designed to drive growth, resilience, and competitive advantage. Yet despite this effort, a familiar pattern emerges: as strategy moves from concept to execution, alignment begins to erode.

What starts as a unified direction at the top often devolves into a series of functional priorities, each optimized within its own domain but disconnected from the broader enterprise. The result is a gap between strategic intent and operational reality—a gap that continues to challenge even the most sophisticated organizations.

 

At the heart of this issue lies the need for true C-suite synchronization.

The drift from strategy to execution

Misalignment among senior leaders rarely stems from a lack of commitment. More often, it is the result of how strategy is interpreted across functions such as finance, supply chain, operations, and technology. Each function brings its own priorities, metrics, and incentives, shaping how strategic objectives are translated into action.

In many cases, strategy is distilled into financial targets: growth, cost savings, capital allocation, and workforce investments. While these metrics are essential, they often fail to capture the full scope of what strategy requires operationally. Long-term ambitions are compressed into annual operating plans, where short-term pressures take precedence.

This dynamic creates tension. Leaders are incentivized to deliver immediate results, often at the expense of longer-term strategic initiatives. If current-year targets are not met, future opportunities may never materialize. As a result, strategy is gradually diluted, with decisions made in isolation rather than in alignment with enterprise-wide objectives.

Crucially, the path to delivering these outcomes is frequently underdefined. Without explicit alignment on how strategy will be executed, organizations risk pursuing fragmented efforts that fail to achieve the intended impact.

The consequences of limited visibility

As misalignment grows, leadership teams are often pulled into increasingly tactical decision-making. When performance falls short or confidence in the data is low, executives tend to focus on immediate operational issues, attempting to diagnose and resolve problems within individual functions.

This shift toward tactical engagement can create significant organizational churn. While it may address short-term concerns, it diverts attention from the broader strategic trajectory. The ability to “look to the horizon” becomes constrained, and the organization risks losing sight of its long-term direction.

A key contributor to this dynamic is the lack of clear, consistent information. Although most organizations have access to vast amounts of data, it is often fragmented, inconsistent, or presented without sufficient context. Multiple versions of the truth can coexist, making it difficult for leaders to build confidence in the insights they receive.

Without reliable, well-framed information, decision-making becomes reactive. Leaders may struggle to ask the right questions, and cross-functional alignment becomes even more difficult to achieve.

Breaking down structural and cultural barriers

Structural silos are a well-documented challenge, but cultural factors can be even more difficult to address. In many organizations, there is an inherent reluctance to surface negative information early. Teams may defer difficult conversations in the hope that performance will improve over time, often with the expectation that results can be recovered later in the planning cycle.

This tendency delays critical decisions and limits the organization’s ability to respond proactively. By the time issues are fully visible, the range of viable options may be significantly reduced.

High-performing organizations take a different approach. They foster a culture where transparency is expected and encouraged, and where both positive and negative developments are addressed promptly. Early visibility enables more informed decision-making and reduces the risk of compounded problems.

Achieving this cultural shift requires more than intent. Incentive structures, performance evaluations, and organizational design often reinforce functional priorities over enterprise outcomes. Moving from functional optimization to enterprise-wide performance demands deliberate changes in how success is defined and rewarded.

It also requires patience. Sustainable alignment is built over time, through consistent behaviors and disciplined processes rather than one-time interventions.

Defining visible leadership engagement

“Visible leadership engagement” is frequently cited as a critical success factor, yet it is often poorly defined. In practice, it extends far beyond increased communication or executive presence.

In synchronized organizations, leadership alignment is evident in decision-making, resource allocation, and the consistent reinforcement of priorities. Leaders demonstrate a shared understanding of both the strategic objectives and the trade-offs required to achieve them. Competing priorities are addressed explicitly, rather than left unresolved across functions.

This clarity has a cascading effect throughout the organization. Frontline teams and middle management gain a clear line of sight between their day-to-day activities and the company’s strategic goals. Conflicting signals are reduced, and accountability becomes more meaningful.

Visible engagement, therefore, is not about visibility alone; it is about coherence. It reflects an organization where leadership actions consistently support enterprise-wide objectives.

The role of integrated business planning

Integrated business planning (IBP) plays a central role in enabling C-suite synchronization. When implemented effectively, IBP establishes a continuous link between strategic intent and operational execution.

Through a structured and repeatable cadence, IBP provides a forum for aligning assumptions, reviewing performance, and making informed decisions. It enables organizations to develop a shared understanding of what is happening across the business and to assess whether current operations are delivering against strategic goals.

Importantly, IBP does not require perfect data, an unrealistic expectation in most environments. Instead, it relies on agreement around the most critical information needed to support decision-making. By focusing on “right enough” data, organizations can move forward with confidence rather than waiting for complete accuracy.

Technology can support this process, but it is not a substitute for it. Many tools remain functionally oriented, limiting their ability to provide true enterprise visibility. The effectiveness of IBP ultimately depends on the alignment of processes, data, and leadership behaviors.

When these elements come together, IBP becomes a powerful mechanism for shifting organizations from reactive to proactive management.

Measuring alignment through outcomes

The effectiveness of C-suite synchronization is best measured through business performance. Organizations that achieve strong alignment typically demonstrate improved top-line growth, enhanced margins, and greater consistency in meeting commitments.

Equally important, they experience reduced internal friction. Decision-making becomes faster and more transparent, with clearer trade-offs and fewer conflicting priorities. Execution becomes more predictable, enabling the organization to respond more effectively to both opportunities and challenges.

These outcomes reflect not only better processes, but also stronger leadership cohesion.

Navigating uncertainty with confidence

In an environment defined by geopolitical volatility, policy shifts, and market disruption, the importance of C-suite synchronization is amplified. Organizations are under constant pressure to respond to external events, often with incomplete information.

A common pitfall is overreacting to short-term uncertainty. Early signals can be disproportionately influential, leading to decisions that may not align with longer-term realities.

Synchronized leadership teams are better equipped to navigate this complexity. They maintain a balanced perspective, addressing immediate challenges while keeping strategic objectives in focus. By grounding decisions in aligned processes and shared data, they can respond with greater confidence and consistency.

Advances in technology are also creating opportunities to accelerate planning cycles and improve the speed of decision-making. However, the ability to act quickly remains dependent on alignment. Without it, increased speed can simply amplify existing inefficiencies.

From process discipline to strategic advantage

Ultimately, while results are the primary measure of success, process discipline is what enables those results to be achieved consistently.

Organizations that excel at strategy execution recognize that alignment is not a one-time initiative. It is an ongoing capability, built through structured processes, clear governance, and sustained leadership commitment.

C-suite synchronization does not eliminate functional expertise; it connects it. By aligning perspectives, priorities, and actions, organizations can ensure that strategy is not only defined effectively but also executed with precision.

In doing so, they transform strategy from an annual exercise into a continuous, enterprise-wide reality that drives measurable and sustainable performance.

About the author

Andrea Montecchi, Chairman of Oliver Wight Americas and Chairman of Oliver Wight International, is an accomplished operations executive and has extensive international experience in strategy development and global supply chain management. He has a sound understanding of cross-cultural influences in business relationships and excels at managing teams that deliver sustained growth and performance improvement in highly competitive industries. Prior to joining Oliver Wight, he worked with companies to attain their full growth potential through strategic planning and deployment, S&OP/Integrated Business Planning, and leadership development.

Executive takeaways

We’ve all seen the news and felt it at the gas station. I live in Silicon Valley, and I filled up my gas tank yesterday with $6.40/gallon fuel. The Strait of Hormuz is closed because of the U.S. war on Iran, and American consumers are feeling it in the price of gas. Supply chain pros are feeling it in fuel surcharges on transportation and higher prices on raw materials. Countries around the world are shortening school and work weeks because of shortages of fuel. Some communities in India cannot get cooking oil, a basic need for most families. This is a global squeeze. And, of course, there is the grave human cost of soldiers and citizens dying.

Oil producers in the Gulf have shut down refineries because storage tanks are full and there is no place to store refined products. Ships are stuck in port and cannot pass the blockades. Insurance companies will not insure cargo moving through the Strait, so vessel operators will not take the risk of moving uninsured cargo.

What else besides gas?

Fuel prices and supplies aren’t the only things affected.  Products that use petroleum or natural gas as ingredients in their products are also feeling the pinch and long-term concern. Fertilizer, for example, uses nitrogen and ammonia from natural gas to produce nitrogen-based fertilizers. Farmers in California’s Central Valley, where 40% of America’s fruits, nuts, and vegetables are grown, are in emergency mode, trying to find alternatives to synthetic fertilizer.  Fertilizer prices are skyrocketing, which will raise the cost to grow produce and the price to consumers.

Over 99% of plastics are made from crude oil and natural gas. Consider the plastics used for products at your local grocery store.  Almost everything is packed in plastic or in plastic-coated wrapping. We use plastic bags when we select unwrapped produce. Think about this as you look around at the grocery store. If food producers and grocery stores cannot wrap products in plastic because of short supply, they will be unable to sell some products, or the price for packaged products will increase, and that will result in increased prices to consumers.

Specialty chemicals made from petroleum products are not moving either.  These chemicals are used in adhesives, coatings, cosmetics, and pharmaceuticals. Even helium, a by-product of natural gas production, is not moving from Iran.  Helium is used in semiconductor production, and this will likely result in semiconductor shortages if the war continues.

Shortages won’t be over when the war is over

As veteran supply chain professionals know, the effects of supply chain disruptions do not end immediately after the disruption is solved. It’s more likely to be many months or even years before supplies and shipping return to normal levels.  We are likely to see shortages and increased prices for some time. Months of production shutdowns in the Middle East will result in shortages and scarce supplies. Price increases are inevitable. It’s the law of supply and demand.

Another wake-up call

Global supply chains are complicated with scores of moving parts and layers of participants. Disruptions mean that many parts of global supply chains will be affected.  As Brian Straight pointed out in his “Straight Talk with Brian Straight” on May 3, 2026,

  “Perhaps it’s better that we just accept that we operate in a world of constant disruption and that is normal, so we can move on.”

The thing to do now is to continuously plan for alternative ways to address disruptions because they keep coming. Whether it's tariff changes, unexpected wars, changes in geopolitics, or natural disasters, nothing is normal in supply chains anymore.

This is certainly an interesting and exciting time to be in supply chain management. We must now be strategic thinkers and planners for what’s around the next bend in the road.

Executive takeaways

For years, supply chain visibility largely meant tracking shipments through a series of disconnected scan events. The push for increased, end-to-end visibility, though, is heating up with another logistics provider announcing a new solution.  

Penske Logistics this week introduced Supply Chain Insight, a new visibility and analytics platform designed to bring together transportation, warehousing, inventory, and partner data into a unified operational view, as reported by Supply Chain 247. The platform reflects a broader industry shift underway as companies move beyond fragmented visibility toward more continuous, end-to-end operational awareness and follows a similar announcement by UPS, which rolled out RFID scanning across its network.

The Penske platform aggregates data from transportation and warehouse operations alongside outside carriers, third-party warehouses, and partner systems into a single dashboard environment. Penske said the goal is to help companies identify disruptions earlier, respond faster to exceptions, and improve coordination across increasingly complex supply chain networks.

“Our goal with the launch and development of Supply Chain Insight is to help our customers accelerate supply chain performance,” said Jeff Jackson, president of Penske Logistics. “This new platform provides customers with an unprecedented and unified view across their highly complex transportation and warehousing operations. It connects data that is often split across separate systems, giving teams a clearer picture of what’s happening across their supply chain.”

The launch comes as visibility itself is evolving inside supply chain operations. Historically, many visibility platforms focused primarily on transportation milestones and shipment tracking. But companies increasingly want broader operational intelligence that connects inventory, warehouse activity, transportation flows, and order execution into a more synchronized environment.

That shift has accelerated as supply chains face persistent disruption, labor volatility, geopolitical instability, tariff uncertainty, and growing pressure to improve execution speed.

In recent years, technologies such as RFID, IoT sensors, computer vision, and AI-driven analytics have expanded what organizations can monitor in real time. The industry is moving from periodic updates toward continuous visibility models capable of creating near real-time operational awareness across facilities, inventory, freight, and supplier networks.

That evolution was highlighted recently UPS’ large-scale RFID rollout initiative, which signaled how visibility strategies are increasingly focused on persistent inventory and package awareness rather than isolated barcode scans. Instead of relying solely on manual touchpoints, newer visibility architectures are designed to create automated streams of operational data that continuously update as goods move through the network.

The broader goal is not simply seeing more data, but reducing the lag time between operational events and decision-making.

That challenge becomes particularly important during disruption events. In many organizations, operations teams and transportation teams still operate from separate systems and often respond to disruptions with incomplete or delayed information. The result can be what some industry observers describe as a “tug-of-war” between fulfillment priorities and transportation realities, where late operational adjustments create downstream transportation inefficiencies, added costs, and service failures.

Platforms like Supply Chain Insight are designed to help close some of those gaps by creating a more synchronized operational picture across functions.

One of the central challenges Penske is attempting to address is data fragmentation. Transportation management systems, warehouse management systems, inventory systems, and partner platforms often operate independently, making it difficult for companies to identify exceptions quickly or understand how disruptions in one area impact the broader network.

The addition of AI-based querying also reflects another growing trend across the supply chain software market: shifting visibility platforms from passive monitoring systems toward more intelligent orchestration layers capable of surfacing insights, prioritizing risks, and eventually helping automate operational responses.

That may represent the next phase of supply chain visibility strategy. For many organizations, visibility alone is no longer enough. The competitive advantage increasingly comes from how quickly companies can translate operational signals into coordinated action across transportation, warehousing, inventory, and fulfillment operations.

Information from Supply Chain 247 was used with permission in this report.

Executive takeaways

For a long time, sustainability has been the main language through which firms have tried to improve supply chain performance beyond cost, quality, and delivery. This has led to many important changes. Firms have worked to reduce emissions, cut waste, improve traceability, use resources more carefully, and strengthen responsible sourcing practices across their supply chain (SC) networks. All of this has value, and none of it should be dismissed.

At the same time, the pressure on the local social and ecological system that supports SCs continues to deepen. In many sourcing regions, soil is degrading, water is becoming scarce, local biodiversity is declining, and producer-led communities remain economically and environmentally vulnerable. This creates a difficult reality for managers. A firm may show progress on sustainability indicators while remaining dependent on an upstream system that is weakening over time.

This is where the idea of regeneration becomes important.

Why sustainability alone may no longer be enough

Within SCM, sustainability has largely been understood as the effort to reduce negative impact on the local social and ecological systems. The basic question has been straightforward: How can firms make their operations and supply networks less harmful? That has led to a focus on efficiency, compliance, emissions reduction, waste minimization, and better control over sourcing practices. These are necessary efforts, especially in sectors where SCs have historically imposed considerable social and ecological costs.

Regeneration begins from a different starting point. Instead of asking only how harm can be minimized, it asks whether SCs can contribute to the restoration of the local social and ecological systems on which they depend. In other words, the question is not limited to whether the SC is becoming cleaner or more efficient. The deeper question is whether it is helping the local social and ecological systems around it become healthier, more resilient, and better able to sustain long-term value creation.

This difference may appear subtle at first, but it has important implications in practice.

From reducing harm to restoring ecosystems and communities

A sustainable SC may aim to use less water. A regenerative SC would ask whether the underground water table in that sourcing region is improving. A sustainable approach may focus on reducing chemical input or improving supplier compliance. A regenerative approach would go further and consider whether farming methods, local livelihoods, and ecological functions are being incrementally replenished rather than gradually depleted. One tries to reduce damage. The other asks whether the underlying system is being restored.

That distinction matters because many contemporary SCs are still built on an extractive logic, even when they are managed under the banner of “sustainability.” They may extract resources more efficiently, monitor suppliers more closely, and report performance more transparently, but the pattern underneath often remains unchanged. The firm continues to draw value from places that are under ecological stress without sufficiently rebuilding the conditions that make production possible in the first place.

This is especially visible in upstream, nature-dependent sectors, although the lesson applies much more broadly. Consider a sourcing region where yields are maintained only through growing pressure on soil, water, and air. From a conventional standpoint, the SC may appear stable if volumes, prices, and quality remain within acceptable limits. Yet from a longer-term perspective, the SC is becoming more fragile because the local ecological base beneath it is weakening. The same may be true in social terms. A network may be efficient and even well-audited but if local communities remain vulnerable and producer capabilities are not strengthened, then the system (as a whole) is not truly becoming more resilient.

How regenerative supply chains reshape resilience thinking

Seen in this light, regeneration is not simply a moral extension of sustainability. It is also a strategic one.

SCs do not operate in isolation from the local places in which they are embedded. They depend on land, water, and air as part of the local ecological system alongside institutions, communities (in the form of producers and workers), and intermediaries as part of the local social system. When these social and ecological systems are damaged, the effects do not remain external for long. They eventually emerge as risk, volatility, declining quality, weaker resilience, and rising adaptation costs. Firms may try to respond through technology, diversification, visibility tools, or compliance mechanisms, and many of these responses are useful. But such efforts remain partial if the conditions at the source continue to deteriorate.

For this reason, regeneration should not be treated as a more ambitious version of sustainability. It is better understood as a different way of thinking about SC (re)design and performance. It asks managers to look beyond immediate operational outcomes and consider whether their decisions made across the entire network are helping to renew the very systems that make those outcomes possible.

This requires a shift in managerial attention.

Traditionally, SC leaders have been trained to optimize flows, reduce uncertainty, control costs, and improve service levels. More recently, they have also been asked to integrate sustainability metrics into decision-making. Regeneration adds another layer. It requires managers to pay closer attention to the relationship between the firm and the source context from which it draws value. That means asking a set of different questions:

• Are sourcing decisions supporting the long-term health of the production landscape?
• Are supplier relationships building local capability, or are they securing short-term compliance?
• Are firms merely managing the symptoms of fragility or are they helping address the conditions that produce fragility in the first place?

Once these questions are taken seriously, several familiar assumptions begin to change.

Supplier relationships can no longer be viewed only as transactional arrangements governed by price, quality, and delivery metrics. They must also be seen as vehicles through which local ecological and social restoration can either be supported or undermined. Performance can no longer be evaluated only through short-term efficiency gains. It must also account for whether SC activities contribute to soil renewal, the water table, local biodiversity, and community development. Even the meaning of resilience begins to shift. Resilience is not only about buffers or redundancy. It is also about whether the upstream systems that sustain production remain viable over the long term.

This does not mean that sustainability has failed or become irrelevant. On the contrary, many sustainability practices remain essential. The point is that current challenges require a broader horizon. Reducing damage is still necessary, but it may no longer be sufficient in settings where the larger system, in the form of planetary boundaries, is already under significant strain.

Why long-term competitiveness may depend on source-region renewal

For practitioners, this has a simple but important implication. The future of SC competitiveness will not depend only on how efficiently firms move materials, manage information, or monitor suppliers. It will also depend on whether they can protect and renew the places from which value is created. Firms that ignore this may continue to perform well in the short run, but they do so by relying on systems that are becoming increasingly unstable. Firms that take regeneration seriously are more likely to build networks that remain viable because they invest in the renewal of the source itself.

In that sense, regeneration is not a slogan, and it is not a cosmetic add-on to sustainability. It reflects a different managerial understanding of what long-term supply chain performance requires. It suggests that doing less harm, while necessary, is only one part of the task. The grand challenge is to ensure that supply chains do not leave behind weaker ecosystems, weaker communities, and weaker production systems in the process of creating value.

That is why the conversation around regeneration deserves attention “right now”. It asks supply chain leaders to move beyond the narrow goal of minimizing damage and to engage with a more demanding, yet ultimately more realistic question: Can supply chains become part of the renewal of the systems on which they depend?

For many firms, that may well become the defining question as we head towards the 2030 deadline for achieving the SDGs, but the targets are not even halfway there.

About the author

Abhijeet Tewary is a faculty associate in the Operations & Supply Chain Management department at T. A. Pai Management Institute, Manipal Academy of Higher Education, Bengaluru, India. His research focuses on sustainability in supply chain management, especially regenerative supply chains.

Executive takeaways

Editor’s note: This article first appeared on the University of Tennessee, Knoxville’s Global Supply Chain Institute’s blog. It is being reprinted with permission. You can read the original post here. It is the second in a two-part series. You can read the first article here.

Author’s note: The white paper, “Love to Hate the Forecast: Segmenting Planning Demand Triggers to Drive Total Value,” co-authored by Haslam College of Business faculty members Mike Burnette and Lance Saunders, and edited by Ted Stank and Dan Pellathy, marks the 40th white paper released by the UT Global Supply Chain Institute. The paper was released during the Spring Supply Chain Forum and is available for digital download.

While inaccurate forecasts create service and inventory defects, the deeper issue is misalignment between demand signals and supply system capability. The solution is not eliminating forecasting altogether. Rather, it is segmenting demand triggers and applying forecasting only where it creates the highest total value.

The Global Supply Chain Institute white paper, “Love to Hate the Forecast: Segmenting Planning Demand Triggers to Drive Total Value,” argues that the answer is not simply mathematical—it is cultural.

Right-to-Left (RtL) planning

Leading-edge companies are adopting Right-to-Left (RtL) supply chain planning. RtL synchronizes activity as closely as possible to actual consumption. Instead of defaulting to forecast-based planning, organizations segment SKUs and apply demand triggers that best match variation patterns.

The vision is ambitious: provide consumers with the exact product they desire at the moment of next consumption, maximizing total value across the enterprise.

Understanding variation

Three sources of demand variation must be assessed:

• Consumer demand variation
• Customer demand variation
• Company demand variation

In many systems, company-driven variation exceeds consumer-driven variation—an indicator that internal behaviors are distorting the demand signal.

When forecasting does not make sense

Forecasting begins to lose its value when the data reveals persistent structural bias. If monthly forecast results are biased high or low more than 60% of the time, the issue is not random error—it is systemic distortion.

Another warning sign appears when customer service performance and inventory days on hand consistently lag competitors. In these environments, improving forecast accuracy alone rarely fixes the problem. Instead, the underlying demand signal is being influenced by internal behaviors.

Cultural signals are equally revealing. When sales rewards are tied to beating the forecast, when general manager compensation depends on forecast comparisons, or when ownership of revenue and inventory is split across functions without integrated accountability, forecasting becomes political rather than operational. Under these conditions, statistical refinement simply sharpens a distorted signal.

Additionally, when shipment patterns fail to follow clear regression-based slopes or when variation is driven primarily by internal promotions, pricing decisions, or end-of-quarter revenue management, the forecast becomes reactive to internal noise rather than consumer behavior.

In short, forecasting is least effective when internal variation exceeds external demand variation.

When forecasting does make sense

Forecasting remains valuable in specific contexts.

It plays an essential role in long-range business planning, particularly in six-month to five-year horizons where strategic investment decisions must be informed by directional demand expectations.

It is also indispensable for new-to-the-world product introductions, where historical shipment data does not yet exist. In these cases, statistical modeling, combined with structured assumption testing, provides necessary guidance.

Forecasting is most effective when consumer variation is the dominant source of demand fluctuation and when shipment patterns follow identifiable statistical slopes. In these environments, regression-based forecasting tools can meaningfully improve planning precision.

Equally important, forecasting works best in organizations that rigorously measure bias, detect minimal distortion, and operate without financial incentives tied to beating the forecast. When the organization embraces the forecast as a value-added tool, rather than a scoreboard, it regains its strategic relevance.

Case study results

A Fortune 500 CPG company reduced finished goods inventory from 180 days on hand to below 100 after implementing a produce-to-shipment RtL trigger, while improving service performance for 48 consecutive months. A regional food manufacturer identified unused capacity and reduced inventory by 25–30% without sacrificing service. An electronics company implemented a rate-based trigger and reduced safety stock by approximately 20% while maintaining service levels.

These examples demonstrate that forecasting should be applied intentionally, not universally.

Supply chain leaders must segment SKUs, assess variation patterns, eliminate cultural distortions, and build capabilities that move planning closer to actual consumption.

Written by UT professors in collaboration with GSCI partners, white papers translate rigorous research into practical insights for business leaders. The institute’s applied research has been featured in Forbes, Harvard Business Review, Supply Chain Management Review, and The Wall Street Journal. To learn more about how your company can partner to explore advanced supply chain management concepts, visit ASCC.

About the author

Michael H. Burnette is a Global Supply Chain Institute fellow at the University of Tennessee, Knoxville. Burnette came to UT after a 33-year career as a supply chain executive at Procter and Gamble. Most recently, he was the P&G Global Supply Chain leader for Skin Care ($2 billion+ Olay brand) and P&G Global Supply Chain Leader for Hair Care ($4 billion Pantene and Herbal Essence brands). His supply chain leadership and expertise include supply strategy/design, manufacturing, logistics, innovation, PLCM, acquisitions and human resources.

Burnette teaches supply chain courses and manages multiple GSCI projects, including coordinating and publishing white papers based on research conducted between UT faculty and industry leaders. He is a consultant, speaker and co-author of the book Supply Chain Game Changers.

Executive takeaways

Agentic artificial intelligence is beginning to shift procurement from a function defined by insight and analysis to one increasingly driven by execution, particularly across the high-volume, low-value transactions that have historically gone unmanaged.

That was the message from Kaspar Korjus, co-founder and CEO of Pactum, who described how AI agents are already being deployed to autonomously manage portions of the procurement process, including supplier negotiations.

“Now the agents are covering end-to-end procurement,” Korjus told Supply Chain Management Review. “Some things are helping buyers, and in some instances they are doing everything automatically.”

The shift reflects a longstanding constraint in procurement operations. While organizations have invested heavily in analytics, visibility platforms, and sourcing tools, the ability to act on that insight, particularly at scale, has remained limited by human capacity.

From visibility to execution

Procurement teams have traditionally focused their efforts on high-value, strategic suppliers, leaving a significant portion of spend, often referred to as the “long tail,” largely unmanaged. The result is missed opportunities for cost savings, inconsistent contract compliance, and inefficiencies in requisition handling.

Agentic AI is designed to address that gap by executing routine procurement tasks autonomously. In practice, that includes validating requisitions, ensuring compliance with contract terms, and negotiating within predefined parameters.

The approach has already been tested at scale. In a widely cited example, Walmart partnered with Pactum to automate supplier negotiations. As reported by Harvard Business Review, Walmart identified that approximately 80% of its suppliers were not actively engaged in negotiations due to bandwidth constraints.

By deploying AI agents to manage those interactions, the company was able to extend its procurement reach without increasing headcount. That use case highlights a broader shift in how organizations are approaching procurement performance by enabling more consistent execution.

A phased approach to adoption

Despite the growing capabilities of agentic AI, adoption within procurement remains measured and incremental. Korjus noted that most organizations begin by applying AI agents to low-value, low-risk transactions, typically under $50,000. These transactions represent a high volume of activity but relatively limited downside risk, making them a practical starting point for automation.

“Usually, we kick off without humans in the lowest spend, lowest value negotiations,” he said. “Once the value is proven and trust is built, we scale.”

As confidence in the system increases, organizations expand the role of AI agents into more complex scenarios, while maintaining human oversight for high-value or strategic decisions. In many cases, procurement teams define thresholds within existing platforms that determine when AI can act independently and when human intervention is required.

Korjus emphasized that AI agents require onboarding similar to human employees, though.

“They are not magical,” he said. “You need to teach them where the data is, how decisions are made, and what the company values.”

This structured rollout reflects both the opportunity and the caution surrounding AI adoption in procurement, where risk management and supplier relationships remain critical.

Scaling small decisions for measurable impact

While much of the attention around AI in procurement has focused on strategic sourcing and large-scale negotiations, the primary value of agentic AI is emerging in its ability to manage high volumes of smaller transactions.

Organizations often process tens or hundreds of thousands of requisitions annually, many of which receive limited scrutiny due to resource constraints. AI agents can systematically evaluate and act on those transactions, improving pricing alignment, enforcing contract terms, and identifying opportunities for incremental savings.

“If you need to handle 200,000 requisitions coming in and someone has to clean that, the AI agent is making requisitions clean and according to compliance recommendations,” Korjus said.

The financial impact of those improvements is cumulative. While savings on individual transactions may be modest, applying them across a large volume of activity can produce significant results in aggregate, including cost reductions, improved working capital, and better payment terms. Even a $100 savings on a low-value contract, multiplied across thousands of contracts yearly, can result in significant savings.

Redefining the role of procurement

The introduction of agentic AI is not eliminating the need for procurement professionals, but it is changing how their time is allocated. As routine transactional work is increasingly automated, procurement teams are able to focus more on strategic initiatives, supplier relationships, and complex negotiations that require human judgment.

At the same time, the technology is prompting organizations to reconsider how procurement functions operate at scale. Rather than limiting engagement to a subset of suppliers, AI enables broader coverage across the entire supplier base.

That shift aligns with earlier discussions in Supply Chain Management Review around the evolution of AI-driven procurement. In The Invisible Handshake: AI-to-AI Procurement Negotiations, it was noted that automated negotiation capabilities is expanding to interactions between AI systems on both sides of a transaction, further increasing efficiency and reducing friction.

While that scenario is still developing, the current trajectory suggests that AI will continue to expand its role in procurement execution, particularly in areas where processes are structured, repeatable, and data-driven.

Balancing automation and control

Despite the progress, organizations remain cautious about extending AI into high-stakes procurement decisions. Strategic sourcing events, critical supplier relationships, and large contract negotiations continue to require human oversight.

Korjus acknowledged that adoption timelines vary depending on the complexity and risk of the application. Simpler use cases, such as data validation or compliance checks, can be implemented quickly, while more advanced applications may take months or longer to gain organizational acceptance.

“Usually things are in the extremes,” he said. “Some agents can give value within weeks. For more critical parts, adoption is slower because trust needs to be built.”

That balance between automation and control is likely to define the next phase of AI adoption in procurement as organizations seek to capture efficiency gains without introducing unnecessary risk.

A shift already underway

While agentic AI is often framed as an emerging technology, its application in procurement is already moving beyond pilot programs and into production environments.

The key distinction is not the presence of AI itself, but its role in the process. Rather than simply providing recommendations, agentic systems are increasingly responsible for executing decisions within defined boundaries.

For procurement leaders, that shift represents both an opportunity and a challenge: the opportunity to extend operational reach and improve consistency, and the challenge of redefining how decisions are made, governed, and measured in an increasingly automated environment.

As organizations continue to test and scale these capabilities, the focus is likely to remain on a familiar constraint, how to move from knowing what to do to actually doing it.

Executive takeaways

When Amazon announced the launch of Amazon Supply Chain Services (ASCS) on Monday, the headline was that the company was opening its end-to-end logistics network, across freight, fulfillment, and parcel delivery, to businesses of all sizes.

But for close observers of Amazon’s logistics operation, it was more a sign of finally, rather than surprise. Amazon changed the game in logistics with its Prime delivery service and two-day and then next-day shipping. Then it opened up that service to third party sellers on its platform, offering them the same fulfillment levers to pull. A few years ago, its Buy with Prime service launched, allowing companies to access Prime fulfillment services for off-Amazon sales, as long as they were a member of Amazon’s network.

Now, that same network has been opened up to any company, Prime member or not. Already, some big names have joined the fray, with Lands’ End, American Eagle Outfitters and Procter & Gamble are leveraging aspects of Amazon’s capabilities.

In a conversation with Supply Chain Management Review at the Gartner/Xpo Conference in Orlando on Monday, Peter Larsen, vice president of Amazon Supply Chain Services, said the move is less about a sudden strategic shift and more a culmination of a years-long build.

“We’ve been working on these supply chain capabilities for a long time, literally a couple decades,” he said.

The timing, however, is notable. Amazon is entering the broader third-party logistics (3PL) market amid ongoing volatility, capacity constraints, and rising transportation costs. Rather than viewing those conditions as a deterrent, Larsen suggested they were part of the rationale.

“I think we think that plays into our favor actually,” he said. “We’ve been dealing with as much volatility as anyone out there, and we’ve just got a couple decades of [experience under our belts].”

A gradual externalization of Amazon’s network

ASCS effectively extends the logistics backbone that powers Amazon’s retail and marketplace operations into a standalone service. The offering includes multimodal freight, distribution and fulfillment, and parcel delivery, all capabilities that have been incrementally opened to third-party sellers over time. It gives companies access to its transportation network that spans ocean, air, ground, and rail freight, supported by a fleet of 80,000+ trailers, 24,000+ intermodal containers, and 100+ aircraft.

According to Larsen, the final step of making the full suite available to any business required some internal restructuring.

“It took us a little bit of time to externalize our end-to-end supply chain so that business could have the options to use one piece of our network or all of it,” he said.

Much of that work involved decoupling systems originally built exclusively for Amazon sellers. What may sound simple at a high level, such removing the requirement for a seller ID, for example, was far more complex at Amazon’s scale, where hundreds of interconnected services had to be reconfigured.

The push to expand beyond sellers was also market-driven. Larsen noted that existing Amazon sellers were among the first to request broader access.

“They started to knock on our door [saying] ‘it’s a pain to have one supplier for our on-Amazon business and one supplier for our off-Amazon business. Can you open this up?,’” he said.

That demand led to rapid growth in off-Amazon logistics services for sellers, creating what Larsen described as a “logical extension” to now serve the broader market.

Not just excess capacity

Amazon’s move will inevitably raise questions about network utilization, particularly given its massive infrastructure investments over the past decade. Larsen acknowledged that additional volume can help smooth operations but pushed back on the idea that this is primarily about filling unused capacity.

“That’s certainly not the first reason we’re doing it,” he said. “The first reason is because our Amazon sellers started to knock on our door. Of course, more volume is always better for everybody who uses the network.”

In practice, that means ASCS may help Amazon better utilization in its network during off-peak periods, even if that’s not the core strategic driver.

Competing on scale, expectations, and data

By opening its network, Amazon is positioning itself more directly against established 3PLs, brokers, and freight providers. Larsen framed Amazon’s differentiation around three core advantages: capacity, operational rigor, and data.

“We build for a peak that very few, if any, other logistics companies build for,” he said, noting the company’s ability to absorb demand spikes or disruptions.

That capacity is paired with what Larsen described as a high operational bar shaped by decades of serving Amazon Prime customers.

“Prime customers have rising expectations, they always want delivery faster and with more certainty,” he said.

The third pillar—data and AI—is increasingly central. Amazon’s forecasting models operate at massive scale, enabling inventory placement and demand planning capabilities that many enterprises struggle to replicate.

“We’re forecasting demand and placement at a regional level for over 400 million SKUs every day,” Larsen said, arguing that scale helps mitigate one of the most persistent challenges in supply chain planning: the “cold start” problem for new products. By leveraging existing demand patterns across similar SKUs, Amazon can make more informed placement decisions from day one.

Extending Prime-like capabilities beyond Amazon

ASCS also includes parcel delivery and fulfillment services that can extend Amazon’s speed advantages to external customers. Through offerings like Buy with Prime and expedited fulfillment, companies can tap into delivery speeds that approach the Prime experience, though not always identically.

Over time, he added, those services will continue to improve as Amazon’s broader network becomes faster and more efficient.

A familiar playbook

The comparison to Amazon Web Services (AWS) is intentional. Larsen acknowledged that Amazon is applying a similar framework: identify a large, addressable problem, deploy internal capabilities externally, and build a scalable business around it.

“We’re running effectively the same playbook with Amazon Supply Chain Services,” he said.

But logistics is not cloud computing. Physical networks come with constraints such as capacity, labor, infrastructure, and regulation that are far harder to abstract away.

Still, Amazon is betting that its combination of scale, operational discipline, and data advantage can translate into a compelling alternative for companies navigating an increasingly complex supply chain landscape.

The bigger question now is not whether Amazon can enter the 3PL market, but how disruptive it will ultimately be once fully embedded within it.

Executive takeaways

Gather AI has built its business on improving inventory visibility through the use of sensors and cameras mounted to drones. But, as anyone that works in a warehouse knows, there are many more opportunities to track visibility. To address that Gather AI has expanded its intelligence platform to include the ability to mount cameras directly on lift trucks while adding enhanced drone functionality.

Gather AI’s approach focuses on capturing and digitizing every movement within the warehouse, moving beyond traditional scan-based tracking toward a more comprehensive, AI-driven visibility model.

It was just one of the announcements at the recent Modex conference in Atlanta that saw the expansion of end-to-end visibility. UPS announced expansion of its RFID technology to cover its entire supply chain, creating hundreds of thousands new data points within its network and enhancing the ability to locate and track packages in real time. Gather AI’s approach is designed to help operators more accurately track inventory within their warehouse.

From scan-based tracking to continuous visibility

Traditional warehouse operations have relied heavily on manual scans and system updates to track inventory movement. That approach often creates gaps in visibility, particularly between scan events. Gather AI’s system replaces that model with continuous data capture using cameras and AI.

“Instead of having a scan gun,  you now have an AI overlay of every single story,” Sean Mitchell, vice president of customer operations at Gather AI, told Supply Chain Management Review.

The system works by mounting camera modules directly onto material handling equipment, such as forklifts, allowing inventory to be scanned and tracked automatically as it is moved.

As operators pick up, transport, and place inventory, the system captures images, identifies products, and records each transaction in real time.

Turning forklifts into data collection platforms

A key component of the company’s latest offering is the ability to transform existing warehouse equipment into autonomous data collection systems. Camera modules mounted on forklifts scan pallets, count cases, and identify product information during normal operations.

“As the driver drives up, it’s scanning the pallet telling you exactly what you have,” Mitchell said.

The system then connects with warehouse management systems to guide operators to the correct storage location and validate that inventory is placed accurately. Mitchell noted that if the operator places the item in the wrong location, the system generates an alert. The operator can override it and if so, it updates the inventory mapping system so future workers can find the product.

In addition to inventory tracking, the platform captures location and movement data for each piece of equipment.

“It gives you that pinpoint accuracy of exactly where the lift is at any time,” Mitchell said. “You can see the movement of every single lift in that warehouse,” Mitchell said.

Expanding visibility beyond racking

The lift-based system builds on Gather AI’s existing drone-based inventory scanning capabilities, extending visibility across more areas of the warehouse. While drones are effective for scanning racked inventory, the new system enables tracking of bulk storage, pallet movement, and loading and unloading activities.

“It gives you that flexibility of the entire warehouse, not just the racking material,” Mitchell said.

Together, the systems create a more complete picture of warehouse operations, capturing both static inventory positions and dynamic movement.

Introducing continuous drone operations

In addition to the lift-mounted system, Gather AI is introducing enhancements to its drone platform, including an autonomous battery swapping capability on Gather AI’s proprietary drone. The system allows drones to operate continuously without manual intervention, automatically recharging and returning to operation.

“Within three minutes, you’re up and flying,” Mitchell said.

This capability enables warehouses to conduct inventory scans overnight or during off-hours, increasing utilization and reducing operational disruption.

From data capture to operational insight

The company’s platform aggregates data from both drones and equipment-mounted sensors into a centralized system, providing visibility into inventory, workflows, and performance.

“You can see every single put away, every single pick, every single movement… you can get that traceability of every single movement,” Mitchell said.

Warehouse managers can use this data to monitor operator productivity, identify inefficiencies, and better understand inventory flow.

The system also provides insights into broader operational trends, such as inventory turnover, storage utilization, and workflow bottlenecks.

A shift toward fully digitized warehouse operations

As supply chain organizations continue to invest in automation and AI, the ability to capture and act on real-time operational data is becoming increasingly important. Collecting and analyzing that data is a core part of the recent announcements from Gather AI and UPS, among others, as the industry shifts away from event-based tracking toward continuous, system-wide visibility.

By combining computer vision, AI, and existing warehouse equipment, the trend is enabling businesses to digitize the movement of goods throughout facilities.

For supply chain leaders, the implications go far beyond tracking. With more granular data on how inventory moves through the warehouse, organizations may be better equipped to optimize operations, improve accuracy, and respond more quickly to disruptions.

As Mitchell summarized, the goal is to move from partial visibility to a complete operational picture.

“You now have traceability of all of those movements and higher operational efficiency because you’re no longer chasing problems.”

The difference isn’t driven by what companies buy, but by how procurement work gets done. Top-performing organizations achieve significantly higher productivity and faster cycle times, issuing purchase orders in as little as one day compared to 2.5 days for lower performers.

See the full graphic for more.

Related article: How efficient is your procurement process?

 

Executive takeaways

Körber Supply Chain’s newly announced collaboration with NVIDIA is another sign that the push to bring digital twins to the fore in supply chain has moved beyond the theoretical stage for most businesses. The concept of building a digital twin for your supply chain is no longer something that only the largest organizations can accomplish, and that means the technology is now a true decision-making tool.  

The partnership centers on combining Körber’s logistics data and operational expertise with NVIDIA’s Omniverse platform to create accurate digital twins mirroring real-world warehouse and logistics operations with increasing precision. The goal is not just better visualization, but the ability to simulate, test, and optimize systems before they are ever deployed.

That ambition aligns closely with what industry leaders have been working toward for years. As Helena Garriga, president of Körber’s supply chain business, explains, the key enabler is not the concept itself, but the computing power now available to support it.

“The amount of data that you need to analyze and read per second requires a GPU power that is not the norm that we have in a computer,” she said in an interview with Supply Chain Management Review. The partnership with NVIDIA opens new doors to access the power necessary, she added.

From simulation to physical AI

What differentiates this next phase of digital twins is the emergence of what Körber and NVIDIA describe as “physical AI.” These are systems that don’t just analyze data, but interact with realistic, physics-based simulations of the physical world.

Historically, that level of capability has been out of reach, the companies noted in a release.

“The way the simulation was portrayed in the real world was not accurate at all for us to really use it at the customer base,” Garriga said.  

That gap is now beginning to close. Advances in GPU computing and simulation platforms are enabling organizations to incorporate environmental variables such as movement, positioning, system interactions, and physical constraints into their models.

“The amount of data you need to analyze to really have a simulated world that is the same as what we have here is huge,” Garriga said.  “The system will be able to simulate [the accuracy and speed of a supply chain] almost at the same time as it happens.”

The result is a digital environment that can increasingly mirror operations in near real time.

Expanding beyond design into execution

As these capabilities improve, digital twins are no longer confined to network design or long-term planning. They are starting to influence multiple stages of the supply chain lifecycle. One of the earliest and most practical applications is in the sales and solution design process. Instead of relying on static proposals, organizations can now simulate how a system will perform before it is built, Garriga said.

“What we are able to do now is simulate pretty accurately what the customer wants,” she said, noting that it allows the customer to how the solutions will perform before installing.

“It allows them to really understand what they are asking and bridge the gap between what they’re asking and what they really need when they see it running,” Garriga said.

The same approach is accelerating research and development. Instead of relying on physical testing cycles, engineers can iterate in virtual environments. The engineers can test online and see which changes were the right ones. Previously, somebody had to go into a lab and make those tweaks physically.

“That speeds up the R&D process by months,” Garriga said.

Reducing risk in live operations

Where digital twins may ultimately deliver the most value is in live operations. The ability to test changes, whether layout adjustments, throughput increases, or automation strategies, before deploying them reduces both risk and disruption.

This is particularly relevant in high-volume environments where even small disruptions can have outsized impacts. It also ties directly to cost reduction, especially in maintenance and downtime, Garriga said.

Adoption still depends on maturity

Despite the growing capabilities, adoption is far from uniform. Some organizations are still exploring digital twins conceptually, often driven by broader interest in AI. Others are embedding them into core workflows with clearly defined use cases. Garriga points to a common disconnect between interest and execution—the desire to incorporate AI and digital twins and what technology is actually needed to solve the problem.

More mature organizations are taking a different approach, starting with the problem and working backward to the technology, she said.

Technology is not the differentiator

The Körber–NVIDIA collaboration, though, is another step in the evolution of technology, moving simulation technology into the mainstream. Platforms are improving and computing costs are declining, enabling scalable capabilities.

“Thresholds are going down as we speak,” Garriga said. “It will be easier for simple setups to get there, where before that was not economically viable.”

But that doesn’t guarantee value, she said. As with any technology investment, Garriga advised clearly defining the problem. Technology is advancing quickly, but installing the wrong solution won’t be beneficial if it doesn’t solve the underlying problems.

As with any AI-driven initiative, success ultimately depends on clarity of purpose.

Global supply chains are facing more uncertainty than at any time in the past decade. Cyberattacks, supplier failures, geopolitical conflicts, extreme weather, and regulatory changes now intersect to create continuous, multi-dimensional risk.

In this high-impact webinar, leaders share their latest insights, tools, and strategies for navigating volatility. Through a guided discussion moderated by Supply Chain Management Review Editor-in-Chief Brian Straight, Mark Landry, Director of Security Intelligence, AMAROK will explore:

• Best practices in risk intelligence
• Third-party visibility
• Real-time monitoring
• Business continuity
• Proactive mitigation

Executive takeaways

For years, the vision of the fully autonomous, “lights-out” warehouse has captured the imagination of supply chain leaders. In theory, robotics and artificial intelligence would eliminate the need for human labor, creating highly efficient operations that run continuously with minimal intervention.

In practice, that vision remains out of reach for most organizations, though. The reason, according to Erik Nieves, CEO of Plus One Robotics, is straightforward: modern supply chains are too complex and too variable for fully autonomous systems to handle on their own.

“In an actual supply chain constraint, variability is the rule, not the exception,” he told Supply Chain Management Review in a recent interview.

The challenge of variability

Traditional industrial robotics has long succeeded in structured environments, particularly in manufacturing settings where processes are predictable and inputs are consistent. But warehouse and distribution environments operate differently. Products vary in size, shape, packaging, and condition, and nowhere is that more evident than in e-commerce and parcel operations where returns and mixed inventory streams introduce additional complexity.

Variability creates a fundamental challenge for automation, particularly in tasks like picking, sorting, and truck unloading, where robots must interpret and act on constantly changing conditions. Nieves argues that many companies misunderstand the root cause of the problem and focus on installing solutions that don’t solve the underlying problems.

“You have a vision problem because everything in your world is variable,” he said, noting that identifying the real cause is the first step in finding the right solution.

The limitation is not the mechanical capability of robots, Nieves said, but their ability to interpret unstructured environments with the same flexibility as a human operator.

Why full autonomy falls short

The push toward fully autonomous warehouses assumes that AI systems can reliably handle all scenarios without intervention. But in real-world operations, edge cases are not that rare, they are the standard.

Even advanced computer vision systems can struggle in situations where objects are obscured, overlapping, or highly similar. Think a conveyor with four different types of products, one of which is inside an envelope. It is easy for a robot to get confused when the image it sees isn’t the image it expects.

“If it exceeds the confidence threshold, then the robot knows I can just work autonomously, but every once in a while, you get a scene that the robot can’t comprehend,” Nieves said.  

Those moments when the system cannot confidently determine the correct action are where fully autonomous models tend to break down. For operators, the risk is not theoretical as missed picks, stalled systems, or unhandled exceptions can quickly disrupt throughput and service levels.

That’s why Nieves remains skeptical of the lights-out model without humans.

“I’m not a proponent of the lights out warehouse,” he said. “You’re going to need a human in the loop.”

The rise of human-in-the-loop automation

Instead of pursuing full autonomy, a growing number of automation strategies are incorporating a hybrid approach: combining AI-driven systems with human intervention for exception handling.

This “human-in-the-loop” model allows robots to operate autonomously most of the time while ensuring that edge cases are resolved quickly when they arise.

In practice, the system evaluates each task based on a confidence threshold. When confidence is high, the robot proceeds independently. When it falls below that threshold, a human operator is engaged to provide guidance.

Importantly, that human involvement is not constant. It is triggered only when needed, often remotely, allowing a small number of operators to support multiple systems simultaneously. The goal is not to replace human labor entirely, but to use it more efficiently, focusing human attention on the scenarios where it adds the most value.

Learning over time, but never eliminating exceptions

One advantage of the human-in-the-loop model is its ability to improve over time. Each instance of human intervention is captured and fed back into the system, allowing AI models to learn from past scenarios.

“You then feed into the reinforcement learning model and then the robot [so] the next time it sees that scenario, it knows how to respond autonomously,” Nieves explained.

As a result, systems become more efficient, and the frequency of human intervention decreases. But it does not disappear entirely. New products, new packaging, and new edge cases continuously enter the system, particularly in fast-moving environments like e-commerce.

“They never get to zero because there’s always something new being introduced,” he added.

That ongoing variability reinforces the need for a hybrid approach rather than a fully autonomous one.

Rethinking the role of labor

The shift toward human-in-the-loop automation also changes how organizations think about labor. Rather than eliminating jobs, automation often redistributes work. Nieves pointed to the fact that warehouses are traditionally understaffed already, so robots should be added to supplement the current workforce, not replace it.

Instead of performing repetitive picking tasks, workers take on more supervisory and exception-handling roles, often overseeing multiple automated systems at once. This evolution can increase both productivity and job quality, while helping organizations address ongoing labor shortages.

Fully autonomous warehouses may remain a long-term aspiration, but for most organizations, they are not a near-term solution. First, supply chain leaders need to close the gap between vision and reality, and in managing the variability and exceptions that come in real time in modern warehouses.

Executive takeaways

Editor’s note: This article first appeared on the University of Tennessee, Knoxville’s Global Supply Chain Institute’s blog. It is being reprinted with permission. You can read the original post here.

Author’s note: The forthcoming white paper, “Love to Hate the Forecast: Segmenting Planning Demand Triggers to Drive Total Value,” co-authored by Haslam College of Business faculty members Mike Burnette and Lance Saunders, and edited by Ted Stank and Dan Pellathy, marks the 40th white paper released by the UT Global Supply Chain Institute. The paper was released during the Spring Supply Chain Forum and is available for digital download.

I have been actively involved in supply chain work since 1980, across Procter & Gamble, consulting, lecturing, and conducting applied research at the University of Tennessee. During these decades, I have interfaced with hundreds of supply chain organizations. In the last 20 years, virtually all the company leaders I’ve interviewed have included forecasting issues as a top-five improvement opportunity. Despite continuous investment in systems and analytics, forecasting remains a widely recognized barrier to achieving business goals and a significant source of organizational frustration.

Why is forecasting such a persistent obstacle?

The Global Supply Chain Institute white paper, “Love to Hate the Forecast: Segmenting Planning Demand Triggers to Drive Total Value,” argues that the answer is not simply mathematical—it is cultural.

A professional, robust corporate volume forecasting process has been in place in leading supply chains since the 1960s. In the 1980s, organizations began formally documenting forecasting systems and measuring results. Statistical regression methods became commonplace tools for improving accuracy. Over five decades, SKU-family level MAPE—Mean Absolute Percentage Error, a common forecasting metric—has improved incrementally. Yet forecast bias has not improved at the same rate.

This reality forces us to examine two foundational questions: Are forecasts negatively impacted by organizational and functional cultural norms? And are statistical forecast models appropriate for all demand patterns?

Cultural influencers

Historically, supply chains have created at least two—and sometimes as many as four or five—aggregated forecasts covering a 1-to-12-month planning horizon. One forecast (often conservative) is shared externally with enterprise owners or shareholders. A second forecast—typically higher—is deployed internally as the business commitment. In some organizations, additional forecasts exist to support functional reward systems.

Through monthly forecast deployment, the supply chain is tasked with creating a supply plan that delivers the aggregate commitment while meeting customer service, inventory, and cost objectives. Historically, the internal business commitment is “bias high”—in other words, consistently exceeding actual demand—more than 90% of the time.

In response, many supply chains create their own SKU-level forecast through a process known as Left-to-Right (LtR) planning. LtR planning incorporates supplier capability, operational efficiencies, cost targets, and safety stock into the demand signal. Inventory buffers are built to protect against variation in demand, cycle time, lead times, and operational reliability.

While rational, LtR planning shifts the system’s focus from delivering actual consumer demand to managing internal efficiency and protection. Over time, this dynamic generates waste.

Normalized dysfunction

Unproductive behaviors often become normalized. Inventory is added to SKUs experiencing service issues. Inventory is reduced across the board during cash crises. Sales rewards are tied to beating the forecast. General manager compensation may be linked to forecast comparisons. When service or inventory targets are missed, forecast accuracy becomes the convenient explanation.

When forecasting is not working, several warning signs typically appear:

• Monthly forecast results are either bias low or high more than 60% of months
• Customer service results lag competition
• Inventory days on hand lag benchmarks
• High level of demand variation is driven internally

In these environments, the forecast is not the root problem but a symptom of deeper organizational defects.

The role of demand-supply integration (DSI)

True demand-supply integration is focused on creating a single-number business plan that every function executes. Demand plans must be based on unconstrained consumer demand; supply plans must reflect demonstrated capacity. Leadership—across demand, supply, finance, and general management—must own the process. DSI must be a disciplined decision-making drumbeat, not a reporting ritual.

Changing the culture is the first step toward eliminating waste from forecasting systems. Without eliminating internal variation and aligning incentives, new tools will simply reinforce old dysfunction.

Written by UT professors in collaboration with GSCI partners, white papers translate rigorous research into practical insights for business leaders. The institute’s applied research has been featured in Forbes, Harvard Business Review, Supply Chain Management Review, and The Wall Street Journal. To learn more about how your company can partner to explore advanced supply chain management concepts, visit ASCC.

About the author

Michael H. Burnette is a Global Supply Chain Institute fellow at the University of Tennessee, Knoxville. Burnette came to UT after a 33-year career as a supply chain executive at Procter and Gamble. Most recently, he was the P&G Global Supply Chain leader for Skin Care ($2 billion+ Olay brand) and P&G Global Supply Chain Leader for Hair Care ($4 billion Pantene and Herbal Essence brands). His supply chain leadership and expertise include supply strategy/design, manufacturing, logistics, innovation, PLCM, acquisitions and human resources.

Burnette teaches supply chain courses and manages multiple GSCI projects, including coordinating and publishing white papers based on research conducted between UT faculty and industry leaders. He is a consultant, speaker and co-author of the book Supply Chain Game Changers.

Executive takeaways

Supply chain organizations have spent the better part of the last decade investing in digital capabilities, ranging from control towers and advanced planning systems to artificial intelligence and automation. But despite those investments, many are still struggling to translate visibility into measurable operational outcomes.

That gap between insight and execution is becoming one of the defining challenges in supply chain transformation. According to Nick Banich, chief revenue officer at Miebach Consulting, the issue is not for a lack of tools or data; it’s the difficulty of turning those inputs into decisions that drive real performance improvements.

“One of the biggest requests we’re seeing is how can we get more tangible results operationally out of the digital investments that we’re putting into place,” he told Supply Chain Management Review in an interview at the Modex 2026 conference in Atlanta.  

From investment to impact

The surge in digital investment has been driven in part by rising complexity. Global supply chains are dealing with tariff volatility, shifting sourcing strategies, and ongoing disruptions across logistics networks.

At the same time, organizations have built out increasingly sophisticated technology stacks to manage that complexity. Data lakes, control towers, and AI-enabled tools are now common across large enterprises. But more data has not necessarily led to better decisions.

Banich noted that many companies have successfully piloted new tools but are struggling to demonstrate clear return on those investment.

“We see a lot of frustration of, ‘we’ve tried this, we put in this proof of concept, but what’s the ROI,’” he said.  

That frustration is particularly evident as organizations move beyond experimentation and begin to evaluate whether digital initiatives are delivering value at scale.

The limits of visibility

One of the core challenges is that visibility alone does not drive execution, Banich noted. Dashboards can aggregate signals from across the network, and AI tools can process those signals faster than ever. But neither can determine what a specific development means for a given business or what action should be taken. In many organizations, that responsibility still falls to individuals who may not have clear ownership of external risk or decision-making authority.

As Banich explained, signals are often identified but not acted upon in a coordinated way. “Signals get picked up in pieces,” he said. “Without focused attention or anyone clearly responsible for deciding what needs escalation.”

The result is delayed responses, missed opportunities, and in some cases, avoidable disruptions.

When  outpaces process

The execution gap is also being driven by a mismatch between technology adoption and process maturity. Many companies have implemented advanced planning systems, warehouse management systems or analytics tools. Often, though, those systems are not always being used to their full potential. In some cases, organizations revert to legacy processes, limiting the value of the new platforms.

“Are you getting the most out of that system?” Banich asked. “Or does it become outdated and then everyone goes back to Microsoft Excel and they’re just using the APS to pass data around?”

Similarly, continuous improvement efforts are often constrained by how data is accessed and analyzed. Rather than focusing on operational changes, teams can become bogged down in manual analysis.

“The continuous improvement people [are not] continuous improvement people, they’re continuous analytics people going to the floor with a stopwatch and a notepad,” Banich noted.

Tools such as process mining are beginning to address that challenge by providing real-time visibility into how processes actually operate. But adoption is still evolving.

AI: Promise vs. reality

Artificial intelligence is adding another layer to the conversation and another source of expectations. While AI is now a standard feature in many supply chain platforms, its impact is still emerging. Banich noted that much of what is currently labeled as AI delivers incremental improvements rather than transformational change.

“I think right now we’re primarily in small productivity—seven, eight percent—I don’t think we’re at the point [where] large-scale systemic agentic AI solves [all] problems,” he said.

One of the limiting factors is data quality and system complexity. Supply chains remain highly fragmented, with multiple systems of record and inconsistent data structures.

“Our data is rough. The number of systems we have is endless. We still have those same issues,” Banich said. As a result, AI initiatives often struggle to move beyond pilot stages or deliver consistent results across the organization.

Bridging the gap

Closing the execution gap requires more than additional technology investment. Instead, it requires a combination of clear ownership of decision-making, alignment between systems and processes, and a focus on operational outcomes rather than tool deployment.

Organizations are increasingly looking to integrate digital capabilities with stronger process discipline and governance. That includes using simulation, process mining, and scenario analysis to better understand how decisions impact performance and to act on those insights more quickly.

Banich emphasized that the goal is not simply faster analysis, but better decision-making.

“Having the IT and tool stack … you can rapidly analyze a number of options to make a comprehensive decision in a shortened decision cycle,” he said.

A shift in focus

As supply chains continue to evolve, the focus is shifting from building visibility to enabling execution. The organizations that succeed will be those that can connect data, systems, and processes in a way that drives consistent action, Banich said.

That may require rethinking how digital investments are structured and how success is measured, but with the data and tech available today, that success is now within reach.

Executive takeaways

One year after Infios emerged from its rebrand under the leadership of CEO Ed Auriemma, the company is entering its next phase; one defined less by identity and more by execution.

When Auriemma outlined the vision for Infios at ProMat 2025, the focus was on repositioning the company around modern supply chain needs. A joint venture between Körber AG and KKR, Körber Supply Chain Software was rebranded as Infios a year ago following its acquisition of MercuryGate, which moved its solutions outside the four walls.

“We literally, overnight, went from warehousing fulfillment to full supply chain execution,” Auriemma said in an interview with Supply Chain Management Review at that time. But the acquisition also highlighted some areas of concern. As a joint venture company, it was “murky to figure out who we were; we really didn’t have an identity. [I went to the board] and said it’s time, it’s time for us to now go create a new identity that differentiated on who we are.”

Twelve months later, at MODEX 2026, that vision is being tested, and, according to company leadership, gaining traction in a market that continues to prioritize supply chain performance as a strategic differentiator.

According to Tim Moylan, chief growth officer at Infios, the past year has been about sharpening that focus and aligning the business to where demand is strongest.

“We did a lot of work to determine where we should play and where we should go,” he told Supply Chain Management Review. “The ideal customer profile was really important to us.”

From rebrand to execution

If the first phase of Infios was about redefining the brand, the second phase is about delivering results. That shift reflects broader changes in the market. Companies are no longer investing in supply chain transformation as a theoretical exercise; they are demanding measurable outcomes, faster timelines, and clearer ROI.

Infios’ growth over the past year suggests that demand is real, particularly among customers looking to strengthen execution capabilities.

“What we found was … there was a lot of pent-up demand,” Moylan said. “Our bookings last year were very strong, double digits.”

Supply chain remains a board-level priority

Despite ongoing uncertainty from tariffs to geopolitical shifts, supply chain investment has held steady, Moylan said.

“Supply chain … is definitely a board-level conversation. It can make or break C-level careers,” Moylan said.

That level of scrutiny is driving sustained investment, even as organizations navigate cost pressures and shifting global dynamics. “Each and every one of them [we’ve spoken with] said they’re doubling down on their supply chain,” he said.

One of the most notable changes over the past year is the evolution of artificial intelligence in supply chain discussions. While interest has surged, adoption remains pragmatic.

“Supply chain … is quite a conservative space,” Moylan noted. “They’re not going to take on AI because it’s the latest buzzword.”

Instead, companies are prioritizing focused use cases that deliver immediate, measurable value. “We’ve adopted a very use-case based approach,” he said. “You can deploy it in days and you’re getting value out of it within 24 hours.”

At the same time, AI is accelerating innovation behind the scenes. Moylan said it is allowing Infios to develop solutions in shorter periods of time.

Speed to value reshapes technology decisions

Perhaps the most significant shift since Infios’ rebrand is how customers evaluate technology investments. Time to value, Moylan said, “has become a super important component of the investment decisions they’re making.”

 That emphasis is forcing a departure from traditional implementation models as companies move away from monolithic systems in favor of quicker deployments with more targeted benefits. Companies are prioritizing modular solutions that can be deployed quickly, scaled incrementally, and aligned with measurable outcomes.

At the same time, organizations are becoming more disciplined in how they evaluate investments, Moylan noted. “The amount of due diligence is probably as heavy as it’s ever been,” he said as companies focus more on outcomes rather than technology itself.

Looking ahead, Infios faces the same challenge as its customers: execution.

“The next one to two years is really consolidating on the investments… and realizing that investment,” Moylan said of the company’s direction. “Nothing fancy; but the execution is the hardest part.”

Executive takeaways

UPS announced at the recent Modex conference in Atlanta the expansion of RFID-based package sensing across its entire network. While UPS claims it is the first rollout of RFID sensing across an entire network among major transportation providers, it represents an advancement in strategy in the quest for freight shipping visibility and operational flexibility. By shifting from a scanning solution to a sensing solution, UPS is helping usher in a new era of visibility where tracking is no longer just about determining a delivery ETA, but rather about an always-on, end-to-end visibility approach that enables more operational flexibility and rapid decision-making.

“We’re announcing the deployment of RFID package sensing at scale across our network… to enable unprecedented levels of visibility, transparency and reliability for customers of all sizes,” said Michael Yoshida of UPS, told Supply Chain Management Review at Modex.

UPS has used RFID package sensing in areas of its network for years now, most notably in its pharmaceutical cold chain, but the rollout across its entire network, which will take place over the next 18 months around the globe, represents a step forward.

 The technology is pretty straightforward and is similar to what many people experience at airports and some retail locations where you can simply walk out with items in your hand and the sensors automatically charge your account. The UPS tech will use RFID to sense packages as they move through the network, generating more data points and providing more visibility of any given package as it moves around the globe. The RFID is part of the label, providing no additional friction for shippers.

From event-based tracking to continuous sensing

For decades, logistics visibility has been built on events such as barcode scans that require manual intervention. A missed scan or duplicate scan created inconsistencies and black holes in the visibility chain.

“Scanning has been the industry standard … since the early 1990s,” Yoshida said. “With sensing, it’s going to happen automatically.”

UPS’s RFID deployment replaces those point-in-time updates with persistent tracking, creating what Yoshida describes as an “always-on” sensing environment. In traditional models, a package is only visible when someone scans it. In a sensing model, its location and movement are continuously understood as it flows through the network.

This is part of a broader industry shift of visibility evolving from passive monitoring into an active input for decision-making. Guy Yehiav, president of SmartSense by Digi, told Supply Chain Management Review at the NRF Big Show in January that visibility is not a single checkpoint, but a continuous thread. In food production, for example, monitoring begins before freezing, with checks on temperature, humidity, and even pH levels. From there, products move into transportation, where route validation and condition monitoring become critical. UPS has done this in its cold chain, but is now moving it into the mainstream.

Visibility alone isn’t the goal, flexibility is

UPS’s rollout highlights a how many organizations are now thinking about visibility. Continuous sensing enables earlier detection of issues, allowing organizations to respond quicker to those issues. UPS has already seen measurable gains.

“We’ve been able to reduce misloads by nearly 70% with RFID tech that we’ve already enabled,” Yoshida said, noting that those errors can be corrected in real time. “If you walked [a package] onto the wrong truck … the shift to sensing is going to notify us you’re on the wrong truck so that they walk it off and put it on the right truck,” he said.

That kind of real-time visibility is the missing ingredient that organizations are looking for as they seek to move from insight to execution.

Earlier data, better decisions

Another key shift is when visibility begins. “Today our data starts when the package gets into our building,” Yoshida said. “With this RFID announcement, that data is going to start at the time of the label getting created and us picking it up.”

That earlier signal extends the decision window for both UPS and its customers. In practice, that could mean earlier exception detection, more accurate delivery commitments, and improved coordination across upstream and downstream partners.

In January, GreyOrange and Zebra Technologies announced a real-time, location-level visibility solution for retailers to help close the gap between store execution and inventory planning.

“Stores and supply chains are very, very connected,” Akash Gupta, CEO of GreyOrange, told Supply Chain Management Review in a conversation at the NRF retail show. “As we start bringing both of them together, there is a lot of intelligence that you can bring all across.”

The rollout of RFID brings another level of visibility to the entire supply chain.

The economics of RFID

RFID itself isn’t new, but it has remained a technology used only in specialty applications. UPS has used it in healthcare logistics, where shipment sensitivity demands higher visibility. What’s changed is the cost and scalability.

“One of the big drivers were the cost of the RFID labels, which are now down to just a few cents,” Yoshida said.

That cost curve allows RFID to move from niche, high-value use cases into network-wide deployment by making continuous sensing economically viable at scale.

While RFID dramatically increases data volume, UPS is taking a selective approach to how that data is exposed to customers. There is a chance of data overload, and for many customers, they don’t need all the insight RFID sensing will bring.

“We will continue to evaluate what value our customers need versus what we need … and balance what we turn on,” Yoshida said. “We’ve already talked to customers where they’re like, ‘I don’t need every one of those [but] I need to know it was on [the truck] for the final time and it’s now off.’”

Visibility becomes a foundation for execution

UPS’s rollout is part of a larger transformation starting to take place across supply chains. As sensing technologies expand, the definition of visibility is changing from event-based notifications to continuous data generation. This is allowing supply chains, with the help of AI and digital twins, to move to predictive decision-making and active execution.

The winners will not be who has the best data, but who makes the best use of that data.

Executive takeaways

All supply chain organizations operate with performance expectations and target KPIs. The methods used to set those targets vary and as a result an organization may achieve its targets. But performance against plan alone does not necessarily mean that a supply chain has reached a higher or sustainable standard.

Operational excellence implies more than meeting internally defined goals. It reflects not just the setting of stretch targets—often representing a step‑change above current performance or industry benchmarks—but also the organization’s ability to deliver against those targets over time. Without that distinction, organizations can consistently “hit the numbers” while still falling short of what the business needs to compete effectively.

What separates operational excellence from hitting the numbers?

Operational excellence applies to all the day-to-day aspects of running the supply chain, from manufacturing to planning, transportation, and warehousing. It also relates to performance measured over daily, weekly, monthly, and annual time horizons. What separates it from simply hitting targets is not just the level of performance achieved, but how that performance is delivered and sustained.

In practice, operational excellence is defined by results that are consistent, efficient and predictable.

Consistency is about sustaining performance at or above target over time. If high-level performance is merely a temporary spike and then drifts back, it doesn’t really help the business or show that the organization has reached a new sustainable standard. Operational excellence requires that improvements hold over time and become the expected level of performance, not just an occasional high point.

Efficiency shows up in how results are achieved, using the right level of effort and cost. If hitting your targets requires excessive manual intervention, temporary workarounds, or a disproportionate use of resources, performance might look strong on paper—but it isn’t sustainable. Once productivity or cost pressure increases, those results are hard to maintain.

Predictability is reflected in outcomes that are expected rather than surprising. It depends on strong in-process measures that show whether results are on track, allowing for timely interventions where needed if there are gaps. Without predictability, even strong operations are difficult for the business to rely on or leverage. The key benefits of a strong supply chain operation, such as short-cycle innovation and time-sensitive customer promotions, are tied closely to predictability.     

Operational excellence suggests that the organization is driving exceptional outcomes compared to peers and industry norms. But if its goals are yielding average performance, they are likely based more on the targets the organization knows it can reliably hit than on stretching targets that force a step-change in thinking and execution. For example, if supply chain A consistently delivers 3% savings vs. plan each year, that might reflect solid performance. But if the gross margin is too low, that level of improvement may still be insufficient. And if a competitor (supply chain B) consistently delivers 5% in annual savings, it shows that a higher bar is achievable. In this context, consistently hitting goals does not necessarily indicate a level of excellence that meets the needs of the business.

Why is operational excellence in supply chain so critical?

Supply chains that achieve operational excellence provide their business with a competitive advantage, and since the pandemic, C-suites have increasingly recognized the value supply chains can deliver to the top and bottom lines. While few are best-in-class by definition, all supply chain organizations can aim to be operationally excellent and consistently deliver meaningful business impact. 

Benefits of operational excellence: When supply chain teams consistently set stretch goals and higher performance expectations that enable them to consistently deliver results efficiently and predictably, the business sees clear, tangible outcomes, both hard and soft:

• lower costs (which can in turn yield increased sales revenue and profit)
• reduced cash needs (inventory, capital)
• improved customer service
• faster speed to market
• enhanced quality
• improved employee morale and engagement
• optimized processes, tools, and ways of working

Beyond hard output measures such as cost or speed, setting stretch goals and higher performance expectations can surface opportunities to improve processes, tools and ways of working.

Watchouts: Unfortunately, there can also be some unintended barriers to achieving operational excellence, including:

• Setting goals with no business basis: It’s best to avoid pushing the organization to be excellent without a clear connection to business needs, competitive benchmarks and other key metrics. Otherwise, it will be hard to get organizational buy-in on a sustained basis.
• Muscling the system and becoming a one-time wonder: Delivering strong results in an unsustainable or unrepeatable manner, such as through mandatory overtime across departments on stretch timing, can produce short‑term gains but does not establish a sustainable or repeatable way of working.  Process and work systems need to support improved performance on an ongoing basis and in an efficient manner. Sustained reliance on extraordinary measures, such as mandatory overtime, ultimately creates cost and morale or retention issues.
• Chasing a silver bullet: Buying and implementing a new system and hoping results will improve without addressing foundational work processes, roles, training and qualification rarely leads to lasting improvement.
• Ignoring daily work and results: This perspective may be counterintuitive, but over-focusing on improvement work and letting current results drift can lower your baseline and mute the impact of improvements.

How to develop operational excellence?

For starters, the organization needs to objectively assess current supply chain performance against operations, setting stretch goals for improvement even if leadership and the organization do not yet know how to achieve them. These goals can be based on competitive benchmarks, business needs, or simply step-changed performance vs. the current state. It’s also important to ensure that the organization is educated on the reasoning behind and the importance of these new goals.

When it comes to implementation, Step 1 is ensuring that foundational capabilities are in place (e.g., training and qualification; standardized data, work and roles). Step 2 is automating stable and repeated tasks—areas such as master data checking, demand forecasting, and distribution planning. For Step 3, using the capacity saved by automation to invest in improvement work and change management is critical. And finally, Step 4 involves rigorously measuring and tracking performance vs. goals, using in-process measures that can predict output results to catch any drift early.

As performance improves, the organization should revisit its goals and targets and make adjustments where needed. Above all, remember that operational excellence is never business as usual because the bar is always rising.

About the authors

Andrew Byer is a former P&G Supply Chain Leader. Mike Dobslaw leads Ernst & Young LLP’s Supply Chain Planning practice. To learn more about how Ernst & Young LLP and P&G team to support Supply Chain Transformations please write michael.dobslaw@ey.com

Executive takeaways

With AI now entering routine supply chain work, chief supply chain officers (CSCOs) are racing to make their organizations “AI ready.” Yet readiness alone is too limited an ambition.

If CSCOs focus only on adoption, they risk missing the bigger shift already underway: roles are changing, team structures are changing, and the way work gets done is changing with them. Gartner research shows that leaders who take this big picture approach by focusing on AI-human collaboration and its organizational implications outperform laggards who focus primarily on technology adoption.

The pressure to rethink how work gets done in supply chain is only growing, with 88% of supply chain leaders surveyed by Gartner believing it likely or very likely that advancements in agentic AI alone will require new processes for future talent pipelines. That urgency is coming from the top with CEOs looking to leaders across the business to help drive transformation with AI.

That makes AI more than a technology decision. It is a talent, process, and performance issue that sits squarely with supply chain leadership. To capture the full value, CSCOs need to rethink the expectation for their talent, how work is organized and how teams perform through the lens of AI-human collaboration.

To maximize success, CSCOs should prioritize the following three shifts.

1. From “do my job” to “co-evolve with AI”

The first shift is a change in mindset. Many supply chain teams treat AI as a tool that can help people efficiently do existing tasks. However, as AI agents start proposing options, explaining trade-offs, and taking guided action, they can play a larger role in how work gets done. This changes what employees are expected to contribute and how leaders should provide clarity on that expectation.

CSCOs should prepare teams to work with AI as a collaborator, not as a background application. This calls for stronger AI and data literacy and better judgment. Planners need to know when to trust a recommendation from AI and when to push back. Frontline supervisors need to know how to use time saved by automation in ways that raise the quality of decisions, coaching, and execution.

In practice, that could mean a planner spending less time assembling reports and more time resolving exceptions, or a frontline manager using AI-supported insights to coach team members more effectively. The goal is to raise the value of human work as AI takes on more of the routine load.

2. Redefine performance measurement for human–AI teams

As AI becomes part of execution, CSCOs need a broader definition of team performance. Beyond meeting supply chain goals and objectives in the realm of cost and revenue, CSCOs also need to factor in how people and AI work together under real operating conditions.

For example, if an AI agent flags a likely supply disruption earlier than the team would have spotted it on its own, the real measure of success is what happens next. Did the team act quickly, weigh the trade-offs, and make a stronger decision because the signal arrived sooner? That is the kind of intelligence CSCOs need to understand and measure.

3. Embrace agility in workflows and team formations

Many organizations still respond to friction by revisiting job boundaries, process maps or individual KPIs. Those tools have their place, but are not fast enough in supply chains where priorities are changing and decisions are made in real time in response to disruptions. When teams must wait for a formal process review to adjust roles or handoffs, confusion lingers longer than it should, and momentum is lost.

CSCOs need a more adaptive operating rhythm. Core accountability should remain clear, but teams should be able to adjust parts of the workflow as conditions change. Consider a team working from an outdated process design while AI is already predicting demand shifts and triggering earlier replenishment signals. If role clarity is revisited only during a quarterly review, the result is avoidable delay, duplicate effort, and missed value. However, if the team can rework handoffs and regroup around emerging needs, they will be better positioned to keep pace with the business and get more from AI.

Leading through AI-human collaboration

AI will create the most value in supply chains when leaders treat it as an operational issue, not just a technology rollout. That means preparing employees to work alongside AI, redefining what strong team performance looks like, and giving teams more flexibility in how work is organized. CSCOs who make those shifts will be in a stronger position to use AI in ways that improve execution, strengthen decisions, and keep the organization moving as the environment changes.

About the author

Mel Mohamednur is a Director Analyst with Gartner’s Supply Chain Practice. As part of the supply chain talent team, Mel works with CSCOs, head of strategy, and supply chain leaders to navigate talent strategy development. Mel and other Gartner analysts will provide additional insights on human and AI collaboration at the Gartner Supply Chain Symposium/Xpo, taking place May 4-6 in Orlando, FL. Follow news and updates from the conferences on X using #GartnerSC.

Executive takeaways

Despite the enormous effort to meet customers’ expectations, e-retailers often struggle with late deliveries. News reports indicate that the struggle is real. Sure, consumers expect free and fast delivery (Kumar, 2025; Riedl & Mehta, 2026), but final mile operations are expensive and risky. They represent over 50% of e-fulfillment costs and are a key driver of customer satisfaction (Yerapothina, 2025). Furthermore, 87% of shoppers feel that the retailer is fully responsible for delivery delays, regardless of whether the fault lies with the warehouse or the carrier (Convey, 2018).

To address this, retailers typically pour investments into siloed solutions like faster picking bots, better packing materials, or more aggressive carrier contracts. However, these efforts often overlook the entire process flow and, more importantly, the psychological and operational "tug of war" that occurs when managers and workers face mounting order queues.

Our research, recently published in the Journal of Business Logistics (Masorgo et al., 2026), investigates the intricate relationship among order processing (picking, sorting, packing), order delivery, and ultimate on-time performance. By analyzing a dataset of over 10,000 orders from a major e-retailer and conducting interviews with senior operations managers, we identified three critical levers that dictate whether an order arrives on time or falls through the cracks.

1. Early detection is everything

The first step in winning the tug of war is knowing when you are actually losing. Many managers operate in a “visibility vacuum,” where they can see the length of the queue but cannot identify which specific orders within that queue are jeopardizing the delivery promise.

We found a concave relationship between lateness and Order Processing Time (OPT), defined as the proportion of total planned lead time consumed by picking, sorting, and packing. Our data show that orders enter a “danger zone” as soon as internal processing eats up more than 25% of the e-retailer’s total promised lead time.

When processing exceeds this threshold, the pressure shifts downstream to transportation, often leaving carriers with an impossible window. As one operations manager noted: “During the day, pick stations have long lists, then pack stations also have a long line. There is no way to know which orders are about to be late to process them first. We focus on reducing the lines, but the cart with the late order can be stuck way in the back.”

Deeper dive: Elaborating Theory of Swift Even Flow in E-Fulfillment Operations

To counter this, managers must implement monitoring systems that flag orders the moment they hit the critical OPT mark (e.g., 25% in our study), allowing for surgical precision in taking action before the delay becomes irreversible.

2. The art of strategic deprioritization

As internal processing time climbs, a natural tug of war ensues. Managers initially try to compensate for warehouse delays by expediting transportation. They might switch a standard shipment to a premium courier or authorize overtime for drivers.

However, there is an economic and behavioral limit to this recovery effort. Our research found that when OPT exceeds 58% of the e-retailer’s planned lead time, a shift occurs. Managers begin to deprioritize the order.

This is a behavioral response to lost causes. When an order is likely to be late, managers often choose to protect the system’s overall flow rather than wasting expensive resources on an order that looks like it will miss its window. One manager summarized this pragmatism: “Don’t sacrifice the mass to save the few. If an order is late by a day, does it matter if it is two days late?”

Understanding this critical benchmark is vital. Without it, operations often fall into the trap of “re-picking,” or resending items to the picklist because they haven’t reached the transportation bay in time. This creates ghost inventory and overwhelms the staff. By formalizing this threshold (e.g., 58% in our study), companies can stop chasing doomed orders and redirect those resources to ensure that the other orders in the queue stay on track.

3. The complexity lag: Simplicity as a speed lever

The third lever involves the nature of the order itself. It is well-documented that larger, multi-item baskets are harder to pick, but the impact on lateness is compounded by workers’ and managers’ behavior.

Managers and workers naturally gravitate toward less complex orders, such as those containing multiple units of the same SKU or very few items. These orders allow for consistent, rhythmic movements in picking and packing. We found that these simple orders are delivered approximately 8 hours faster than complex ones.

Managers can use this to their advantage by creating dedicated fast-track workflows for low-complexity orders. By clearing the easy wins quickly, they reduce the total volume of the queue, allowing specialized teams to focus on the high-complexity baskets that are more prone to errors and delays.

Breaking the cycle

The friction between warehouse operations and transportation is where the customer experience often falls apart. The battle against lateness cannot be won through brute-force speed; it requires a sophisticated understanding of these operational tipping points.

To move forward, supply chain leaders should:

• Synchronize visibility: Ensure the warehouse management system (WMS) and transportation management system (TMS) share a single lead time clock.
• Empower decisive deprioritization: Document and use critical thresholds to prevent the bullwhip effect of late orders clogging up the system.
• Buffer for complexity: Account for the 8-hour complexity lag when promising delivery windows for multi-item baskets.

By mastering the critical thresholds, e-retailers can stop the internal tug of war and improve on-time performance.

References

Convey. (2018). Last Mile Delivery: What Shoppers Want and How to #SaveRetail. Convey Retrieved from https://www.getconvey.com/press-d-last-mile-delivery-save-retail/

Kumar, N. (2025). Navigating the future of e-commerce logistics: Balancing speed and cost. Supply Chain Management Review https://www.scmr.com/article/navigating-the-future-of-e-commerce-logistics-balancing-speed-and-cost

Masorgo, N., Hoang, T. T. (Jenny), Dobrzykowski, D. D., Bell, J., & Swink, M. (2026). Elaborating Theory of Swift Even Flow in E‐Fulfillment Operations. Journal of Business Logistics, 47(2). 10.1111/jbl.70063

Riedl, P., & Mehta, P. (2026). How autonomous fulfillment is rewriting the rules of supply chain execution. Supply Chain Management Review https://www.scmr.com/article/how-autonomous-fulfillment-is-rewriting-the-rules-of-supply-chain-execution

Yerapothina, S. T. (2025). Unlocking the last mile: A strategic framework for in-store fulfillment. Supply Chain Management Review https://www.scmr.com/article/unlocking-the-last-mile-a-strategic-framework-for-in-store-fulfillment

About the authors

Nicolò Masorgo (Ph.D., University of Arkansas) is an Assistant Professor in the Farmer School of Business at Miami University. Drawing on his order fulfillment and logistics operations industry experience, his main research interest focuses on last-mile delivery operations, service operations, and service supply chain management. His research has been published in several leading journals, including Journal of Business Logistics, International Journal of Physical Distribution and Logistics Management, and Transportation Journal.

Thu Trang Hoang is an empirical supply chain researcher. She graduated from the University of Tennessee, Knoxville, with a PhD in Supply Chain Management. Her research focuses on three main topics: traceability (i.e., food and human trafficking), operational adaptability (i.e., firms’ roles in community relief during disasters), and crowdsourced logistics/e-commerce (i.e., drivers; behaviors under new service/insurance launches, and mathematical modelling).

David D. Dobrzykowski is a Professor of Supply Chain Management and Senior Director of the SCM PhD program at the Walton College of Business, University of Arkansas. His research examines operations and supply chains that feature unique challenges to information processing and the coordination of work processes, such as in healthcare, humanitarian, and sharing economy contexts. He has published in Journal of Business Logistics, Production and Operations Management, Journal of Operations Management, Decision Sciences, Journal of Supply Chain Management, among other leading outlets.

John E. Bell is the Dove Professor of Supply Chain Management at the University of Tennessee.  He holds a doctorate in Management from Auburn University. His research focuses on raw materials, transportation, and sustainable supply chains. He has published over 40 articles in journals such as Journal of Business Logistics, Transportation Journal, and Journal of Operations Management. Prior to joining UT in 2010, Dr. Bell was a career military officer.

Morgan Swink is the Eunice and James L. West Chaired Professor of Supply Chain Management, and Executive Director of the Center for Supply Chain Innovation in the Neeley School of Business, TCU. He teaches and leads research in areas of supply chain management, innovation management, project management, and operations strategy. He has co-authored two supply chain operations text-books, one managerial book on supply chain excellence, and published more than 100 articles in a variety of academic and managerial journals.

Executive takeaways

Despite the advancements in POS (point-of-sales) systems, retail—or notably certain major retailers—still have an inventory accuracy problem. The issue is not due to theft or shrinkage, but according to an insightful December 16, 2024, article on Retail Dive by Daphne Howland, it is due to their use of a draconian method of accounting.

The article states that Nordstrom and Macy’s are moving away from the problematic “retail inventory method” after decades of use, making the shift to “cost accounting.” But based on annual reports (as per the article), retailers Dillard’s, Target, Walmart, Kohl’s, J.C. Penney and Dollar Tree are still using the retail inventory method. One quarter of the National Retail Federation’s top 100 retailers fully rely on the retail inventory method. Other retailers use a partial or hybrid methodology, sometimes due to mergers and acquisitions. 

Created in the 1920s by Malcom McNair, the retail inventory method calculates inventory based on the retail price without counting the inventory. While this was a real time-saver “back in the day” when retail prices probably didn’t change often and before technologies like barcode scanning, the methodology has realistically lost its effectiveness in modern times where retail prices are in a state of flux.

Using the retail inventory method can be like a dog or a cat chasing its tail: because inventory is determined upon price, discounts distort the inventory balance. And how do retailers get rid of excess inventory? Markdowns and discounts create more distortions in the calculation. And because buyers buy based on inventory count, a distorted inventory count has a knock-on effect on the buy or re-buy quantity, worsening the problem. 

Is the shrink issue that’s been in the news really as tied to theft as it has been made out? It is possible that a notable portion of this shrink is actually due to internal issues including administrative mistakes and process errors. 

If inventory counts are not accurate, wouldn’t this also affect forecasts if the retailer offered these to their vendors? What about the accuracy of product activity (sales) data … could this information be compromised as well? Retailers continually blame vendors for their inventory woes, but if retailers cannot provide accurate forecast and inventory data to their vendors (as well as their own personnel), aren’t the retailers as much or even more to blame for their inventory problems, whether it’s too much or too little or too late? 

It's in everyone’s best interest to ensure that inventory is always available when and where the consumer wants it. That’s part of achieving the perfect order. But if the data isn’t accurate, it’s going to have an affect on execution.        

Executive takeaways

A planner walks into a morning operations meeting with three dashboards open. One shows excess inventory, another signals potential stockouts, and a third highlights rising logistics costs. Each is accurate. None leads to a clear decision. The first half of the meeting is spent reconciling numbers. The decision is delayed or escalated. Everyone has the data, but no one owns the decision.

This situation is no longer unusual. Over the past few years, companies have invested heavily in control towers, real-time visibility tools, predictive analytics, and AI. The expectation was straightforward: more data would enable faster and better decisions. Instead, many organizations are experiencing slower decision cycles, more alignment meetings, and increased hesitation. The problem is no longer data scarcity. It is how decisions are made in a data-rich environment. Supply chains are becoming data-rich but not decision-ready.

Why more data is not leading to better decisions

This issue has become more visible as supply chains operate under continuous disruption, where volatility is no longer episodic but structural. In this environment, speed matters as much as accuracy, because a delayed response to a disruption can quickly erase the value of even the most accurate forecast.

At the same time, expectations around “data-driven decisions” have shifted. Leaders are now expected to justify actions with analytics, not just experience. While this improves transparency, it also introduces friction, as decisions are validated, rechecked, and aligned across functions before anyone commits to a course of action. Many organizations now face what a recent industry report describes as an “insight-to-action gap,” where the ability to generate insights exceeds the ability to act on them in time. The underlying issue is not data quality but that decision processes have not evolved at the same pace as data capabilities.

Where decisions break down

Across organizations, similar patterns appear. First, metrics often conflict. Procurement may focus on cost, operations on throughput, and customer‑facing teams on service levels. Each function works with accurate data, but there is no clear rule for which metric takes priority when trade-offs arise. Targets are met locally while the end-to-end decision stalls, and meetings are spent defending metrics rather than choosing a path. Teams optimize locally, but the organization cannot decide globally.

Second, decision ownership is often unclear. Data is widely accessible, but authority is not. Multiple teams can see the same issue, yet no one is clearly accountable for acting on it. Planners hesitate, managers forward issues to peers, and problems travel “up and sideways” until someone senior feels compelled to step in. This creates hesitation, frequent escalation, and a culture where analysis moves faster than decisions. Issues move faster than authority.

Third, more data introduces more validation. Leaders seek confirmation across systems before committing to a decision, asking for one more report, another scenario, or a cross‑check from a different team. While this reduces risk in isolated cases, it slows the organization. Speed is traded for certainty, even when certainty is unattainable. Over time, people learn that it is safer to ask for more data than to decide with the information already available. These patterns reinforce each other. More visibility exposes differences in priorities. Differences require alignment. Without clear ownership, alignment becomes escalated. The result is slower decision‑making despite better information.

The issue can be summarized simply:

When decisions catch up to data

Some organizations are addressing this gap by designing decisions first, and data second. UPS’s Harmonized Enterprise Analytics Tool (HEAT) is often cited for its analytics capabilities. Its impact, however, comes from how it supports specific operational decisions such as routing and capacity allocation. HEAT ingests more than a billion data points per day to create a single view of network performance and feeds that into planning and management routines that adjust how packages move through the network in near real time. Rather than presenting all available data, the platform emphasizes what matters most for those decisions and embeds it in day‑to‑day operations.

PepsiCo took a similar approach with its sales intelligence platform. Instead of building a broad analytics hub, the company focused on a single decision: predicting and preventing out‑of‑stocks at the store level. Its AI‑driven demand forecasting, developed with partners such as TAZI, has achieved about 98% accuracy for most products and reduced truck stock‑outs by roughly 4%, while improving order size and product mix on delivery routes. By tying analytics to a concrete decision and playbook, PepsiCo made it easier for teams to act quickly rather than simply observe more data.

Pfizer’s Global Supply Digital Operations Center also illustrates this shift. The DOC functions as a virtual cockpit for manufacturing and supply, providing a shared, end‑to‑end view of operational performance across sites. Pfizer reports that the DOC has helped reduce cycle time in some areas and, more importantly, has “transformed how manufacturing colleagues collaborate and make decisions,” enabling teams to predict issues before they occur and adjust in real time. The emphasis is not on adding more dashboards, but on speeding and coordinating interventions where they matter most. In each case, technology is important, but the design principle is more important. Data is organized around decisions, not the other way around.

Making supply chains decision-ready

For supply chain leaders, the key question is no longer how to improve visibility. It is how to improve decision speed and clarity. Figure 1 shows a supply chain decision-making pathway. A practical starting point is to identify a small set of critical decisions where speed matters most, such as disruption response, allocation across channels, or supplier adjustments. For each decision, three elements should be clearly defined: who owns the decision, how quickly it needs to be made, and which metrics take priority when trade-offs occur. Writing these down and socializing them turns vague “data-driven” expectations into an explicit playbook.

Leaders should also examine the dashboards and tools used in operations. Each should be tied to a specific decision and cadence. If a dashboard does not clearly answer “what action should be taken,” it is adding noise rather than value. In many organizations, simply retiring or redesigning a few widely used dashboards removes friction and reduces time spent reconciling numbers.

Finally, analytics efforts should be aligned with decision cycles. Instead of building general-purpose tools, organizations should design analytics to support specific decisions at defined intervals. This shift forces clarity about what information is necessary and what is sufficient to act, and it helps analytics teams measure success in terms of faster, better decisions rather than the number of reports delivered.

Figure 1: Supply Chain Decision Making Pathway

From data-rich to decision-ready

Most supply chains have already solved the problem of visibility. The next challenge is execution. Organizations that continue to invest primarily in data capabilities may see diminishing returns. Those that focus on how decisions are made will move faster and respond more effectively to disruption. The advantage is no longer having more data. It is the ability to act on it. Most supply chains do not lack data. They lack clarity on who decides, how fast, and based on which signals.

About the author

Corrine Chen is an educator, researcher, and former industry executive with over a decade of hands-on experience in supply chain management, procurement, and innovation. She teaches supply chain management courses at the University of Nebraska Omaha. Corrine’s work bridges academia and practice, with published research, applied projects, and a passion for empowering the next generation of supply chain professionals. She can be reached at peonyhill@yahoo.ca.

Executive takeaways

It’s Tuesday morning. A VP of supply chain opens her inbox and sees a carrier notice about port congestion in Los Angeles, a broker email about updated CBP documentation requirements, three headlines about tariff developments, and a supplier email flagging delays out of Vietnam. All of it arrived overnight, but none of it tells her which, if any, require action before her 9 a.m. call with the CFO.

Brad McDougle

That’s not an unusual morning. It’s a typical one.

Importers don’t lack information. They get carrier updates, broker notices, tariff headlines, customs guidance, supplier emails, and market reporting all day long. What many still lack is a clear way to decide which outside developments actually matter to their sourcing profile, trade lanes, customer commitment and margin.

Importers aren’t operating in a stable environment with the occasional disruption anymore. This is not getting easier to manage. Thomson Reuters reported in its 2026 Global Trade Report that 72% of trade professionals identified U.S. tariff volatility as the most impactful regulatory change they face, up from 41% a year earlier. Supply chain management rose to 68% as a top strategic priority, up from 35% the year before. Those are real changes in what supply chain teams now have to watch every day.

The usual response is to throw more data at the problem. But more information doesn’t automatically lead to better judgment. Dashboards can pull signals into one place and AI tools can process them faster, but neither can tell a company what a specific development actually means for its business. In many organizations, someone still has to make that call. Large multinationals may have dedicated risk, trade, or intelligence teams focused on that work full time. Many mid-market importers don’t.

Most of the time, external risk gets pushed onto trade compliance, logistics, procurement, or finance as an add-on responsibility, usually on top of a full-time role that’s already demanding. The result is predictable. Signals get picked up in pieces, without focused attention or anyone clearly responsible for deciding what needs escalation. By the time a development is recognized as urgent, they may have already missed the window to respond.

Trade policy makes the problem easy to see. The recent tariff turmoil shows why. Importers aren’t dealing with a single tariff announcement followed by a clean downstream response. They’re dealing with a constant stream of policy changes, legal challenges, agency guidance, retaliatory measures, exemption questions, and sourcing implications. What matters is whether a development changes landed cost assumptions, creates exposure on open purchase orders, affects a key supplier country, or needs immediate attention.

Mistakes here have become expensive. An ECB study cited by Reuters in March 2026 found that U.S. consumers and importers are absorbing most of the financial hit from tariffs, not foreign exporters. Thomson Reuters reported that 76% of trade professionals now believe the current tariff environment reflects a permanent policy shift rather than a short-term problem. According to the same report, 65% of trade professionals are already changing sourcing patterns, while most are renegotiating supplier contracts or moving manufacturing closer to home. Those are major operating decisions made under uncertainty, which makes good judgment even more important.

Customs and enforcement create a different kind of exposure because the signals rarely look urgent until they affect a real shipment. A common example looks like this: CBP updates its documentation expectations for a particular product category. The notice goes to the compliance team, gets logged, and gets marked as something to monitor. Logistics isn’t looped in. A purchase order is already in transit. The shipment arrives and gets flagged for additional documentation the broker wasn’t prepared for. The release is delayed a week. Now the product that was supposed to ship on Friday is sitting there, operations is scrambling, the customer is asking questions, and finance is trying to calculate the cost of the delay. The compliance team saw the notice. What was missing was someone clearly responsible for deciding what needed to happen next.

Supply Chain Management Review noted in January 2026 that trade compliance has moved from a back-office function to a strategically and legally exposed leadership responsibility. That shift shows up in enforcement activity, but it also shows up in who gets asked to explain what happened and why it wasn’t caught earlier.

In logistics, the problem looks a little different. Port advisories, carrier notices, labor developments, severe weather, and chokepoint disruptions often look routine on their own. A notice about congestion at the Port of Miami reads the same way whether a company has three containers sitting at the terminal waiting for chassis or none. A blank sailing announcement from a major carrier looks like background noise until someone checks it against the replenishment schedule for a product line running at six days of inventory cover. The alert was accurate. It just didn’t mean much without the right operating context.

The difference isn’t always how much information a company has. It’s whether anyone is clearly responsible for watching the environment, filtering for relevance, applying judgment, and getting the right signal to the right person early enough for it to matter.

For many importers, that responsibility is still scattered. The carrier sent the notice. The agency published the update. Someone saw the headline. What was missing was not access to the data. It was a lack of clarity about who needed to review it, who needed to raise it, and who needed to respond.

About the author

Brad McDougle is the founder of Import Risk Intelligence. His work focuses on trade policy, customs and enforcement developments, and logistics disruptions affecting U.S. importers. He previously served as a DSS Special Agent and has also operated a U.S. importing business.

Executive takeaways

Airlines manage billions in spend across safety-critical aircraft components, FAA-regulated maintenance services, airport operations, and enterprise technology. Yet many organizations still operate with fragmented processes and loosely connected systems. This limits visibility into how spend is planned, committed, and governed.

Anshul Bansal

Even with modern source-to-pay platforms, insufficient integration with contracts, projects, and financial systems can slow improvements in compliance, spend transparency, and working capital management. Closing this gap requires viewing procurement as an integrated capability within the broader enterprise operating model rather than a standalone system upgrade.

The airline procurement reality

Airlines manage a uniquely complex mix of indirect and direct spend. While indirect categories such as IT, HR, facilities, real estate, marketing, and legal are often governed through enterprise procurement systems, large volumes of operational spend, such as aircraft parts procurement, ground services, catering, and airport operations, have historically operated outside structured S2P processes.

Historically, much of this operational spend bypassed structured procurement. Non-PO transactions were common, approvals occurred outside formal systems, and invoices arrived after services were rendered. Spend data was fragmented across ERPs, legacy maintenance systems, spreadsheets, and emails. Even when strong commercial contracts were negotiated, they were frequently disconnected from execution, stored in offline repositories, unmanaged SharePoint sites, or paper files, with no systematic enforcement at the point of spend.

Compounding the challenge, many aircraft maintenance and parts systems are FAA-regulated, decades-old platforms that are mission-critical and difficult to replace. Rather than attempting wholesale replacement, leading transformations have focused on synchronizing these environments with modern S2P platforms, preserving operational integrity while enabling enterprise governance and visibility.

Integrating direct spend without disruption

Integrating operational spend into S2P proved to be as much an organizational challenge as a technical one. Maintenance and airport operations teams prioritize speed and safety, and any perceived friction can undermine adoption.

Successful programs respected these realities by allowing teams to continue working in certified systems while synchronizing contracts, commitments, and financial controls end-to-end. This approach embedded governance without disrupting execution, creating a compliant and auditable procurement environment for direct spend at enterprise scale.

Operationalizing contracts and suppliers

A major source of value unlock came from operationalizing contracts directly within procurement workflows. Thousands of contracts and active projects were cleansed, digitized, and embedded into transactional workflows.

This shift converts unmanaged spend into contract-enabled purchasing, ensuring pricing, terms, and regulatory requirements are applied automatically at requisition and invoice stages. Standardized supplier onboarding and catalog-driven buying further reduce spend leakage and accelerate supplier enablement. As a result, spend under management has increased from around 40% to 75% to 80% in large-scale programs.

Enable enterprise IT spend

Enterprise IT procurement is complex, with a diverse user base that includes corporate staff, airport personnel, pilots, and flight crews. Some programs have introduced reusable integration connectors between major SaaS platforms to orchestrate IT hardware procurement directly into S2P workflows. These connectors standardize processes for laptops, tablets, mobile devices, and peripherals. Unlike one-off integrations, they can be reused across the enterprise and industry. This improves usability, keeps auditability intact, and ensures operational and financial controls are aligned without disrupting daily workflows.

Measurable outcomes

Across large airline S2P programs, results included:

• Spend under management increased from roughly 40% to 75% to 80%
• Supplier onboarding cycle times dropped from 20–25 days to around 7 days
• Invoice exception rates decreased significantly due to upstream enforcement
• Procurement and payment cycles became faster, improving operational responsiveness
• Multiple legacy procurement and finance systems were sunset, reducing overhead and improving data accuracy

Operational categories such as catering, cleaning, baggage handling, and ground services also saw productivity gains. Suppliers gained better visibility into contract terms, invoice status, and payment schedules, reducing exceptions and improving collaboration.

Organizational realities and trade-offs

These transformations are not executed without challenges. Integrating regulated legacy platforms takes time and careful planning. Resistance can occur when teams are used to legacy systems. Success often requires phased rollouts, role-based training, and close collaboration between procurement, finance, IT, and operational teams. When managed well, the long-term benefits in visibility, compliance, and enterprise agility outweigh the upfront effort.

Procurement as an operating system

Source-to-pay transformation is no longer just a back-office modernization initiative. It has become a strategic enabler of operational resilience, compliance, and value realization. Integrating legacy and modern systems, direct and indirect spend, and supplier and internal workflows shows that procurement can directly influence financial performance, working capital efficiency, and operational agility.

Measurable results, from higher spend under management to faster supplier onboarding and improved compliance demonstrate that procurement can move from a transactional function to a core driver of enterprise-wide performance. For airlines facing margin pressures, complex regulations, and distributed operations, mature S2P models are essential infrastructure for sustaining growth, reducing risk, and capturing the full value of negotiated contracts.

Author disclosure

The author holds over 18+ years of technology consulting experience and has held senior technical and delivery leadership roles on multiple large-scale airline Source-to-Pay transformation programs and continues to advise airline clients on procurement and technology modernization initiatives. This article reflects industry insights and observed outcomes rather than personal opinion.

About the author

Anshul Bansal is a technology consulting leader at Accenture LLP with 18+ years of experience in Big 4 firms for designing and delivering large-scale Procurement, Finance, and Supply chain transformation programs. He specializes in implementing Source-to-Pay (S2P) and spend management transformations, helping organizations translate strategy into executed outcomes.

Throughout his career at leading consulting firms, including Accenture and Deloitte Consulting, he has served as a trusted advisor to C-suite executives, guiding complex enterprise transformations spanning sourcing, supplier risk management, procurement operations, and financial systems integration. He works closely with executive sponsors and functional leaders to modernize operating models, integrate digital platforms, and embed new capabilities into day-to-day execution. From experience working with Fortune 500 clients across several industries, he has led one of the industry first transformation for US major airline carrier and had further replicated to other major airline carrier. He serves as an industry expert advising several airline clients on leading practices for end to end process re-engineering and also define solutions to connect with decades old complex direct spend procurement applications.  

His recent work focuses on technology reinvention through analytics, automation, and AI, with a pragmatic emphasis on breaking down data silos, reducing manual workarounds, and enabling more resilient and adaptive supply chain operations.

Executive takeaways

Warehouse automation is no longer a differentiator—it is a prerequisite for operating at scale. Rising labor costs, volatile demand patterns, tighter delivery expectations, and increasing SKU complexity have forced organizations to accelerate investments in goods-to-person systems, automated storage, high-speed sortation, and automated packing. Yet as automation density increases, many enterprises discover that technology selection alone does not translate into predictable performance or sustainable return on investment.

The primary challenge has shifted from what automation to deploy to how automation is orchestrated. Modern warehouses now operate as complex systems of systems, where SaaS-based warehouse management systems, automation execution platforms, and on-premises control software must function as a cohesive ecosystem. In these environments, the success or failure of automation initiatives is determined less by mechanical capability and more by the underlying software architecture that coordinates decisions, execution, and exception handling in real time.

A common misconception in automation programs is the assumption that vendor-provided systems will naturally integrate “out of the box.” In practice, each platform introduces its own data models, service-level assumptions, and failure behaviors. SaaS WMS platforms, while offering faster deployment and continuous innovation, are intentionally constrained to configuration over customization, limiting direct control over execution logic. As a result, the responsibility for ensuring scalability, resilience, and predictable behavior increasingly falls on the customer’s integration and orchestration design.

This practitioner-led case study examines the software architecture behind a highly automated, greenfield warehouse implemented using commercially available SaaS and automation platforms. Rather than describing a single company’s solution, the article presents a generalized, reusable reference architecture that reframes the SaaS WMS as an orchestration layer rather than a control system. The intent is to provide supply chain and technology leaders with a practical blueprint for reducing automation risk, improving time-to-value, and future-proofing warehouse platforms in an era of rapid change.

Why businesses need automation-first warehouses

Warehouse automation has shifted from a long-term aspiration to a near-term business necessity. Rising labor costs, fluctuating demand, tighter delivery windows, and increasing SKU complexity have fundamentally changed the economics of warehouse operations. Automation-first warehouse designs are no longer driven solely by efficiency gains, but by the need to create resilient, scalable fulfillment platforms that can operate predictably under peak conditions.

At the inbound edge, automation enables no-touch or low-touch receiving and storage, reducing manual handling while improving accuracy and throughput. Automated putaway, buffering, and goods-to-person systems allow inventory to be stored densely and retrieved efficiently, enabling better utilization of vertical and horizontal space. This is particularly critical as facilities grow larger and land and construction costs continue to rise. Automation-first designs allow organizations to maximize usable storage capacity without proportionally increasing labor or footprint.

On the outbound side, automation enables faster and more consistent fulfillment by reducing travel time, sequencing work intelligently, and decoupling picking, packing, and shipping activities. Automated sortation, buffering, and packing systems support high-throughput order processing while maintaining accuracy. These capabilities are increasingly essential to meet same-day or next-day delivery expectations without relying on unsustainable labor models.

Automation also plays a direct role in shipping cost optimization. By consolidating multiple units across orders and intelligently combining items stored both within and outside automated zones, organizations can reduce carton count, optimize packaging, and lower transportation costs. The business value of automation is therefore not limited to labor savings, but extends across storage efficiency, fulfillment speed, and transportation economics.

However, while the business case for automation-first warehouses is compelling, many organizations underestimate the complexity of making these environments function as a cohesive system. That complexity does not reside primarily in the automation equipment itself, but in the software architecture required to orchestrate it.

Why automation-first warehouses need a software architecture model

Modern warehouses are increasingly defined not by individual automation technologies, but by the software platforms that orchestrate them. As automation density increases, modern warehouses now operate as systems of systems, with multiple automation technologies coexisting within a single facility. Goods-to-person grids, sorters, buffers, conveyors, and automated packing systems must work alongside traditional pallet, case, and manual picking areas. These mixed environments introduce real-time dependencies across workflows that were previously isolated, requiring precise coordination between automated and human-driven processes.

This complexity is further amplified by the reality of SaaS-based warehouse management systems. While SaaS WMS platforms offer faster deployment and continuous innovation, they prioritize configuration over customization and provide limited visibility into internal processing. As a result, organizations have less control over execution behavior and must rely heavily on the quality of their integration architecture to meet performance and reliability expectations. In automation-first environments, integration becomes the primary mechanism through which operational intent is expressed and enforced.

A common misconception in automation programs is the assumption of “out-of-the-box integration.” Vendors often imply that their systems will naturally interoperate, yet in practice, each platform introduces its own data models, service-level assumptions, and failure modes. Message timing, state ownership, error handling, and retry behavior vary significantly across systems. The responsibility for reconciling these differences — and for ensuring that automation behaves predictably at scale — ultimately falls on the customer.

This gap between expectation and reality leads many implementations to suffer from bottlenecks, timeouts, and operational workarounds that erode the value of automation. When software architecture is treated as an afterthought, automation becomes brittle and difficult to evolve. Conversely, when a clear software architecture model is established upfront, organizations gain resilience, scalability, and the ability to extend automation capabilities without destabilizing operations.

For these reasons, automation-first warehouses require more than advanced equipment or feature-rich platforms. They require a deliberate, software-first architecture model that defines how systems interact, how decisions are orchestrated, and how failures are managed. The following sections outline the architectural principles that underpin such a model and form the foundation for a scalable, automation-first warehouse software platform.

Design principles for an automation-first warehouse software platform

Automation-first warehouses demand a fundamentally different software mindset than traditional, labor-centric facilities. The following design principles form the foundation of a reusable architecture model for orchestrating modern, highly automated warehouse environments. These principles are not tied to specific products or vendors; rather, they define the architectural behaviors required for scale, resilience, and long-term adaptability.

Orchestration over direct control

In an automation-first warehouse, the WMS must act as a system of orchestration, not as a controller of physical equipment. Business intent—such as prioritizing orders, releasing work, or handling exceptions—should be expressed at the orchestration layer, while execution systems manage the mechanics of how that intent is fulfilled. This separation prevents business logic from becoming tightly coupled to specific automation technologies and allows execution systems to evolve independently without destabilizing core warehouse operations.

Event-driven by default

Automation environments generate high volumes of state changes that must be processed in near real time. An event-driven integration model is therefore essential. Systems should communicate through well-defined business events rather than synchronous, chatty interactions. This approach improves throughput, reduces latency sensitivity, and enables systems to absorb spikes in volume without cascading failures. Event-driven design also provides a natural foundation for replay, recovery, and auditability.

Loose coupling across vendor platforms

Automation-first warehouses are inherently multi-vendor ecosystems. Each platform introduces its own lifecycle, release cadence, and operational assumptions. The architecture must deliberately minimize coupling between systems by enforcing clear integration contracts, isolating state ownership, and avoiding shared internal dependencies. Loose coupling enables components to be replaced, upgraded, or expanded without requiring wholesale redesign of the warehouse platform.

SLA-aware interactions

Not all warehouse interactions are equal. Some flows, such as induction or packing confirmation, are highly latency-sensitive, while others can tolerate asynchronous processing. The architecture must explicitly distinguish between these interaction types and apply appropriate integration patterns accordingly. Designing with service-level awareness ensures that automation systems meet operational expectations without over-engineering low-risk flows or introducing unnecessary synchronization.

Failure as a normal operating condition

In highly automated environments, failures are inevitable—network interruptions, equipment faults, and partial outages are part of daily operations. The software architecture must assume failure as a normal condition and provide built-in mechanisms for retry, fallback, and manual intervention. Clear ownership of error states and recovery paths prevents small issues from escalating into systemic disruptions and allows operations to continue safely during degraded conditions.

Policy-driven, not hard-coded behavior

Automation-first warehouses require flexibility to adapt to changing volumes, fulfillment strategies, and business priorities. Behavioral decisions—such as routing, prioritization, or buffering strategies—should be governed by configurable policies rather than embedded logic. Policy-driven design enables organizations to adjust operations without invasive system changes and supports continuous improvement over time.

Scalability through architecture, not customization

Scalability in automation-first warehouses is achieved through architectural discipline rather than extensive customization. By relying on standardized integration patterns, clear system boundaries, and event-based communication, the platform can scale horizontally across throughput, facilities, and automation types.

This approach reduces technical debt and preserves the benefits of SaaS-based systems while supporting complex automation scenarios. These design principles establish the foundation for a modern, automation-first warehouse software platform. The next section applies these principles to a reference software architecture model, illustrating how SaaS WMS platforms, execution systems, and automation technologies can be orchestrated as a cohesive, scalable ecosystem.

The strategic blueprint: The automation-first warehouse

Architecture: From orchestration to execution

Modernizing the warehouse requires shifting focus from individual mechanical components to a unified software and integration orchestration layer. This reference architecture serves as a template for a scalable, resilient ecosystem that minimizes the “technical debt” often associated with automation.

Reference Architecture for an Automation-First Warehouse Software Platform

This reference architecture illustrates how a modern, automation-first warehouse can be structured as a layered, software-driven ecosystem rather than a collection of tightly coupled systems. Each layer plays a distinct role in translating business intent into physical execution while preserving scalability, resilience, and vendor flexibility.

1. Orchestration layer: SaaS warehouse management system (WMS)

At the top of the architecture sits a SaaS-based WMS, serving as the system of orchestration for the warehouse network. Rather than directly controlling automation equipment, the WMS defines what work should be performed—order priorities, inventory allocation, and fulfillment policies—while delegating how that work is executed to downstream systems.

This separation enables executives to scale capacity, introduce new automation technologies, or modify fulfillment strategies without destabilizing core warehouse operations. By operating as a cloud-native orchestration hub, the WMS provides enterprise-wide visibility while supporting rapid deployment across facilities.

2. Integration layer: Event-driven, vendor-neutral connectivity

Beneath the WMS, an event-driven integration layer connects planning systems, execution platforms, and automation software through well-defined business events. This layer replaces rigid point-to-point integrations with asynchronous communication, allowing systems to operate independently while remaining coordinated.

By enforcing vendor-neutral contracts and isolating state ownership, the integration layer absorbs variability in vendor behavior, reduces latency sensitivity, and enables the platform to scale throughput without cascading failures. This design is foundational to operating automation-first warehouses under peak conditions.

3. Execution layer: Warehouse execution and control systems

Closer to the physical environment, warehouse execution systems (WES) and warehouse control systems (WCS) translate orchestration intent into machine-level actions. These systems manage sequencing, buffering, and real-time coordination across automation assets such as goods-to-person systems, sorters, conveyors, and automated packing.

Locating execution logic near the equipment improves operational resilience, allowing facilities to continue operating during transient cloud or network disruptions while maintaining alignment with upstream orchestration decisions.

4. Intelligence layer: Analytics, optimization, and AI

Surrounding the core operational layers is an intelligence layer composed of analytics platforms and optional AI/ML optimizers. This layer transforms operational data into predictive insights, enabling organizations to optimize labor, throughput, and inventory flow proactively rather than reactively.

By decoupling optimization from execution, the architecture allows advanced decision-making capabilities to evolve independently, future-proofing the warehouse as fulfillment complexity continues to increase.

Takeaways

• Automation success is an architecture problem, not a hardware problem.

As automation density increases, the primary risk shifts from equipment performance to software orchestration and integration design.

• The SaaS WMS must act as an orchestrator, not a controller.

Treating the WMS as a coordination layer—rather than direct automation control—improves scalability, resilience, and vendor flexibility.

• Event-driven integration is foundational, not optional.

High-throughput, automation-first warehouses require asynchronous, event-based communication to absorb volume spikes and avoid cascading failures.

• Vendor neutrality preserves long-term strategic flexibility.

Loosely coupled architectures reduce dependency on individual automation providers and simplify future expansion or replacement.

• Future-proofing requires policy-driven design.

Encoding operational behavior through configurable policies—rather than hard-coded logic—enables continuous adaptation as volumes, channels, and fulfillment strategies evolve.

• Reframing the WMS as a cloud-native orchestration hub shifts automation investment from fixed CAPEX to scalable OPEX, improving responsiveness to seasonal demand.

Implications for supply chain & technology leaders

For executives overseeing large-scale warehouse automation investments, the implications are clear. Automation-first fulfillment strategies must be governed as software platform programs, not as collections of isolated equipment deployments. This requires elevating software architecture, integration design, and orchestration ownership to the same level of executive attention traditionally given to mechanical automation and facility design.

Leaders should ensure that SaaS WMS platforms are positioned as systems of orchestration, with clear execution boundaries defined between planning, integration, and automation control layers. Investment decisions should prioritize event-driven, vendor-neutral integration capabilities that preserve long-term flexibility and reduce dependency on individual automation providers. Finally, organizations must adopt policy-driven operating models that allow fulfillment behavior to evolve without repeated system rework, enabling automation platforms to scale sustainably as volume, channels, and customer expectations continue to change.

About the author

Muruganandham Kalimuthu is a principal engineer and software architect at a major U.S. retail company, with experience designing both custom, in-house warehouse management systems and large-scale, SaaS-based WMS and automation platforms. He has led greenfield automation initiatives as well as brownfield modernization programs involving legacy warehouse stacks and phased SaaS adoption across high-volume retail fulfillment networks.

Executive takeaways

There is a pattern buried inside every major technological revolution that operations leaders rarely discuss because it takes us away from the exhilarating feeling of having found the silver bullet to organizational bottlenecks. Every time a transformative technology matures and becomes broadly accessible, organizations do not benefit equally. The ones that fall behind are rarely the ones who failed to adopt the technology. They are the ones who adopted it without understanding where the constraint moved.

Niraj Jha

This is the Law of Constraint Migration. And it is playing out right now with AI, at a speed that will separate this decade's operational winners from its casualties.

The pattern, three times over

We can study the transition from horse-drawn transport to the internal combustion engine in the early 20th century. Before the IC engine, the binding constraint on freight and logistics was biological. Success depended on an intricate operational knowledge: the care and conditioning of horses, the management of stables and feed supply chains, the scheduling of rest cycles, the expertise to read an animal’s health before a long haul. Fleet operators who mastered that knowledge had a genuine competitive advantage. Then the IC engine arrived and made all of it irrelevant almost overnight. The constraint did not disappear. It migrated. The new operational imperatives were fueling infrastructure, mechanical reliability, clutch cables and drive trains and the staffing of skilled mechanics, route planning around fuel stops rather than water troughs. The transport operators who thrived were not simply the ones who bought trucks earliest. They were the ones who recognized that the knowledge defining success had fundamentally changed, and rebuilt their organizations around the new constraint before their competitors understood what had shifted.

The same pattern repeated with electrification. By the early 20th century, electricity was widely available and cheap. Yet factory productivity did not surge immediately. Historians of technology, most notably Paul David in his work on the dynamo and the computer, documented the lag. Factories that simply replaced their existing power arrangements with electric motors gained little. The constraint had migrated from energy generation to factory layout, workflow design, and the organizational logic of production itself. The productivity revolution came only when manufacturers rebuilt their operations from the floor up around what electricity actually made possible.

Then computing. By the 1990s, computing power was commoditized. Hardware was cheap. Software was available. Yet research consistently showed productivity paradoxes across industries. The organizations extracting full value were not the heaviest technology spenders. They were the ones that changed how decisions were made, how information flowed, and how humans worked alongside machines. The constraint had migrated from processing power to organizational capability and process design.

Three revolutions. Three migrations. Each time, the technology became a commodity faster than most organizations could adapt to where the new constraint had landed.

Where AI is moving the constraint now

AI in supply chain and operations is following this exact pattern, but the migration is happening faster than any prior cycle, and the destination is less obvious.

The surface-level constraint AI is dissolving is clear: the time and cost of analysis. Tasks that required a team of analysts working for days, demand sensing, supplier risk scoring, routing optimization, anomaly detection across thousands of SKUs, can now run continuously and autonomously. That constraint is effectively gone for any organization with the infrastructure to deploy modern AI tools.

But the constraint has not disappeared. It has migrated to three places most organizations are not yet looking.

The first is data architecture. AI does not create insight from noise. It amplifies whatever signal exists in the underlying data. Organizations with fragmented systems, inconsistent master data, and siloed operational records will find that AI accelerates their existing dysfunction as readily as it accelerates good decision-making. The constraint has moved from analytical capacity to data quality and governance, and most operations functions still treat data infrastructure as an IT problem rather than a strategic one.

The second is decision rights. Autonomous systems make autonomous decisions. When AI reroutes a shipment, activates a secondary supplier, or adjusts a production schedule without human initiation, the question of who owns that decision, and who is accountable when it is wrong, becomes operationally critical. Organizations that deploy AI without restructuring their decision rights frameworks will discover this constraint the hard way, usually during a high-stakes disruption when accountability is suddenly important.

The third is the capability of the people left in the loop. As AI absorbs routine analytical and transactional work, the humans remaining in operational roles are being asked to do something harder: exercise judgment on novel situations the model has never seen, interpret outputs that arrive without full explanatory context, and make calls that sit at the edge of the system’s confidence. The constraint has migrated from execution capacity to human judgment quality, and most organizations are not investing in that capability with anything like the urgency they are investing in the AI deployment itself.

Why equal access does not produce equal outcomes

This is the part of the AI conversation that does not appear in vendor presentations. When a technology matures and becomes broadly accessible, the naive assumption is that the playing field levels. Everyone has access to the same tools, so outcomes should converge.

They do not converge. They diverge. Because the constraint has migrated, and organizations differ enormously in their readiness at the new constraint location.

Two companies in the same industry can deploy the same AI platform, with the same budget, at the same time, and produce radically different results. The difference will not be the algorithm. It will be the quality of the data feeding it, the clarity of the decision frameworks governing it, and the depth of human judgment available to override it when the situation demands. Those factors are not created in a software deployment. They are built over years, through deliberate organizational investment, and most leaders are not yet treating them as the urgent operational priorities they are.

The organizations that extracted full value from electrification did not simply buy electric motors. They spent a decade redesigning their factories. The organizations that will extract full value from AI will not simply buy platforms. They will spend years redesigning the organizational substrate that AI operates within.

What operations leaders should do differently starting now

The practical implication is not to slow down AI adoption. It is to invest in parallel, with equal urgency, in the three places the constraint has migrated.

On data architecture: treat data quality as a supply chain input, not an IT project. Map the data flows that your AI systems will depend on the same way you would map a supplier network. Identify the single points of failure. Understand where the signal degrades. This is infrastructure investment, and it needs to appear on the capital plan accordingly.

On decision rights: before deploying autonomous systems, define explicitly which decisions they own, which they inform, and which remain with humans regardless of what the model recommends. Build the exception protocols before you need them, not during a crisis. The time pressure of a disruption is the worst possible moment to be clarifying accountability.

On human judgment: identify the roles in your operation where AI will concentrate decision complexity rather than reduce it. These are the people who will be asked to exercise judgment on novel, high-stakes situations with AI-generated context they may not fully understand. Invest in their capability development with the same intentionality you are bringing to the technology deployment itself.

The constraint has always moved

There is a version of the AI conversation that is essentially triumphalist. The technology is powerful, adoption is accelerating, and the organizations moving fastest will win. That version is incomplete.

The more accurate version, the one that the history of industrial transformation actually supports, is that the technology is powerful, the constraint is moving, and the organizations that will win are the ones that figure out where it landed before their competitors do.

The horse did not end the constraint of distance. It relocated it, until the IC engine moved it again. Electrification did not end scarcity. It relocated it. AI will not end operational constraint. It will relocate it, to data quality, to decision architecture, to human judgment at the edge of algorithmic confidence.

The leaders who understand this will not simply be early adopters. They will be the ones who invest in the right things at the right time, because they were looking in the right place when everyone else was still celebrating the deployment.

About the author

Niraj Jha is Senior Director of Logistics at Niagara Bottling, the largest privately held beverage company in the United States, where he oversees a network of manufacturing plants, third-party logistics providers, and the deployment of AI across supply chain operations. He is the author of From Engines to Algorithms and is an avid writer, sharing his ideas across his substack and multiple reputable publications.

Executive takeaways

Artificial intelligence has become central to supply chain risk management. Companies are deploying predictive models, control towers, and agentic systems to monitor disruptions across suppliers, transportation lanes, ports, and global events. The infrastructure has never been more sophisticated. And yet a consistent complaint echoes across operations and planning teams: the AI flags the problem, but it cannot tell us what to do about it.

Building a context-ready architecture

Prabhat Rao Pinnaka is a product leader focused on building data and AI-driven enterprise platforms that enhance execution and decision-making across the end-to-end supply chain, including planning, procurement, warehousing, transportation, and customer fulfillment. He leads cross-functional teams in building analytics and AI-enabled workflow solutions that strengthen supply chain performance, increase operational visibility, and support governed automation at scale. Through his work as a keynote speaker, peer reviewer, and advisor, he contributes practitioner insight on the adoption of AI in operational environments. He serves on the ISCEA Americas Advisory Board and is a founding member of Saint Louis University’s Technology in Supply Chain Advisory Board.

Sukanya Bollineni is a Technical Product Owner at Johnson Controls. With a background in technology and product delivery, she works at the intersection of business needs, digital solutions, and cross-functional execution. Her professional focus aligns with broader efforts in digital manufacturing and connected industrial operations, where technology is increasingly used to improve efficiency, performance, and smarter decision-making.

Senthilkumar Thiyagarajan is a supply chain analytics professional whose work focuses on digital twins, supply chain optimization, and Industry 4.0 applications. Currently with Medline Industries, he brings a strong blend of academic research and practical industry perspective, with a Ph.D. in Supply Chain Management from Purdue University and a research focus on resilience in complex supply chains.

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