Executive takeaways

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

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

The gap nobody warned you about

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

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

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

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

What actually breaks

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

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

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

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

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

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

Why it keeps getting missed

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

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

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

The question to ask before you sign

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

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

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

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

How organizations close the gap

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

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

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

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

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

What to do before the business case closes

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

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

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

A final word

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

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

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

About the author

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

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

Executive takeaways

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

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

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

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

Why is adoption important?

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

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

What it takes to deliver ROI

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

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

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

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

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

Summary: How to get ROI from digital transformation

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

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

About the authors

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

Executive takeaways

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

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

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

Purpose-built facilities and automation

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

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

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

Labor costs and operational efficiency

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

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

Market dynamics and vacancy trends

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

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

Key site selection factors

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

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

Best practices for supply chain professionals

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

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

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

Looking ahead

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

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

About the author

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

Executive takeaways

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

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

Supplier integration is increasingly strategic

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

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

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

The problem: Satisfaction with SINPD remains low

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

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

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

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

Skill gaps continue to limit SCM contributions

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

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

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

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

Organizational barriers are often the bigger issue

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

Several themes emerged repeatedly in the qualitative responses.

SCM is often viewed as tactical

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

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

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

Silos continue to limit collaboration

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

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

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

Training alone is not enough

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

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

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

How organizations are responding

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

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

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

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

Implications for industry and higher education

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

Organizations may need to:

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

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

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

About the authors

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

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

Executive takeaways

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

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

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

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

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

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

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

Key actions:

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

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

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

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

Key actions:

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

Phase 3: Manage financial risk actively through implementation

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

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

Key actions:

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

Phase 4: Lock in financial value after go-live

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

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

Key actions:

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

Phase 5: Build capability for continuous transformation

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

Key actions:

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

Finance belongs at the center

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

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

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

About the author

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

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

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

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

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

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

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

SPEAKERS:

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

Executive takeaways

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

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

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

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

Investment is high, but clarity is not

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

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

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

Technology alone is not the differentiator

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

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

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

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

The rise of the “value blueprint”

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

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

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

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

AI success starts with the problem

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

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

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

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

Workforce reinvention

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

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

The foundation problem

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

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

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

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

Supply chain’s opportunity

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

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

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

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

The bottom line

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

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

Executive takeaways

Part 1: The room goes quiet

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

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

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

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

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

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

It is all about AI literacy

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

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

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

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

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

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

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

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

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

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

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

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

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

Part 3: Building the app—what I actually learned

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Learning 4: Iteration beats specification—in outcome and speed

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

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

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

The scenario calculation for the exec S&OP meeting

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

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

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

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

Closing remarks

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

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

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

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

About the author

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

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

Executive takeaways

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

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

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

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

Existing regulations

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Strategic actions for supply chain leaders

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

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

Seizing competitive advantage

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

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

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

About the authors

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

Executive takeaways

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

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

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

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

Operational evolution

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

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

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

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

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

Ready to scale

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

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

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

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

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

Cross-functional collaboration

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

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

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

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

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

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

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

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

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

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

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

Executive takeaways

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

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

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

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

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

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

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

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

The 2026 agenda is organized around four industry focus areas:

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

Confirmed speakers include:

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

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

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

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

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

Executive takeaways

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

From business requirements to working prototypes

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

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

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

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

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

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

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

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

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

Why domain expertise matters

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

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

The difference between MVPs and enterprise systems

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

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

Building a culture of experimentation

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

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

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

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

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

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

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

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

Executive takeaways

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

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

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

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

The 14-day advantage: Anchoring the data

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

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

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

The executive checklist: Real-world applications

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

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

Implementation: The 12-week pilot

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

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

Conclusion: The Monday morning reality

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

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

About the authors

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

Executive takeaways

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

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

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

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

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

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

Financial Drag =V ×W ACC× Daysexposure365=

=100 000 ×0,15× 91365 ≈USD 3,740

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

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

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

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

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

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

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

The math of density: A comparative logic flow

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

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

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

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

Result: $121.80 per ton.

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

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

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

Tactical surcharge: $100 overweight fee for road transport.

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

Result: $83.20 per ton.

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

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

∆Cost=Conventional Model- Sharapova FrameworkConventional Model =

= 6,090-4,1606,090 ≈31.7% Savings

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

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

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

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

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

The self-financing loop

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

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

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

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

0.5 ×Q ×1+M≥Qpayable

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

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

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

From reactive logistics to financial dominance

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

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

Final thought: The velocity of the dollar

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

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

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

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

Exeutive takeaways

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

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

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

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

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

Technical footwork and the safety net

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

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

Purposeful transparency and the value proposition

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

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

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

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

Setting the stage to finding your rhythm

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

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

About the authors

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

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

Executive takeaways

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

Introduction

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

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

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

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

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

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

Why the spreadsheet lies

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

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

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

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

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

Introducing the Volatility‑Adaptive Automation Portfolio (VAAP)

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

Pillar 1: The Volatility Index (VIX‑SC)

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

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

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

Pillar 2: The Flexibility Score (Δ‑auto)

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

Table 1: Δ‑auto calculation checklist

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

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

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

Pillar 3: The Dynamic Rebalancing Rule

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

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

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

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

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

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

Figure 1

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

The financial innovation: Automation swaps

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

Figure 2

Figure 2: How an Automation Swap Works

Implementing VAAP: A four‑step roadmap for CFOs

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

Why this matters now

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

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

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

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

References

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

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

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

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

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

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

Executive takeaways

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

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

Volatility is reshaping global supply chains

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

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

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

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

The ripple effects of disruption

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

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

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

AI: Separating hype from value

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

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

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

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

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

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

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

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

Planning in an unpredictable world

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

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

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

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

From data to decision-making

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

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

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

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

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

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

Executive takeaways

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

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

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

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

From excitement to skepticism

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

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

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

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

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

Flexibility becomes the new requirement

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

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

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

That includes:

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

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

The rise and reality of digital twins

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

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

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

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

Execution is the risk

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

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

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

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

Automation doesn’t stop at the four walls

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

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

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

The bottom line

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

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

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

Executive takeaways

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

“What is missing is we lack coordination,” he Supply Chain Management Review during an interview at the recent Gartner Supply Chain/Xpo Symposium in Orlando. “What we [need to] do is the coordination and synchronization across systems, across partners, across the SLAs and how to deliver on it.”

More platform providers are moving to an end-to-end approach, and Cleo is no different.

From data to context

At its core, Cleo’s approach is built on aggregating data from across what Rajasekharan calls the “multi-enterprise supply chain”—customers, suppliers, logistics providers, and internal systems—and transforming it into a real-time operational view.

“We essentially get disparate information across the multi-enterprise supply chain… and we bring it all together,” he said.

But aggregation alone isn’t the differentiator. The key, he argues, is building context. A context layer continuously ingests signals from orders, shipments and inventory and interprets them within the broader flow of an active transaction. Instead of analyzing performance after the fact, the system identifies risk while orders are still in motion.

“You’re in the middle of an active fulfillment … and in the middle of an active cycle, you identify what the risks are,” he explained.

Moving from reactive to proactive execution

That shift from retrospective analysis to real-time intervention is central to true visibility. Today, too many companies operate in what Rajasekharan described as a reactive model where issues surface only after they have already impacted performance.

“Most people live on the concept of deductions and chargebacks that hit them after the fact,” he said, noting that the alternative is a proactive model where supply chain teams can anticipate disruptions and act before they cascade into penalties, missed service levels, or strained customer relationships.

“We’re shifting the world from the reactive supply chain to proactive management where you’re essentially seeing these signals and making sure you’re not violating your performance metrics,” he said.

Why context matters more than connectivity

The industry has made significant progress in connecting systems such as ERPs, WMS, and TMS, but Rajasekharan argued that connectivity without context still leaves companies struggling to act effectively.

“Right now, humans are chasing information across different systems and trying to put it together,” he said. “Almost always they miss things.”

A unified data model offers what Rajesekharan describes as “process choreography”—an understanding of how orders, shipments, and transactions should flow across the supply chain.

“We know an order is an object. We know inventory as an object, and based on signals, we combine deterministic and probabilistic data,” he said. That combination allows the system to not only identify what has happened, but also predict what is likely to happen next and prioritize which issues require immediate attention. The most critical information is funneled to a human for decision-making.

The financial impact of inaction

While the technology conversation often centers on operational efficiency, Rajasekharan put in into financial terms. He pointed to industry estimates suggesting that supply chains lose trillions annually due to inefficiencies in what he called the “informational supply chain.”

“Any typical shipper loses between 2% to 5% in chargebacks and short pay,” he said.

For a $1 billion business, that translates to $20 million to $50 million in lost revenue. “Two to 5% essentially takes your profit margin down a third to a half,” he added.

Beyond direct financial losses, poor execution can also limit growth opportunities, particularly in retail environments where supplier performance directly influences expansion potential.

Where AI fits—and where it doesn’t

In a market saturated with AI-driven solutions, Rajasekharan took a more nuanced stance on the role of artificial intelligence. He said that 95% of the time, the supply chain works just fine, but it’s that 5% where AI offers tremendous value. Cleo is using AI to manage exceptions by identifying disruptions, prioritizing actions, and enabling faster recovery.

At the same time, he cautioned against overusing AI where simpler approaches would suffice.

The next phase: orchestration

Rajasekharan framed Cleo’s broader vision as part of an emerging category: supply chain orchestration. To achieve more autonomous operations, he outlined a layered approach that goes beyond integration.

“You need integration but what is missing is a context layer and an intelligence layer before you put agents on top,” he said.

Without that foundation, he warned, many AI-driven solutions will struggle to handle the complexity of real-world supply chains, particularly the exceptions that drive most operational risk.

“The trick is in the exceptions,” he said.

Supply chains are not short on data. They are not short on AI solutions. But they continue to be short on visibility. That happens when data, AI and execution are coordinated into a context-driven solution.

AI will help organizations get there, but it still needs human guidance.

Executive takeaways

Every supply chain disruption starts the same way. Something changes faster than the operation can respond. Freight arrives early. A carrier cancels. A port backs up. A seasonal spike exceeds every projection. And a warehouse or operations manager who was running a tight operation yesterday is now in reaction mode, making expensive decisions under pressure with limited time.

What happens next determines whether that disruption becomes a manageable event or the beginning of a cycle that is very hard to break.

After years of working with warehouse and operations managers across retail, automotive, manufacturing, and distribution, I have observed that most supply chain problems at the operational level trace back to one of four root causes. I call them pressure points. They are not new phenomena. But the frequency and severity with which they are now occurring has changed the stakes considerably for managers who have not yet built operations designed to flex under pressure.

This framework is not a cure for macro disruption. It is a diagnostic tool. Its value is in helping managers identify which specific failure mode is driving their problem before they apply a solution because the most common and most expensive mistake in supply chain operations is solving the wrong problem with confidence.

Pressure point one: Space pressure

Space pressure is the most visible of the four and the most frequently misdiagnosed. When a warehouse manager runs out of room, the instinct is to frame it as a square-footage problem and pursue a square-footage solution—a new lease, an additional facility, a building expansion. That framing is often wrong.

Space pressure is more accurately described as a flexibility problem. The question is not whether you have enough square footage in aggregate. It is whether you have the right capacity, configured correctly, available at the point in time when you need it. A facility with 80,000 square feet can still experience acute space pressure if its dock capacity, racking configuration, or aisle layout cannot accommodate the specific freight profile arriving in a given week.

The more fundamental issue is structural. Most warehouse infrastructure is designed around average or projected volume. It is sized for what management expects, not for what actually arrives. In an environment where demand swings of 30% to 40% within a single quarter are no longer unusual, infrastructure sized for expectations will be wrong in both directions—over-capacity during slow periods and under-capacity during peaks.

The managers who navigate space pressure most effectively have made a conceptual shift: they treat their fixed infrastructure as a baseline layer sized for their average throughput, and they access flex capacity to cover variance above that baseline. This approach does not require owning or leasing more. It requires building access to capacity that can be added and removed as conditions change.

The operational failure mode to watch for: using permanent, long-term solutions to solve variable, short-term problems. A 10-year lease signed at the top of a demand cycle is one of the most common and most damaging errors in warehouse capital planning.

Pressure point two: Flow pressure

Flow pressure is the subtlest of the four and the one most frequently attributed to something else. When throughput slows and operations start to back up, managers typically diagnose the visible symptoms: the dock is congested, so they add dock staff; the floor is falling behind, so they extend shifts; delivery windows are being missed, so they blame the carrier. The diagnosis often misses the actual problem.

Flow pressure occurs when the movement of product through an operation—inbound receipt, staging, storage, retrieval, and outbound dispatch—loses coherence. The components of the operation are working but they are not working in sequence. Inbound arrives faster than it can be unloaded. Staging areas fill before putaway can keep pace. Outbound builds up because empty capacity is not available at the right dock at the right time.

The diagnostic question that most reliably identifies flow pressure is this: is your team working harder than usual while your throughput is declining? If the answer is yes, you almost certainly have a flow problem. Effort is not the constraint. Movement is.

If your team is working harder than usual while throughput is declining, effort is not the constraint. Movement is.

A Tier 1 automotive supplier facing a production spike illustrates the point. Their existing dock and staging configuration could not keep pace with the volume of inbound parts required to maintain assembly line schedules. Adding labor would not have solved it. The constraint was not the number of people. It was the point at which parts transitioned from inbound transport to production-ready staging. Addressing that specific handoff—the actual bottleneck—resolved the flow problem without a significant labor investment.

The operational failure mode to watch for: applying solutions upstream or downstream of the actual constraint. Adding labor, technology, or capacity at the wrong point in the process is expensive and ineffective. The discipline required is to locate the actual point of constraint before committing resources.

Pressure point three: Cost pressure

Cost pressure in warehouse operations almost always traces back to a structural mismatch between the variability of revenue and the rigidity of the infrastructure that generates it. This is not a P&L management problem. It is an asset strategy problem.

Most warehouse operations are built on a cost structure that made sense at a specific volume level at a specific point in time. Leases were signed. Equipment was purchased. Headcount was hired. Those commitments were rational when volume was predictable. When volume becomes variable—and for most operations it now is—those fixed commitments become a persistent drag in low periods and an inadequate foundation in high ones.

The math is unforgiving in both directions. During slow periods, the operation carries overhead for capacity it cannot fill. During peak periods, the fixed base is insufficient and the cost to access incremental capacity on short notice is high. The average of two bad outcomes is still a bad outcome.

A manufacturing operation's experience with overseas container detention fees illustrates the cost structure problem in concrete terms. When parts shipments became erratic, incoming containers were sitting and accruing detention fees at rates that compounded quickly across multiple containers over multiple weeks. The cost per container per day was more than 25 times the per-day equivalent of the alternative storage cost available to them. The savings from restructuring that one cost element ran into six figures within a matter of months—not from a capital project or a renegotiation, but from identifying a mismatched cost structure and correcting it.

The operational failure mode to watch for: treating cost pressure as a rates problem when it is a structure problem. Renegotiating vendor rates on a fixed-cost model yields incremental savings. Restructuring the ratio of fixed to variable costs in the underlying model yields structural improvement.

Pressure point four: Resilience pressure

Resilience pressure is distinct from the first three in an important way. The first three pressure points represent operational conditions that can be measured, tracked, and addressed proactively. Resilience pressure is a latent condition—it is not visible until the disruption arrives, at which point the organization discovers how prepared it actually was.

The diagnostic question for resilience is straightforward: how many single points of failure does your operation have? A single point of failure is any dependency—a carrier, a facility, a vendor, a system, a key employee—whose failure would move the operation from functional to crisis within 24 hours. Most operations have more of these than their managers realize, because the dependencies were never catalogued. They accumulated over time as the operation optimized for efficiency rather than flexibility.

Efficiency and resilience are in tension. An operation optimized purely for efficiency eliminates redundancy by definition. The most efficient supply chain is a single thread. A single thread breaks.

The contrast case is instructive. Operations that recover from disruptions quickly almost always share one characteristic: they have already tested their contingency options before they needed them. They have used their backup carrier on a routine shipment. They have accessed their flex capacity provider during a moderate volume peak. They have walked through their recovery scenario in a planning session rather than in a live crisis. The difference between paper resilience and operational resilience is practice.

The operational failure mode to watch for: confusing a documented contingency plan with actual resilience. A plan that has never been tested against reality is a hypothesis, not a capability.

Applying the framework

The Pressure Point Framework is most useful as a diagnostic discipline rather than a remediation checklist. The value is not in knowing that these four conditions exist—most experienced supply chain practitioners recognize them. The value is in using them as a structured lens before committing to a solution.

In practice, that means asking four questions before acting on any significant supply chain problem. Is this a space problem, a flow problem, a cost structure problem, or a resilience problem? Which of the four is the primary driver? Are multiple pressure points compounding, and if so, which one is upstream of the others? And is the solution I am considering addressing the root cause or the visible symptom?

The most common pattern I observe is that space pressure and flow pressure are the presenting symptoms, while cost pressure and resilience pressure are the structural conditions that make those symptoms worse. An operation with a well-matched cost structure and genuine contingency options will absorb a space or flow disruption with significantly less damage than an identical operation carrying the structural vulnerabilities of the third and fourth pressure points.

Supply chain operations in the current environment are not going to stop encountering disruption. The macro conditions that generate pressure—demand volatility, transportation constraints, global sourcing complexity—are not temporary. What is within the control of warehouse and operations managers is the structure of the operation they are running and its capacity to absorb pressure without converting it into crisis. That is the work the Pressure Point Framework is designed to support.

About the author

John Brooks is the CEO of Warehouse on Wheels, an on-demand mobile industrial storage and logistics solutions company operating across more than 37 locations in the United States, Canada, and Mexico. Before founding the company, Brooks worked as a warehouse manager and logistics operator. He can be reached via email at jbrooks@warehouseonwheels.com

Executive takeaways

EDI (electronic data interchange) remains a top technology offered by 3PLs (third-party logistics) providers as a service to their customers. For shippers who are retail or grocery vendors, EDI and barcode labeling are the two critical supply chain technologies that are not negotiable in terms of capability and reliability.   

Whether you are on the lookout for a 3PL fulfillment partner, or perhaps your company already has one or more 3PL partners, EDI ability should be at the top of your requirements list. You did your due diligence (or you think you did) when you asked about the EDI capability of the 3PL when you were interviewing them prior to signing the contract. Are you satisfied with the EDI service your 3PL is providing? If not, perhaps you didn’t go far enough with your initial inquiry. Not all 3PLs have the same EDI capabilities.

Is their EDI an embedded part of their warehouse and fulfillment software or integrated to it?  Does your 3PL outsource its EDI, or does it develop EDI software in-house? How dedicated to your 3PL is this outsourced partner, and are they focused on just EDI or are they also maintaining other software such as the warehouse management system? Remember: in supply chain, it’s also about knowing your supplier’s supplier. If your 3PL outsources its EDI, find out from who and research the company. (How far backlogged is the in-house EDI team if there is one?  Where are they located?  And what’s the backlog of the outsourced team too?) There’s nothing wrong with outsourcing software—it’s all the fashion these days—and there’s also everything right with making sure you fully know the expectations and risks before you sign the contract.

Depending upon how you and your 3PL intend on exchanging transactions, you may need your 3PL to send the EDI856 ASN (Advance Ship Notice) to your customers on your behalf. The industry requirement for retail and grocery is that the EDI856 ASN should be sent within one hour of the shipment leaving the facility. The question here is: can the 3PL send the EDI856 and make it look like it came from your company? This “spoofing” (normally a term associated with fraud, I’m just putting it to other use here) of the communication identifiers by a 3PL to make the EDI transaction look like it was sent by its customer and not the 3PL is an EDI capability not all 3PLs have. If this is something that you need, make sure you inquire about it upfront. If your 3PL cannot do this, you’ll need an EDI provider to get involved unless you have your own EDI software and expertise in-house.

Selecting the right 3PL is a significant task. One must examine the 3PL’s locations, operations, and technologies. This requires more than just ticking boxes on a checklist: there has to be deeper considerations for each and every requirement. Sometimes, a technical capability will be associated with an operational functionality and the two must be considered together. EDI isn’t just one of those requirements that can be ticked off as a “Yes” or “No”.

Ultimately, your choice of 3PLs will directly impact your ability to execute.  Your customers don’t want to deal with a disruptive vendor, and they don’t care about your 3PL problems.  To stay competitive, ensure that you’ve partnered with a 3PL you know and can hopefully grow with.        

Executive takeaways

Wholesale inflation is flashing a warning sign for supply chain leaders. U.S. producer prices rose 1.4% in April from the prior month, the largest monthly increase since March 2022. Meanwhile, energy prices are projected to surge at least 24% this year, and prices for base metals including aluminum, copper and tin are also expected to reach all-time highs.

At the same time, demand remains uneven. Some sectors are slowing, while others, especially those tied to AI infrastructure, data centers, electrification and renewable energy, continue to pull hard on materials, power and logistics capacity.

That leaves CSCOs on the front line of a second-half cost challenge. According to Gartner research, 71% of CSCOs are focused on controlling costs in the short term, yet only 21% are confident they can deliver against cost management expectations over that same horizon.

Today, many CSCOs see cost management as a delicate dance of managing relationships with peers and expectations from executive leadership. They do their best to build consensus by being accommodating to preserve goodwill. Those instincts matter. No supply chain leader succeeds by alienating peers. In the current market, however, consensus alone can become a polite route to poor outcomes.

Gartner research points to a more durable model: the assertive advocate CSCO. These leaders collaborate with their peers, while also building process guardrails and demonstrating the P&L impact of different business decisions. Assertive advocates are more than twice as likely as consensus-builders to drive sustained cost excellence.

For CSCOs facing historic cost increases, the objective is clear: sustained cost excellence requires more than influence. It requires challenging unrealistic cost targets, reshaping decisions before costs are locked in, and making trade-offs visible before the business pays for them.

Why consensus breaks down under pressure

The consensus-builder model often fails because it accepts the cost target before the trade-off has been understood. Sixty-one percent of consensus-builder CSCOs accepted aspirational or aggressive CFO-set targets in the past 12 months to be a “team player,” compared with only 21% of assertive advocate CSCOs, according to a Gartner survey.

That difference changes enterprise decisions. Consider an industrial manufacturer that depends on aluminum, copper and energy-intensive components. Input costs are rising just as customers are resisting price increases. Commercial leaders may want to preserve every service promise to protect revenue, while finance pushes for lower working capital and operations is asked to reduce inventory or consolidate shipments. Each request may be reasonable in isolation. Together, they can create stockouts, missed delivery windows or expedited freight costs that erase the intended savings.

An assertive advocate CSCO forces that discussion into the open. If inventory is reduced, which service commitments become harder to meet? If shipments are combined to reduce transportation costs, which delivery windows stretch? The goal is to make these consequences visible before the decision is made.

Cost authority starts with the P&L

A CSCO cannot credibly challenge a cost target with operational anecdotes. They must make this argument in the language the CEO and CFO already use: the P&L. To drive impact, CSCOs need to show their contribution beyond cost avoidance by connecting performance to margin.

For example, a supply chain organization managing ocean freight this year may not be able to claim a simple year-over-year reduction if rates remain elevated by fuel surcharges, route disruption or capacity uncertainty. It can separate finance-recognized savings from avoided costs though.

If, for example, comparable shipping lanes become more expensive because of bunker fuel, insurance or Red Sea-related disruption, but the company’s negotiated rates increase less than the market, the CSCO can show how procurement and network decisions helped protect margins.

Put supply chain expertise where decisions happen

Many cost problems stem from areas outside of the supply chain function’s direct control. A sales team may promise expedited delivery to hold revenue in a softening segment. A product team may approve a design that increases exposure to copper or aluminum at an inopportune point in the commodity cycle. A regional business may commit to a supplier that looks cheaper on unit price, but requires longer lead times, higher safety stock or more expensive transportation. By the time supply chain absorbs the cost, the decision has already hardened into a customer promise, product spec or sourcing commitment.

Assertive advocacy inserts supply chain knowledge directly into these cross-functional decision points. An organization could create a rule for new customer contracts: any delivery model that requires nonstandard fulfillment must be reviewed against transportation and inventory implications before the deal is approved. The value is a guardrail that prevents hidden costs from being priced too late.

The fear among CSCOs is that assertiveness will add burden to teams already stretched thin. Gartner’s research suggests the opposite can happen when guardrails are designed well. Decision tools and scenario models can reduce firefighting by helping teams see trade-offs earlier.

The next phase of cost leadership

As cost pressures build heading into the second half of the year, CSCOs will need to do more than manage costs. They will need to help protect profits before rising input, freight and energy costs erode margins.

The assertive advocate CSCO brings evidence, decision discipline and P&L visibility into the enterprise before costs are locked in. That posture may be the difference between absorbing the next cost shock and preventing it from becoming a margin crisis.

Benjamin and other Gartner analysts are providing further analysis on this topic at the Gartner Supply Chain Symposium/Xpo, taking place this week in Barcelona, Spain, May 18-20. Follow news and updates from the conferences on X using #GartnerSC.

About the author

Benjamin Jury is a director analyst for Gartner’s Supply Chain Practice. He leads and contributes to research projects that address chief supply chain officers’ (CSCOs’) key priorities.

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 Bengaluru, Manipal Academy of Higher Education, Manipal, 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?