Executive takeaways

Supply chain AI is generating significant attention, and for good reason. Demand forecasting, supplier risk scoring, logistics optimization, inventory positioning, and procurement automation all benefit from the pattern-recognition and prediction capabilities that modern AI systems bring. Most of the early returns are real.

What is less visible is the infrastructure that AI supply chain systems actually run on. Not the models, which receive most of the investment and attention. The data: supplier identities, product and part attributes, service-level metadata, exception taxonomies, and the ownership structures that determine which system is authoritative when two sources disagree. When AI agents begin acting on that data in real time, its quality stops being an operational inconvenience and becomes a strategic risk.

The planning-execution gap that AI makes visible

Supply chain AI is often deployed in one of two zones. Planning zone systems optimize demand, inventory, and sourcing decisions. Execution zone systems handle order management, warehouse operations, transportation, and supplier communication. The gap between them, where planning assumptions meet execution reality, is where AI failures tend to be most consequential.

A planning model can optimize against supplier lead times that have not been updated in the supplier portal for six weeks. An AI-generated purchase order can rely on a pricing agreement that was superseded by a spot-market negotiation no one updated in the system of record. A demand recommendation can treat a discontinued product variant as active because the lifecycle flag was never closed in the product master. In each case, the model is doing exactly what it was designed to do. The problem is the data it was given.

Five governance checkpoints for agentic supply chains

Before expanding AI autonomy in supply chain operations, organizations should establish five governance checkpoints. These are not a compliance exercise. They are the infrastructure that determines whether an AI agent operating in a supply chain environment makes decisions the business can actually stand behind.

Supplier identity and hierarchy. AI agents working across procurement, logistics, and fulfilment need to operate from a single, authoritative supplier master. That master should include parent-subsidiary relationships, approved trading entity identities, site-level capabilities, and compliance certifications. When the agent evaluates a supplier, qualifies a new source, or escalates a risk flag, it needs to know it is looking at a complete, current, and authorized record. If the supplier master has duplicates, stale records, or unresolved merges from an acquisition, the agent will operate on that ambiguity at machine speed.

Product and part attribute ownership. AI-driven procurement and fulfilment decisions depend on accurate product and part data: dimensions, materials, specifications, compatibility, country of origin, compliance classifications, and technical substitution rules. In many organizations, this data is distributed across product lifecycle management systems, ERP, supplier portals, and engineering databases, with no clear owner for each attribute at the point of an AI decision. Before deploying agents that act on this data, the organization needs to assign an accountable owner for each attribute class, define a freshness standard, and specify what the agent should do when the data is missing or in conflict.

Service-level and constraint metadata. AI-driven scheduling, allocation, and logistics decisions require more than capacity numbers. They require constraint metadata: which lanes are currently disrupted, which suppliers have active quality holds, which SKUs are subject to allocation restrictions, which distribution channels have priority during a shortage. When that metadata is incomplete or stale, the AI agent will allocate capacity it does not have, commit lead times it cannot meet, and create downstream exceptions that require expensive human correction.

Exception taxonomy. Agentic supply chains will generate exceptions. The question is whether those exceptions are classified, routed, and resolved in a way that improves the system or simply managed manually until the next occurrence. Before deploying agents, organizations should define an exception taxonomy: what categories of failure are possible, what the escalation path is for each, who owns resolution, and how resolved exceptions feed back into the agent’s decision parameters. An exception that becomes a private workaround is lost learning. An exception that becomes a classified, routed, resolved record improves the system over time.

Recovery ownership. The final checkpoint is the clearest test of supply chain AI maturity: who owns the outcome when the agent is wrong? Not who is notified. Not who writes the incident report. Who is accountable for returning the affected supplier relationship, the affected order, or the affected inventory position to its correct state, and who is responsible for preventing the same failure from recuring? If that ownership is unclear before the agent is deployed, it will be unclear when the agent fails, which is a much more expensive time to figure it out.

A useful framing for supply chain leaders

The most practical question for a supply chain leader evaluating an AI initiative is not whether the model can make better decisions than a human planner. In many cases it can, under good conditions. The useful question is: what conditions does this model actually need to perform reliably, and can we guarantee those conditions in production? That is not a technology question. It is a data and process governance question, and it belongs at the beginning of the AI initiative, not at the postmortem after the first significant failure.

Supply chain AI will deliver its most durable value to organizations that treat data ownership, exception management, and recovery design as infrastructure investments on the same level as model selection and integration architecture. The model is the capability. The data governance is the foundation it runs on.

About the author

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

Executive takeaways

When Jackie Zhu took over Schneider Electric’s North American Supply Chain earlier this year, he stepped into one of the industry’s most demanding leadership roles. Schneider Electric has topped Gartner’s Supply Chain Top 25 rankings for four consecutive years, earning global recognition for operational excellence, innovation and execution.

For Zhu, the promotion wasn’t simply the culmination of more than two decades at Schneider Electric. It was the product of a career deliberately built across nearly every major function inside the business—from sales and procurement to logistics, industrialization and research and development.

“I’ve [not] been in this role not very long, around five months,” Zhu told Supply Chain Management Review. “There’s been no honeymoon. There are many challenges, but many achievements as well.”

Now serving in the role of Senior Vice President, North America Supply Chain Officer, Zhu believes his broad career experiences are exactly what is setting him up for success in his new role.

Building a career across the business

Many supply chain leaders spend their careers mastering a single discipline before moving into executive leadership. Zhu intentionally did the opposite.

Over 23 years at Schneider Electric, he has led procurement, strategic sourcing, logistics, industrialization, global supply chain strategy and research and development. Most recently, he led supply chain for the Power Products division, overseeing both R&D and global supply chain strategy for products supporting data centers, hospitals, airports and other mission-critical infrastructure.

His first professional role, however, wasn’t in supply chain at all. It was sales. Looking back, Zhu credits those early years with shaping how he approaches leadership today.

“I learned how important it is to listen to customers, how challenging it is to win business, and how important supply chain is in supporting customers and the business,” he said.

The career move that changed everything

For much of his Schneider career, Zhu considered himself a procurement professional.

He spent more than a decade in procurement, eventually leading sourcing operations across China. Then Schneider Electric approached him with an unexpected opportunity: become vice president of logistics.

He almost declined.

“To be frank, I was really hesitant,” Zhu recalled. “I’d worked in procurement for so many years.”

Senior leaders encouraged him to think differently.

“They came to me and said, ‘We have trust in your mindset, your leadership and your ability to drive transformation,’” Zhu said.

Their advice resonated.

“What you’ve learned in procurement wouldn’t be lost,” they told him. “What you learn in logistics will broaden your vision, your experience and your career.”

The experience fundamentally changed his perspective on leadership.

“I proved that when you move into a totally new domain, you learn much faster. Your agility and resilience become much higher,” Zhu said. “After that, I told myself that if someone asked me to take on something where I had no experience, I wouldn’t hesitate at all.”

Every decision starts with the customer

Ask Zhu what has remained constant throughout his career, and the answer comes quickly. Everything begins with the customer.

“I always tell myself and my team that every decision we make needs to be around the customer,” he said. “It can be an external customer or an internal customer.”

That philosophy extends throughout Schneider Electric’s operations. Factories serve other factories. Procurement serves manufacturing. Logistics serves both internal partners and end customers.

“The key is asking whether we’re creating value for the customer and improving customer satisfaction,” Zhu said. “That’s the ultimate goal and the ultimate principle that helps us make decisions.”

Leading the industry’s benchmark North American supply chain

Stepping into leadership of Schneider Electric’s North American supply chain means balancing operational excellence with continuous transformation. For Zhu, that means advancing Industry 4.0 initiatives, expanding artificial intelligence and building greater end-to-end visibility across the business while maintaining the operational discipline that earned Schneider Electric its reputation.

“We need to continue the transformation using AI, IoT and data so we have end-to-end traceability and end-to-end visibility across the supply chain,” he said. “That enables us to make decisions in real time.”

Yet Zhu believes technology alone isn’t enough.

Leadership requires what he calls “intellectual honesty.”

“We need to be much more open,” he said. “Sometimes we need to have intellectual honesty—to admit where we still have gaps and how we can learn from inside the organization, from the market, and even from startups.”

He also believes organizations often undermine themselves by trying to accomplish too much at once.

“If you have 10 or 20 priorities, it’s difficult to align the organization,” Zhu said. Instead, leaders should focus on “the top three or five priorities” that matter most to customers and the business.

Designing resilience into the supply chain

Like many supply chain executives, Zhu spends significant time thinking about resilience. Unlike many others, he believes resilience begins before a disruption ever occurs.

“We call it resilience by design,” he said.

Rather than reacting to disruptions, Schneider Electric incorporates resilience into network design decisions—from plant locations and supplier strategies to transportation flows and distribution center placement.

Artificial intelligence and end-to-end visibility then enable faster responses when disruptions occur. Zhu described one example in which damaged freight automatically triggered an AI-supported recommendation to fulfill the order from another warehouse, allowing Schneider Electric to replace the shipment within 24 hours without disrupting the customer.

Sustainability through visibility

The same philosophy recently earned Schneider Electric national recognition.

Earlier this year, the company received the 2026 U.S. Department of Energy Better Practice Award for scaling circularity across five pilot sites and is currently working to expand that effort to more than 20 North American sites. The initiative uses digital technologies to improve visibility into material flows, reduce waste and converse resources and strengthen operational resilience.

According to Zhu, the company applies the same technologies internally that it delivers to customers.

“We deploy the same approach we deliver to our customers—to electrify, automate and digitize—so they can achieve higher productivity, improve efficiency, reduce waste and modernize infrastructure,” he said.

The initiative demonstrates how greater visibility into materials and inventory can uncover opportunities to extend product lifecycles, reduce waste and improve resource utilization at scale. 

A leader still learning

Despite leading one of the world’s premier supply chain organizations, Zhu insists the work is never finished.

“It’s a great responsibility,” he said. “I consider it positive pressure because it pushes us toward continuous improvement, continuous learning and keeping an open mind to transform our supply chain.”

Looking back, Zhu believes the willingness to leave his comfort zone—not mastering any single function—prepared him for the role he holds today.

It’s a lesson he now hopes to pass along to the next generation of supply chain leaders: the best executives aren’t defined by the number of years they spend in one discipline, but by the breadth of perspectives they develop across the business.

Executive takeaways

Editor’s note: This article first appeared on the Digital Supply Chain Institute website and is being republished here with permission. You can read the original article here.

Supply chains have always evolved in response to new technologies, market disruptions, and changing customer expectations. Today, artificial intelligence (AI) and agentic AI represent another significant inflection point. These capabilities are no longer experimental; organizations are actively deploying them to improve planning, operations, and decision-making across the supply chain.

The challenge, however, extends beyond technology adoption. AI is changing how work is performed, how teams are organized, and which skills create value. Organizations that view AI solely as a software investment risk overlooking its broader implications for workforce strategy and talent development. AI is reshaping supply chain roles, workforce requirements, leadership responsibilities, and talent strategies—and what organizations should do to prepare.

AI's Impact on Supply Chains

The scale of investment in AI demonstrates the seriousness with which organizations are approaching this transformation. Gartner forecasts that the AI-enabled supply chain software market will reach $53 billion by 2030, with 60% of enterprises using AI-enabled supply chain management tools, up from just 5% in 2025.

Yet the more pressing issue is not investment itself; it is workforce readiness.

Many organizations report confidence in their AI strategies while simultaneously identifying significant skills shortages. This reflects a familiar pattern seen in previous technology shifts: companies are acquiring capabilities faster than they are developing the talent required to use them effectively. AI readiness and talent shortfalls can exist at the same time. While 83% of organizations describe themselves as AI-ready, according to Gartner, 47% identify AI skills as their largest workforce capability gap, highlighting a growing disconnect between technology ambition and talent preparedness.

The labor market further compounds the challenge. Demand for AI-skilled workers substantially exceeds available supply, and employees with AI-related expertise command significant wage premiums. Relying exclusively on external hiring is unlikely to be sustainable. Developing internal talent, redesigning roles, and creating new learning pathways will increasingly become strategic necessities.

The Transformation of Roles and Organizational Structures

AI is changing supply chain work from the operational level upward, particularly by automating routine activities that historically justified entire tiers of the organization--analyst, coordinator, and managerial roles.

At the operator and analyst level, AI increasingly performs tasks such as inventory monitoring, exception detection, route analysis, and data preparation. Human workers remain essential, but their responsibilities are shifting toward judgment, oversight, and decision-making.

Examples from manufacturing illustrate this transition. BMW's AI-supported quality systems and Siemens' Industrial Co-Pilot are retraining employees to work alongside AI technologies rather than simply replacing them. AI systems can identify potential defects or recommend maintenance actions, while trained employees evaluate recommendations, incorporate operational context, and determine the appropriate course of action.

The managerial layer may experience some of the most significant changes. AI is increasingly absorbing reporting, coordination, and information-processing activities that once occupied substantial portions of managerial work. The managers who create the greatest value in the future will lead mixed human-AI teams, establish governance frameworks, manage exceptions, and make decisions that require organizational judgment and accountability.

The opportunity is not to eliminate management but to redesign managerial roles around capabilities that technology cannot easily replicate: context, trust, trade-off decisions, and leadership during uncertainty.

Women, Talent Pipelines, and Workforce Equity

The workforce impact of AI will not be distributed evenly across organizations.

Women remain heavily underrepresented in executive leadership positions in supply chain organizations. At the same time, research consistently highlights the importance of capabilities such as judgment under ambiguity, cross-functional collaboration, communication, and change leadership, areas in which many women already demonstrate considerable strengths.

The challenge, therefore, is not one of capability but of access and opportunity.

Organizations must ensure that women are actively included in AI implementation initiatives, leadership development programs, and emerging technical and governance roles. Without deliberate action, companies risk concentrating women in functions undergoing rapid change while limiting participation in the positions shaping future supply chain operations.

Managing this transition effectively is both a workforce issue and a strategic talent imperative.

Building an AI-Ready Culture

Regardless of role or seniority, employee perceptions of AI significantly influence adoption outcomes.

Workforce anxiety, uncertainty about changing responsibilities, and concerns about job security can slow implementation efforts and reduce organizational effectiveness. Studies suggest that employees who view AI as an opportunity rather than a threat are more likely to engage productively with new tools and processes.

Transparent communication about how roles will evolve, what skills will be valued, and how employees will be supported through change is essential. Organizations that actively engage employees in the transition are more likely to realize the benefits of AI investments than those that treat workforce concerns as secondary considerations.

Strategic Actions for Supply Chain Leaders

We recommend four actions for chief supply chain officers and senior leaders.

1. Assess AI's Workforce Impact and Build a Transformation Roadmap

Organizations should evaluate how AI initiatives affect existing roles, identify emerging skill requirements, and map future talent needs. This assessment should extend beyond technology deployment plans to include workforce redesign, succession planning, and human-AI collaboration models. Particular attention should be given to managerial roles, where responsibilities are evolving most rapidly.

2. Develop AI Capabilities at Three Levels

AI capability development must occur across the organization. All employees require foundational AI literacy and an understanding of how AI tools influence daily work. Managers need additional capabilities in governance, change leadership, human-AI team management, and ethical decision-making. Technical specialists require deeper expertise in data, analytics, and AI implementation.

Large organizations such as Amazon have demonstrated the importance of broad-based reskilling investments, moving employees into higher-value roles as automation changes the nature of work. The company announced a $1.2 billion investment to upskill 300,000 U.S. employees by 2025, expanding its original commitment to train 100,000 workers and creating pathways into higher-skilled technical and non-technical.

Organizations should also establish dedicated transition programs for managers whose roles are being redesigned. These programs should focus on AI governance, exception management, workforce leadership, and the development of collaborative operating models that integrate human and machine capabilities.

3. Redesign Early-Career Talent Pathways

Organizations should view early-career talent as a deliberate investment in future capability rather than preserving traditional entry-level roles unchanged.

Many routine activities that once defined entry-level work are increasingly being automated. Companies must therefore create new pathways centered on hands-on learning, critical thinking, experimentation, and human judgment.

The future leadership pipeline will depend on redesigned development experiences that prepare employees to work effectively alongside AI systems from the beginning of their careers. While these investments may involve near-term trade-offs, they remain essential for long-term organizational resilience and capability development.

Programs such as DSCI's TrailblazeHers Leadership Initiative or SMI’s (Strategic Marketplace Initiative) Advancing Women Leaders Program demonstrates how mentorship, practical experience, and exposure to digital transformation topics can accelerate leadership development while expanding participation in emerging opportunities.

4. Build an AI-Ready Culture and Measure Workforce Outcomes

Organizations should communicate clearly about how work is changing, establish expectations for human-AI collaboration, and monitor workforce sentiment throughout implementation efforts. Success measures should move beyond simple technology adoption metrics to include workforce readiness, talent mobility, retention, reskilling outcomes, and business performance improvements.

Leadership accountability is equally important. In most organizations, the AI talent question falls in the gap between the CSCO, the CHRO, and the CEO, each assuming one of the others is leading it. Assign ownership explicitly: the CSCO should define future operating models and workforce requirements. The CHRO should own learning infrastructure and talent development. The CEO must provide strategic commitment, resources, and a clear mandate for transformation.

Conclusions and Recommendations

The organizations that thrive during this transition will not necessarily be those with the most sophisticated AI technologies. They will be those that pair technological advancement with deliberate workforce transformation.

Four priorities stand out.

First, leaders must treat AI as a workforce challenge as much as a technology initiative. Investments in tools without corresponding investments in people will deliver limited returns.

Second, organizations must redesign—not simply preserve—early-career pathways so future leaders develop the skills required to operate in AI-enabled environments.

Third, companies should ensure that women and other underrepresented groups participate fully in AI implementation, governance, and leadership opportunities. Inclusive talent strategies will strengthen both innovation and resilience.

Fourth, middle management will be critical to a successful implementation of AI tools. They should be trained on how to lead mixed AI and human-led teams and how to address concerns held by those within their team.

Over the coming months, supply chain leaders should assess which roles are changing most rapidly, establish clear ownership for workforce transformation initiatives, and invest in the capabilities that will define success in an AI-enabled future. The organizations already doing this are pulling ahead. The window to close that gap is narrowing.

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

AI has created a new supply chain constraint hiding in plain sight: memory.

The same DRAM and NAND components that power laptops, servers, storage arrays, smartphones, smart devices and networking equipment are now being pulled aggressively into AI data centers. As demand surges, memory prices have climbed sharply, putting pressure on enterprise technology budgets and procurement plans.

Gartner research finds that memory prices rose 50% to 200% in the first half of 2026, contributing to PC price increases of 35% to 45% and server price increases of more than 125% in some cases. Recent consumer electronics price moves show how quickly these market dynamics can ripple across the broader technology ecosystem.

The challenge extends beyond rising costs or limited availability. Organizations that move too slowly risk supply disruptions and delayed technology initiatives. Those that overcommit risk being locked into elevated costs as market conditions evolve.

The organizations that navigate this disruption most effectively will be those that balance supply assurance with commercial discipline, protecting critical technology roadmaps while avoiding unnecessary exposure to volatile pricing.

Here are four strategies to build resilience as memory shortages continue to reshape technology sourcing.

1. Forecast before the market moves

Traditional annual planning is too slow for the current market. Hardware quotes can expire in just a few days and configurations (and the inventory) can disappear before approvals are complete.

IT sourcing, procurement, infrastructure and enterprise architecture teams should build rolling 12- to 24-month hardware demand forecasts by category and business priority.

The goal is to create enough visibility to engage suppliers early, build an understanding of which SKUs may become difficult to obtain, and position buyers to capitalize on unexpected short-term availability when it arises. In a volatile market, forecasting is not just a planning discipline; it is a sourcing advantage.

2. Speed up buying decisions

When prices can shift between quote and shipment, delays in internal approvals become a financial risk.

Having preapproved contingency budgets, faster purchase-order authority and clearer escalation paths for constrained hardware categories can make a big difference. Procurement teams need the ability to act when inventory becomes available, rather than waiting through approval cycles designed for stable markets. Establish thresholds for acceptable price changes and require supplier notification of repricing or configuration changes.

In this environment, speed means staying ahead of disruptions, not panic buying. It’s about ensuring internal processes don’t amplify the effects of an already volatile market.

3. Build flexibility into specs

Fixed specifications are brittle in a constrained market.

If a program depends on one exact server, storage or endpoint configuration, it may stall when that configuration is unavailable, withdrawn or repriced. It’s important to focus less on a specific component or manufacturer for commoditized products and more on required outcomes: performance, capacity, resilience, security and lifecycle needs.

Technologists should also review their existing technology footprint and determine whether a refresh is the best option. For example, an organization facing constrained server supply may upgrade memory in existing equipment and postpone a noncritical refresh, preserving capacity while avoiding the cost and delays of scarce hardware.

While memory may also be constrained, upgrading existing equipment often requires sourcing only a single component rather than waiting for an entirely new server build that depends on multiple parts. The smaller physical footprint can also reduce transportation and supply chain complexity.

Organizations may also supplement capacity with older or refurbished equipment when appropriate.

4. Push back on pass-throughs

The memory shock will be priced into virtually everything.

As infrastructure costs rise, SaaS, IaaS and software providers may cite higher operating costs as justification for price increases. Some increases may be legitimate; others may reflect anticipated investments rather than actual customer consumption. Even legacy software providers may face increased hosting or data center expenses that are eventually passed on to customers.

Rather than accepting infrastructure-related price increases at face value, require vendors to demonstrate how higher costs are affecting the products and services being delivered, as it specifically relates to them.

Navigating the AI supply squeeze

AI is reshaping more than digital strategy. It’s driving up costs and creating supply constraints across both the physical and virtual digital supply chain.

The immediate priority is to make the organization more responsive than the market is volatile. This requires organizations to become more proactive in planning and more agile in how they evaluate sourcing and supplier decisions.

The organizations that emerge strongest from this disruption will be those that adapt quickly, limiting financial exposure while keeping strategic priorities on track.

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

About the author

James Smith is a Senior Director Analyst working with IT leaders around sourcing procurement and vendor management (SPVM) related issues in Gartner’s Supply Chain practice.

Executive takeaways

Innovation doesn’t happen in a laboratory. It happens when new ideas solve real business problems.

That philosophy sits at the heart of the 2026 NextGen Supply Chain Conference, where three of the industry’s most innovative technology companies will be recognized during the annual NextGen Supply Chain Solution Provider Awards.

Presented during Friday morning’s general session, the awards recognize solution providers that have demonstrated measurable customer impact through intelligent technologies that improve planning, warehouse execution and operational performance. More importantly, award winners won’t simply accept a trophy—they will share with attendees how they developed their technologies, how customers are deploying them and what lessons other organizations can apply inside their own supply chains.

This year’s winners represent three very different approaches to innovation, yet all focus on helping organizations execute faster, smarter and with greater confidence.

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

Intelligent Transformation Award: Netstock

Netstock earned the Intelligent Transformation Award for helping small and midsize businesses modernize inventory planning through AI-driven decision support.

Integrated with more than 60 ERP systems, Netstock replaces spreadsheet-driven planning with intelligent recommendations that analyze inventory positions, supplier performance, demand signals and operational risk before stockouts or excess inventory occur.

Its Opportunity Engine has generated more than 1.4 million inventory recommendations representing more than $20 billion in potential inventory value across its customer base.

Conference attendees will hear directly from Barry Kukkuk, co-founder and chief technology officer, who will discuss how AI can help lean planning organizations make enterprise-quality decisions without requiring enterprise-sized staffs.

Autonomous Operations Award: Pickle Robot

Warehouse unloading has long been one of distribution’s most labor-intensive and physically demanding jobs.

Pickle Robot is changing that.

Winner of the Autonomous Operations Award, Pickle Robot developed an autonomous unloading system that can be deployed within minutes, integrates with existing conveyor infrastructure and requires no warehouse management system modifications.

The technology has already unloaded more than 25 million pounds of freight while helping organizations including UPS, Yusen Logistics, Ryobi Tools and Randa Apparel improve productivity, increase throughput and reduce workplace injuries.

At NextGen, Pete Blair, vice president of product and marketing, will explain how autonomous robotics are moving beyond pilots into full-scale production environments.

More than an awards ceremony

Unlike many industry awards programs, the NextGen Supply Chain Awards are designed to educate as much as recognize.

Each winning organization shares practical lessons learned, implementation strategies and measurable customer outcomes, giving attendees actionable ideas they can apply inside their own operations.

The awards complement a conference program featuring executives from organizations including Wayfair, Eli Lilly, Tractor Supply Company, Apple, Amazon, Stanford Medicine, Target, DP World, Fanatics, Evonik and many other industry leaders. Across keynote presentations, fireside chats, panel discussions and interactive Small Group Sessions, attendees will explore artificial intelligence, automation, digital transformation, workforce development and operational execution through real-world case studies.

Sponsorship opportunities continue to fill

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

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

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

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

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

Join us in Nashville

The 2026 NextGen Supply Chain Conference will take place October 21-23 at the W Nashville in downtown Nashville, Tennessee.

Early-bird registration is now open, giving supply chain, logistics, procurement and operations leaders access to three days of executive education, networking and practical learning.

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

Executive takeaways

There is little debate that trucks are critical in moving the goods that we need across the final pathways of supply chain journeys that often begin with air, sea, and rail transportation. Truckers face long hours behind the wheel, and don’t have enough safe places to pull over and park for rest stops as I reported in one of my own newsletters several years ago. Truck drivers really do deserve to be paid appropriately for the vital service that they provide.  

So, it seems logical that if trucks were allowed to be larger and haul more freight that more goods would be able to get to where they need to be with the same number of truck drivers.  One concern is whether these larger trucks would be safe in traffic and be able to maneuver through the streets like the current-sized trailers.

The CABT (Coalition Against Bigger Trucks) is working against proposals—such as those in Congress—to allow trucks to be any bigger out of concern that larger trailers would cause more damage to the nation’s roads and bridges infrastructure.

According to the 2025 Infrastructure Report Card by the American Society for Civil Engineers, the nation’s roads and bridges do not grade very highly. In fact, the nation’s roads received a grade of D+ (Poor, At Risk) and the nation’s bridges received a grade of C (Mediocre, Requires Attention). Other D+ rated infrastructures were: aviation (airports), dams, energy, levees, schools, and wastewater. (stormwater and transit each received a grade of D) The highest-rated infrastructures were Ports (B) and Rail (B-).

The congressional proposal would allow truck weights to be increased from 80,000 pounds to 91,000 pounds. CABT argues that this would hasten the damage to our already fragile roads and bridges infrastructure, causing between 65,157 and 82,457 local bridges nationwide to be at risk of failure. The cost of replacing these bridges is estimated at between $70.5 billion to $98.5 billion, depending upon the gross vehicle weight. The states most impacted are noted as being Arkansas, California, Florida, Georgia, Indiana, Kansas, Louisiana, Massachusetts, Missouri, New Jersey, Ohio, Oregon, Virginia and Washington. 

Trucks provide a vital link in our supply chains, but they have to have safe roads and bridges to travel upon. Driverless trucks don’t mean that passengers in vehicles that are sharing the road are any safer if road and bridge infrastructures are compromised. For the safety of truck drivers, vehicle drivers, and all passengers alike, before we go bigger, shouldn’t we first be building better?   

Note: This article is based on a Logistics Management article. The original article can be found at: https://www.logisticsmgmt.com/article/anti_truck_group_says_nations_bridges_at_risk_if_congress_allows_bigger_trucks

Executive takeaways

When inbound flow goes wrong, you see it in the yard before you see it on a dashboard. Trucks queue at the gate, security logs them on paper, and the morning goes to sorting which load belongs where while the next shift’s plan slips. The yard becomes a buffer for everything the schedule didn’t anticipate.

That was the situation at an industrial manufacturer running high-volume inbound receiving. The facility needed yard scheduling, and it needed it in weeks. A purpose-built yard management system wasn’t viable in that window. Procurement for enterprise yard systems runs 6 to 12 months from proposal to go-live, plus integration with the existing transportation management system (TMS), warehouse, and enterprise resource planning layers. The clock didn’t allow it.

The pre-state was hard to live with. More than 40 trucks queued on the apron on busy mornings. There was no slot discipline, no schedule shared between operations and supply chain. No one saw what was inbound until trucks pulled up. Detention exposure ran into five figures weekly. Arrivals spiked to four or five times the steady-state baseline, a feast-or-famine pattern of dock congestion one hour and dock starvation the next.

Three teams ran the orchestration in silos: security at the gate, operations at the dock, supply chain doing the planning. When the upstream bottleneck went down, operations told security to stop letting trucks through, and supply chain heard about it from drivers calling about overnight stays. Gate congestion turned into disruption inside the plant.

The structural mapping

The path forward came from an unexpected direction. We stopped looking at yard management systems and started looking for any tool that could hold a schedule and let several parties see it. Microsoft Bookings, the consumer scheduling tool packaged with Microsoft 365 Business Standard, kept surfacing.

Bookings is built for service businesses: hair salons booking stylist time, dental practices booking chair time, consultants booking 30-minute slots. The data model is simple. Resources are configured as staff, bookable services as services. A customer selects a service, sees available slots across the staff who deliver it, and books one.

The mapping to an industrial load was direct. A dock door is a resource the way a stylist’s chair is a resource, so each dock door becomes a staff member. A trailer type is a service category the way a haircut is, so each trailer type becomes a service. For us, the customer in that model is the carrier or operations user requesting a slot. The booking discipline that runs a salon’s calendar is the same one a yard needs.

That was the structural translation. Once the mapping was clear, the rest was configuration. We built a small prototype to confirm Bookings’ calendar logic could carry industrial scheduling without breaking, and it answered in days. From there, we drove the scale-out into production. The mapping and prototype came together over those days; the rollout and cross-functional adoption took the next three weeks.

The deployment

Three weeks separated prototype validation from production go-live, in late winter heading into spring. The configuration choices made in that window decided whether the system held under load.

Visibility came first. We wanted operations, supply chain, security, and headquarters on the same schedule. Tying resources to shared service-account calendars in Outlook broke Bookings’ sync, and tighter read-only permissions didn’t fix it. The resolution was to tie staff resources to dummy email accounts that matched no real person, then give users visibility another way. They added Bookings to Outlook as an app, and with at least a viewer role, the day’s calendar appeared inside Outlook.

We had four physical doors but defined two resources at launch, one for the 53-foot door and one for the 48-foot doors, compensating with more bookings per hour on each. Two services, one per resource, held carriers to the trailer type they had booked, which mattered in winter when doors stayed closed most of the time. As the weather warmed, we collapsed the two into a single “any” service on one combined resource, carrying both booking-rate allowances since the physical constraints were unchanged.

Permissions carried more weight than expected. Bookings has four roles; we used three. Most users got viewer, read-only. Administrator stayed tight, local supply chain leaders only, since administrators can make sweeping changes. Scheduler went to headquarters staff who adjusted appointments or booked on a carrier’s behalf. External carriers booked through the public link Bookings generates, which has no approval gate, so operations checked the calendar daily against the inbound manifest for wrong trailer types or duplicates.

The toughest transition was at the gate. We were asking security to enforce a queue from an unfamiliar system, while dispatchers new to the form often filled it out wrong, leaving security to validate against incomplete data while drivers waited. We sat with them for the first few days. To ease the gate longer-term, we added an online safety quiz drivers complete on their phones on arrival, replacing a manual routine of explaining expectations and tracking acknowledgments. A first-day access problem traced to Microsoft licensing on the security accounts; once upgraded, visibility was instant.

What happened

The system produced outcomes 18 days after go-live.

The single-day inbound record fell. The facility took nearly 160 loads in a day against a prior record near 145, and total volume that day, inbound and outbound, topped 270 loads with more than 180 trucks through the gate. All of it ran through four doors, more than 45 trucks per door on the record day.

Operations first thought the scheduler was slowing things down. It wasn’t. The pace looked slow because the team was used to feast-or-famine conditions, so a couple of trucks at check-in felt like underutilization. Across 24 hours, that deliberate pace produced the record. The 40-plus trucks that used to sit on the apron dropped to two or three at steady state, roughly a 90% cut in the congestion operations sees from the floor.

The deployment broke down long-standing silos. Security used to halt inbound whenever upstream operations went down; now they rarely do, as long as the driver is scheduled. Operations learned it needs surge capacity at the dock to absorb upstream downtime without choking inbound flow. Supply chain blocks calendar time directly to recover after unplanned events or to stage maintenance windows on bottleneck operations. The calendar became where anticipated disruption stages before it reaches the floor.

Usage stuck because each group had its own reason for using the tool. Operations got slot discipline. Supply chain got inbound visibility, and security got a verifiable arrival list. No group reverted, and no shadow spreadsheets appeared. We instrumented from go-live, building the analytical layer externally on the booking data, gate timestamps, and detention billing, since Bookings provides none of it. Volume moved immediately; carrier-behavior and detention views fill in as data accumulates.

What we built was not sophisticated software. It was a consumer-grade product configured carefully against the real shape of the operation.

What this approach doesn’t do

The deployment is honest only if its limits are. Microsoft Bookings is not a yard management system, and what we built has to be read alongside what we didn’t.

There’s no real-time integration at the gate. Drivers check in physically, and security verifies the appointment against the calendar by hand. At this scale that held: existing staffing handled more than 180 trucks on the record day without added headcount. Higher volumes would force a different model.

Reporting is minimal. We built the carrier scorecard and detention tracking ourselves on the booking data, gate timestamps, and billing feed. The dock-side handoff to the TMS is still missing and would take additional infrastructure.

There’s a scale ceiling. The configuration works at four doors and a small service count. As resources grow, the calendar interface gets harder to use, and Bookings imposes its own staff and service limits. We wouldn’t recommend it for a high-bay distribution center with 25 doors dispatching across three shifts.

The non-software work doesn’t transfer the way the configuration does. Training, security buy-in, operations’ adoption of buffer strategy, and change-management attention carried the load. The tool didn’t do those things; the team did. The same configuration dropped in without that adoption work would not produce the same result.

What to take from this

Three takeaways for practitioners weighing similar moves.

First, when the deployment window is short and purpose-built tools aren’t viable, look at consumer tools that share a data model with the operational problem. The mapping is the translation; once it’s clear, the configuration follows.

Second, production lives in configuration. The structural insight gets you a viable prototype. The deployment rests on the configuration choices after it, and on the willingness to revisit them when conditions change.

Third, cross-functional adoption is the deployment. Software installs in minutes. Operational discipline takes weeks of standing up the practice with the people who use it.

One closing observation, drawn from this case and others we’ve seen revert: improvised tools tend to stick when the people who carry the compliance cost also receive the value. Each group here had its own reason to use the tool, and the tool delivered on each. We didn’t design around that principle, but in retrospect it’s the structural reason the system held.

About the authors

Bruce Rishel is Principal at Poisson Consulting, LLC, where he focuses on supply chain operations under disruption. He led the structural design and prototype validation for this deployment.

Blake Brownlee is an Industrial Engineer at B2 LLC. He led the production scale-out and cross-functional adoption at the client site.

Executive takeaways

Most companies investing heavily in artificial intelligence (AI) for their supply chains are skipping the most important step. Before asking what AI can do, they need to ask whether their supply chain is ready for it. AI is not a transformation strategy. It is a force multiplier. It amplifies whatever system it inherits. Point it at a clean, disciplined operation, and it delivers extraordinary results. Point it at a fragmented operation filled with complexity and inconsistency, and it simply scales those problems faster.

That distinction matters more than ever. Leaders see the potential of autonomous planning, procurement and logistics—yet results consistently fall short. The issue is rarely the technology itself. More often, organizations are trying to automate complexity rather than eliminate it.

3 steps to success

Before deploying AI, supply chain leaders should focus on three fundamentals: Simplify. Standardize. Automate. Each step depends on the one before it. Skip a step and AI magnifies the inefficiency rather than creating value.

1. Simplify before you scale

Many organizations view AI as a way to untangle the complexity their supply chains have accumulated over time. AI does not dissolve complexity. It encodes it.

Simplification starts by questioning not just how work gets done but why. Most organizations have built up layers of approvals, reviews, handoffs and reporting requirements that were created for a specific purpose but never removed once they no longer apply.

The symptoms are familiar. Checkers checking the checkers. Reports produced, reviewed and never acted on. Forecasts updated weekly because they always have been, even when less frequent updates would deliver the same accuracy. Documents created for one meeting, converted into presentations for a review meeting, then translated back into actions.

AI does not benefit from any of this. It absorbs all of it.

The goal is to redesign workflows with a clean-sheet mindset. Remove organizational barriers. Eliminate activities that do not create value. Reduce unnecessary approvals. Connect AI directly to the underlying data sources and decision points rather than routing information through layers of spreadsheets, presentations and reviews.

The highest-return automation project is often the process you stop doing entirely. Organizations will need to simplify first, creating the conditions for AI to operate effectively. Those that don’t simply automate bureaucracy.

2. Standardize for reliability

Once you remove complexity, standardization makes AI trustworthy. AI excels at repeatable, consistent work. It struggles when every team follows different rules, definitions and decision criteria.

Supplier performance management is a clear example. In many organizations, procurement teams evaluate suppliers differently across commodities, regions and business units. Metrics vary. Weightings differ. Scoring methods evolve independently. One team prioritizes cost, another quality, a third delivery performance—each using a different measurement framework.

Without standardization, AI cannot reliably identify performance patterns, benchmark suppliers, predict risk or recommend actions across the enterprise. The data may exist, but it lacks a common language.

Standardization does not mean eliminating judgment; it means creating a common framework for how work is performed, measured and evaluated. When teams operate consistently, AI can learn from larger datasets, generate more reliable recommendations and scale best practices across the organization.

This is as much a cultural challenge as a technical one. Success requires organizations to align on common workflows, metrics and decision frameworks before introducing automation.

3. Automate to compound the gains

With complexity removed and standards in place, automation creates lasting value, dramatically reducing the time between identifying a problem and taking action.

Consider supply-and-demand balancing. Teams often spend significant time gathering inputs, validating assumptions, building spreadsheets and creating supply-and-demand views for future planning cycles. The work is repetitive, sequential and resource-intensive. AI changes the economics of that process. Rather than building one supply-and-demand balance at a time, AI can simultaneously generate scenarios across multiple commodities, suppliers and regions for the next three to six months. What once required hours or days of manual effort can be completed in minutes. More importantly, AI can initiate workflows, assign actions, track follow-ups and escalate exceptions automatically.

This is where agentic AI becomes powerful. It does not simply generate recommendations. It orchestrates execution.

As these agentic workflows enter production, most will keep humans in the loop. Over time, organizations will build confidence by validating decisions and gradually expanding autonomy.

Build a supply chain worth amplifying with AI

The companies that win with AI will not necessarily be the first to deploy it. They will be the ones that make it easiest for AI to operate effectively. They will simplify workflows, standardize decision-making and eliminate unnecessary complexity before introducing automation.

AI will transform supply chains. The question is not whether that transformation will happen. The question is whether your organization is building the foundation required to benefit from it. AI amplifies whatever system it inherits. Leaders who create disciplined, resilient and scalable operations today will gain more than efficiency tomorrow. They will create a competitive advantage that compounds over time.

About the author

Kevin Brown is Executive Vice President of Global Operations and Chief Supply Chain Officer for Dell Technologies. Brown has more than 30 years of leadership experience across operations, technology and procurement. During his time at Dell, he has played a central role in shaping one of the world’s most efficient, sustainable and innovative supply chains, earning global recognition across industries.

Executive takeaways

Every supply chain leader has lived this moment. A cold front pushes through a region, marginal batteries fail overnight, and the demand signal surfaces in the sales data a week later—by which point the shortage is three regions deep and the network is paying for emergency freight. The forecast that drove the plan was not wrong, exactly—it was looking in the wrong direction, reading history while demand was being written by the weather.

Vishal Singh

For most of a decade, I have led replacement-demand planning for low-voltage automotive batteries, where this failure mode is the default. The lesson generalizes well beyond batteries: for a large class of products, the most important thing a planning organization can do is stop forecasting its own sales history and start forecasting the physical world driving demand. That shift is not a model upgrade. It changes the operating posture of the entire function, and it is as much a leadership challenge as a technical one.

Why history is the wrong teacher

Conventional forecasting—exponential smoothing, ARIMA, and most of their machine-learning successors—shares one quiet assumption: that the best predictor of future demand is past demand. For stable consumer goods, that holds well enough. For products whose demand is triggered by external conditions, it fails in a specific and costly way. A battery does not fail because it was sold; it fails because heat degraded it over the summer and a cold snap exposed that degradation on the first hard-starting morning. By the time the purchase appears in the sales record, the event that caused it is already over. A history-based model is, by construction, always reporting yesterday’s news—and the categories that share this property are common: HVAC equipment, agricultural inputs, service parts, anything tied to an aging installed base.

How it works

The methodology I developed, Multi-Signal Causal Demand Sensing (MSCDS), inverts the usual approach. Rather than extrapolating a demand curve, it forecasts the causal drivers of demand and learns how they translate into replacement volume at a regional level. Five families of external signal feed the model: vehicle-in-operation data (the age, model, and climate exposure of the registered fleet, at ZIP-code granularity); weather; point-of-sale activity; retailer inventory; and pricing. In compact form, the regional forecast is a function of those signals:

where predicted demand for a region and period is driven by vehicle-in-operation (VIO), point-of-sale (POS), price and promotion changes, retailer inventory (I), weather (W), and order momentum (O). A linear backbone carries the first-order elasticities so the model stays transparent, while a non-linear term—a gradient-boosted ensemble complemented by a recurrent (LSTM-style) network—captures the interactions that matter most, such as an aging fleet and an incoming cold snap combining to produce a spike neither would generate alone. Each forecast is then decomposed into additive driver contributions, so a planner can read how much of a predicted surge comes from fleet age versus weather versus price. That decomposition is what turns a prediction into a decision a team will act on.

Deployed across a network supplying more than half of the U.S. aftermarket, MSCDS has delivered a high-single-digit improvement in forecast accuracy and, more consequentially, a substantial reduction in emergency freight and in working capital tied up in safety stock—because a sharper forecast hits the same service level with less buffer.

The harder change behind the model

The accuracy gain is not the part worth writing about. What had to change around the model is. Three shifts defined the transformation, and each was organizational before it was technical.

The unit of planning moved from the SKU to the installed base. Planners stopped asking “how many of this part did we sell last year” and started asking “how many vehicles of a given age, in a given climate, carry a component near the end of its life.” That reframing changes what the function measures and treats as a leading indicator.

The trigger moved from the sales report to the weather front. Replenishment began firing ahead of demand rather than in response to it. Inventory is positioned before failures occur, so the planning calendar is paced by what is about to happen, not what already did.

The decision moved from contested to shared. This is the change that made the rest stick. Because every forecast is decomposed into its drivers, planning, supply, and finance stopped arguing about whether to trust a black box and started discussing the physical world: Is a cold front actually coming? Is the fleet in that region actually old? Those are questions an operations team can answer and stand behind. Alignment that once consumed meetings now takes minutes.

Interpretability is a leadership requirement

Much of the conversation about AI in planning treats accuracy as the finish line. In a high-stakes environment, it is barely the starting line. A model that tells a team to move a meaningful amount of inventory will not be acted on unless the people accountable can interrogate it and put their name to the call. A forecast that cannot be questioned will not be trusted, and one that is not trusted changes nothing the company does. Accuracy earns a model a hearing; interpretability earns it a decision. Leaders evaluating AI in their own functions should weigh the second at least as heavily as the first, because the return on a forecast is realized only when a human chooses to act on it.

What transfers

Batteries are an extreme case, not a special one. For any leader whose demand is driven more by external conditions than by their own sales history, three lessons carry over. Find the true driver of demand and forecast that, then translate it into demand—rather than forecasting demand directly and hoping the driver is buried in the history. Treat interpretability as an operational requirement, not a technical luxury. And recognize that the scarce skill in modern planning is no longer building the model; it is turning its output into a decision that operations will execute and finance will fund.

There is a broader stake, too. A distribution network that absorbs weather-driven shocks in advance, instead of amplifying them afterward, is a more resilient piece of infrastructure—fewer stranded motorists, fewer grounded fleets, fewer critical vehicles that fail to start on the coldest morning of the year. The financial results show up on a balance sheet; the resilience does not, and it may be the more durable contribution. When the right part is on the right shelf on the worst morning of the year and a vehicle simply starts, no one notices the forecast that put it there. That quiet non-event is the entire point.

About the author

Vishal Singh leads demand planning for the U.S. and Canada at Clarios, the world’s largest manufacturer of low-voltage automotive batteries, and is the originator of the Multi-Signal Causal Demand Sensing (MSCDS) methodology described here. Over more than 15 years in supply chain and demand planning, including roles at East Penn Manufacturing and Apple, his work has spanned automotive, consumer electronics, and retail. He holds an MBA from Kellogg and a B.E. in Computer Science.

Executive takeaways

Editor’s note: This is the final installment of a three-part series on manufacturing risk appearing on scmr.com. You can read part one here, and part two here.

A failure occurs. Components begin failing at a higher-than-expected rate. Production is affected. Customers escalate. A formal investigation is launched—and the first question sounds deceptively simple: Which supplier is responsible?

That question is a trap. You are searching only where your ERP can see.

In practice, the supplier identified at the end of an investigation is often not the source of the failure. It is the last node the system can trace.

In the MLCC case described in Part 1 of this series, the initial conclusion pointed to a supplier. Documentation linked the failed components to a specific vendor. The vendor was flagged. The relationship was reviewed. On paper, the conclusion looked complete. In reality, it explained very little.

The failure did not originate at the supplier level. It entered earlier—in a fragmented transaction layer (brokers, re-packagers, and secondary aggregators operating between authorized distribution and the buyer) where component lots were aggregated, repackaged, and reintroduced. By the time the components reached the supplier recorded in the ERP system, the issue was already embedded in the lot.

The investigation identified where the components were booked. Not where they changed.

Most investigations follow a predictable path: tracing the component through procurement records, identifying the supplier tied to the transaction, confirming documentation, and assigning responsibility. Each step follows the evidence. The conclusion follows the system—which is exactly the problem. Because the system being interrogated is the same system that failed to detect the issue.

Your ERP knows who you paid. It does not know where the lot actually came from.

The difference between the actual path of a component lot and what the procurement system records is illustrated in Figure 1. Each layer of the supply chain that the lot passes through without documentation becomes invisible to any subsequent investigation.

(Photo: Alexander Litvin)

As a result, investigations converge on the most visible point in the chain—not the most relevant one. This creates a false sense of resolution. The supplier is replaced. Controls are tightened. Procurement marks the issue as closed, while the risk continues to circulate in the system. And the same pattern returns under a different supplier name.

The underlying issue is not a weak investigation; it is a mismatch between how failures occur and how they are analyzed.

Component-level failures do not follow supplier boundaries. A single lot can move through multiple layers, from manufacturer to distribution, through secondary aggregation and repackaging, and back into circulation. At each step, visibility degrades. By the time the lot appears in your ERP, part of its history is already gone. When that lot fails, the investigation inherits the same limitation: it can only analyze what was recorded.

This is why supplier-level blame is so persistent. It is supported by the data that is easiest to access. And that is exactly why it is often wrong.

What a real investigation requires

It does not stop at the supplier. It reconstructs the path of the lot—not just where it was purchased, but where it was handled, split, repackaged, or mixed. That data does not sit in one system. It has to be assembled across multiple sources: inbound warehouse logs, packaging and inspection archives, customs and logistics documentation, supplier-side traceability where available, and independent industry databases such as ERAI or GIDEP.

Part 1: The hidden supply chain risk no dashboard shows

Part 2: When component verification becomes operational

It is slower, and it is the only method that reaches past the last visible node.

Why the wrong conclusion is dangerous

Blaming the wrong supplier does not just close the case incorrectly. It resets the system in the wrong direction. As soon as the market tightens again—availability drops, pressure increases—the same secondary channels return with the same blind spot. But now with more confidence. This is how failures become cyclical.

The lesson is not that suppliers are irrelevant. They remain critical. But they are not at the level at which all risk can be understood. The unit of failure is the lot. The unit of investigation must be the lot.

Without that shift, organizations will continue to detect failures late, assign responsibility incorrectly, implement controls that do not address the cause and repeat the same failure under different names.

Executive checklist: breaking the cycle

If your organization wants to stop repeating the same failures, the investigation process must answer five questions—every time:

• Where did the lot change state (repackaging, splitting, re-labeling)?
• Do physical identifiers (date codes, labeling formats) match across all artifacts?
• Which part of the lot’s path is not documented in ERP?
• Did the lot pass through any secondary aggregation layer?
• What evidence confirms physical origin—lot traceability records, manufacturer authentication, or independent database verification?

If these questions are not answered, the investigation is incomplete, regardless of how quickly it was closed.

That requires effort, slows decisions, and produces answers that are harder to act on. The distinction matters because it changes what you do next.

Your job is to prevent the next one.

About the author

Alexander Litvin is a supply chain executive with 27 years of experience in electronic component distribution. He is an IEEE Senior Member and the originator of the CILM (Component Integrity & Lifecycle Management) methodology. He may be reached at a67444152@outlook.com.

Executive takeaways

The NextGen Supply Chain Conference has released the agenda for the 2026 event, bringing together some of the industry’s most innovative supply chain leaders for three days of practical education, executive networking and real-world case studies focused on the technologies and strategies shaping tomorrow’s supply chains.

The conference will feature keynote presentations from leaders at Wayfair, Tractor Supply Company, and Eli Lilly and Company, alongside executives from many of the world’s most influential organizations. Additional speakers represent Apple and Amazon—both Fortune 100 companies—as well as Fortune 500 organizations including Target and Penske Logistics. Attendees will also hear from innovators such as Stanford Medicine, DP World, Fanatics, Evonik, and Dr. Reddy’s Laboratories, providing practical insights across retail, healthcare, manufacturing, logistics, and technology.

Scheduled for October 21-23, 2026, at the W Nashville hotel in downtown Nashville, Tennessee, the conference features keynote presentations, executive fireside chats, panel discussions and  30 small-group breakout sessions covering artificial intelligence, digital transformation, talent development, healthcare, retail, logistics, climate resilience and supply chain execution.

View the complete agenda here: https://www.nextgensupplychainconference.com/agenda/

Early-bird registration for the conference is ongoing. Click here to register.

Sponsorship opportunities continue to fill

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

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

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

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

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

NextGen Awards return

The 2026 NextGen Supply Chain Conference will once again recognize organizations pushing the boundaries of supply chain innovation and execution through the NextGen Supply Chain Awards. Thursday’s opening general session will honor this year’s End User Award winners, Mars Snacking (Intelligent Transformation), CVS Health (Autonomous Operations), and Ryder and BJC HealthCare (Partnership in Execution), recognizing each organization for successfully translating technology investments into measurable business results.

Tractor Supply will accept the NextGen Supply Chain Conference Visionary award on Thursday afternoon.

Friday morning’s program will spotlight the Solution Provider Award winners, with Netstock (Intelligent Transformation), Pickle Robot (Autonomous Operations) and Robust.AI (Startup) being honored and celebrated for delivering meaningful customer impact.

Unlike many industry awards programs, NextGen winners don’t simply accept an award—they share how they achieved it. All award recipients will participate in conversations and share with attendees practical insights into AI deployment, automation, digital transformation, and operational execution.

All of the 2026 NextGen Supply Chain Conference awards are sponsored by Zion Solutions Group.

Practical insights from companies leading supply chain transformation

Rather than focusing on technology alone, this year’s program examines how organizations are successfully combining AI, automation, data, leadership and operational excellence to build more resilient, agile and intelligent supply chains.

The conference opens Thursday morning with the NextGen Supply Chain End User Awards. Immediately following the awards, attendees will hear from Nitin Kapoor, vice president of technology at Wayfair, who joins Supply Chain Management Review Editor-in-Chief Brian Straight for a keynote conversation exploring how Wayfair has evolved its home delivery network through technology, operational innovation and customer-focused execution.

Craig Ledbetter, Senior Vice President, Chief Supply Chain Officer, Tractor Supply at Tractor Supply Company, will accept the NextGen Supply Chain Visionary Award before sitting down for the Visionary keynote address where he will discuss with Straight how supply chain has become a strategic growth engine for one of America’s fastest-growing retailers.

On Friday morning, Dr. Mar Gimeno, associate vice president of U.S. Supply Chain at Eli Lilly, will examine how agentic AI is transforming supply chain decision-making at the drug giant in her morning keynote address.

Other industry executives scheduled to speak, and their topics, include:

• Carey Boone, vice president of Transformation-Americas at DP World, sharing lessons on aligning operating models, leadership and talent with enterprise transformation.
• Bijoy Sasidharan, director of analytics, capacity planning and forecasting at Fanatics, presenting a real-world case study on agentic AI and demand forecasting.
• Jenny Dissen, of the North Carolina Institute for Climate Studies, discussing how organizations can leverage NOAA data to build climate-ready supply chains.
• Rahul Mittal, head of strategy and innovations at Dr. Reddy’s Laboratories, exploring why successful execution depends on governance, accountability and decision-making infrastructure.
• Debanshu Sharma, senior supply chain manager at Amazon, presenting how machine learning-based carrier risk scoring reduced pickup defects by 35%.

More speakers are being added daily. 

Industry leaders share real-world transformation stories

The agenda includes a series of executive-led discussions highlighting how organizations are addressing today’s biggest operational challenges.

Healthcare transformation takes center stage as Omar Devlin, executive director of supply chain technology at Stanford Medicine, discusses integrating control towers, intelligent planning and AI-driven automation to improve visibility, procurement and clinical operations.

As AI continues reshaping workforce expectations, Piu Ghosh, manager of product operations at Apple, examines how planning, procurement and operations roles are evolving from traditional planners to supply chain orchestrators.

Retail executives from Target, Berry Direct and GXO Logistics will join moderator Norman Katz for a panel discussion exploring automation, omnichannel fulfillment, labor challenges and the evolving retail supply chain.

Friday’s program also includes a panel led by Daniel Pellathy of the University of Tennessee featuring Johnson & Johnson Innovative Medicine, examining how organizations are rethinking education, training and career development to prepare the next generation of supply chain professionals.

Speakers from Amazon, Penske Logistics and Evonik will also be on the agenda.

Designed for executive networking

In addition to educational programming, the conference offers numerous opportunities for attendees to connect with peers throughout the event.

Networking begins with Wednesday evening’s Welcome Reception before continuing through breakfasts, lunches, refreshment breaks and Thursday evening’s rooftop networking reception.

The conference format intentionally combines main-stage presentations with smaller breakout sessions, allowing attendees to engage directly with speakers, ask questions and participate in meaningful discussions with fellow supply chain professionals.

Register today

Whether your organization is exploring agentic AI, modernizing planning processes, strengthening execution capabilities or preparing the workforce for the next era of supply chain leadership, the NextGen Supply Chain Conference offers practical insights from the executives leading these transformations every day.

To view the latest agenda, visit: https://www.nextgensupplychainconference.com/agenda/

To register for the conference, visit:

https://ngsc.regfox.com/nextgen-supply-chain-conference-2026

Organizations interested in sponsoring the conference can learn more here:

https://www.nextgensupplychainconference.com/sponsors/

Executive takeaways

Supplier negotiations are entering a more demanding phase. Persistent cost volatility, ongoing supplier price increases, and geopolitical uncertainty are creating continuous commercial pressure. In many sectors, cost movements are happening faster than traditional sourcing cycles can respond, exposing organisations to margin erosion in-year.

At the same time, procurement teams are often not set up to manage this pace of change. Supplier engagement is frequently fragmented across categories, regions, and business units. Data sits across disconnected systems and contracts, preparation varies widely between buyers, and negotiation outcomes can depend heavily on individual experience rather than a consistent approach.

Together, these pressures are exposing the limits of traditional negotiation models. CPOs need a way to respond faster, with greater coordination and control.

AI is emerging as a key enabler of that shift. By rapidly consolidating and analysing fragmented data, it creates the foundation for a more scalable approach to negotiation: one that allows procurement teams to move at the speed that current market conditions demand.

A more structured, AI-enabled approach to negotiation

Responding to today’s environment requires a shift from fragmented, buyer-led negotiations to a more coordinated and repeatable model. The objective is to execute negotiations with greater speed, precision, and control across the full supplier base, while tailoring negotiation strategies to different supplier and category contexts.

At its core, this approach treats negotiation as a structured program rather than a series of isolated events. Supplier discussions are prepared and executed in parallel, underpinned by a shared fact base and aligned strategy. In many cases, organizations also establish central “war-room” support models, providing buyers with live analytical support, coaching, and rapid decision-making during intensive negotiation waves with targeted negotiation events.

Preparation becomes systematic rather than dependent on individual buyer experience. Leading organizations are increasingly mobilizing coordinated negotiation waves, bringing together analytics, supplier intelligence, behavioral preparation, and real-time decision support to execute large numbers of supplier negotiations within compressed timeframes.

This typically requires a set of core elements:

• Integrated data and insight: combining spend, contract, and supplier data to create a consistent view of commercial positions
• Type identification: multilateral competitive negotiations must be approached differently than bilateral negotiations or monopoly negotiations
• Structured negotiation strategies: clear objectives, prioritized value levers, and supplier-specific messaging aligned to negotiation structure (multilateral vs. bilateral vs. monopoly)
• Coordinated execution: alignment across categories, regions, and teams to ensure consistent supplier engagement
• Buyer preparation and enablement: equipping teams with playbooks, behavioral insight, coaching, and structured responses to supplier tactics

AI is a key enabler of this model. It accelerates the most time-intensive part of negotiation—preparation—by rapidly extracting and structuring information from contracts, pricing data, and supplier interactions. This allows procurement teams to build a robust fact base quickly, identify inconsistencies, and benchmark positions across suppliers.

The value of AI in negotiation is not that it replaces experienced buyers. It helps them prepare faster, challenge supplier claims more confidently, and apply best practices consistently across hundreds of supplier discussions. Increasingly, AI is also being used to support supplier clustering, identify hidden pricing inconsistencies, and help buyers anticipate supplier responses and rehearse negotiation scenarios before discussions begin.

It also enables negotiation insight and best practices to be applied consistently across multiple supplier discussions, rather than remaining with individual buyers or teams.

The result is a more disciplined and responsive negotiation capability. Teams can mobilize quickly, engage suppliers with greater confidence, and deliver more consistent outcomes; turning negotiation into a repeatable engine of value rather than a series of one-off events.

The value of this approach extends beyond procurement. While the immediate impact is often seen in savings, price defense, and improved commercial terms, organizations also gain greater visibility into supplier risk, more disciplined supplier engagement, and stronger procurement capability that can be applied repeatedly across the business.

In this way, negotiation becomes more than a sourcing activity. It becomes a scalable enterprise capability; one that helps organisations protect margins, strengthen resilience, and respond more effectively to changing market conditions.

About the authors

Adi Bijedic began his consulting career as the co-founder of Düsseldorf's first student-run management consultancy, following which he built up over a decade of management consulting experience. He joined Efficio in 2018 and manages projects for clients across a broad range of industries, often within the context of private equity. Adi specialises primarily in procurement transformation - identifying and creating long term value while ensuring the organisation is structured to achive its strategic potential.

Geoffrey Boutin leads Efficio’s Data and AI practice, where he helps organizations unlock the full potential of their data to transform procurement and drive enterprise value. He partners with clients to design and implement AI-enabled solutions that solve complex sourcing and supply chain challenges, make data accessible and actionable, and embed intelligence directly into decision-making.

Felix Brockerhoff is a senior manager at Efficio. He is an economist and expert in applying Game Theory in practice with a focus on procurement and holds a master’s degree in economics from the Ludwigs-Maximilian Universität in Munich.

Executive takeaways

For years, supply chain leaders have been told that resilience comes from better technology. Invest in visibility. Build control towers. Deploy AI. Improve forecasting. Create digital twins.  All of those investments matter. But they miss a critical reality: technology and tools can identify problems, yet people still decide what to do about them. The pattern repeats with each new iteration: the capability improves, yet the decision still lands on a person.

Recent interviews conducted with experienced supply chain executives suggest that the next frontier of resilience has shifted away from software alone toward a managerial judgment focus.

Better data doesn’t mean better decisions

Consider how risk actually unfolds.  A disruption rarely arrives with a label attached. Managers are forced to interpret incomplete information, assess potential consequences, and decide whether to act before all the facts are known. The challenge is deciding which risks deserve attention and which can be ignored.  That distinction matters because modern supply chains face an overwhelming number of potential threats. Geopolitical instability, cyberattacks, commodity volatility, transportation bottlenecks, labor shortages, regulatory changes, extreme weather events, and rapidly changing technologies all compete for management attention.  No organization can respond equally to all of them.

The executives we interviewed consistently described resilience as a process of prioritization and judgment rather than prediction.  One executive noted that artificial intelligence is increasingly handling routine analysis and scenario modeling. As a result, the most valuable employees are no longer the people who have all the answers. They are the people who know which questions to ask.

Access to information is becoming democratized. Visibility platforms have become standard. Analytics capabilities continue to improve. As information becomes more abundant, the source of competitive advantage shifts. Judgement becomes the differentiator.

Resilience must part of everyday decision-making

The executives also highlighted an uncomfortable truth about risk management. Many organizations have risk management processes, but far fewer have risk management cultures.  On paper, companies often maintain formal procedures for identifying, assessing, mitigating, and monitoring risks. In practice, however, risk management frequently remains disconnected from daily decision-making. Part of the disconnect is structural, since accountability for risk often sits outside the supply chain function itself.

The difference between planning for risk and managing risk is significant. Organizations that merely document risks tend to discover problems after they occur. Organizations that operationalize risk management integrate risk discussions into procurement decisions, sourcing strategies, inventory policies, and capacity planning. Risk becomes part of how decisions are made rather than a separate compliance exercise. It is one of the inputs considered in everyday trade-offs rather than a report that is filed and forgotten.

Technology can support that process, but it cannot replace it. The interviews repeatedly emphasized that digital tools are most valuable when they accelerate decision-making rather than simply generate more information.  One executive described AI as achieving levels of speed and accuracy that would be impossible for humans working manually but also stressed that managers still determine how those insights are used.

Building resilience requires flexibility

The interviews also revealed another challenge. Many supply chains remain structurally inflexible regardless of how much data is available.  Managers can often reroute trucks, adjust schedules, or reallocate inventory. They cannot instantly create rail capacity, build new ports, qualify suppliers, or reconfigure transportation networks. More data or visibility does not create capacity. Structural flexibility develops over years of investment and relationship building.  This means resilience is not simply a technology problem; it is also an organizational design problem, and companies must build capabilities before they need them.

Perhaps the most important finding from the interviews concerns how organizations develop future leaders. Several executives emphasized the value of broad experiences over narrow specialization. Supply chain professionals who rotate across functions, understand multiple parts of the business, and develop a total-systems perspective appear better equipped to manage uncertainty than those who spend their careers optimizing a single activity. This insight becomes increasingly important as artificial intelligence assumes more analytical work. One executive credited forced rotation through unfamiliar functions for building the kind of total-system view and judgement that no single specialized role tends to generate.

Humans, not machines, solve problems

If machines become better at generating answers, human value shifts toward framing problems, challenging assumptions, interpreting tradeoffs, and making decisions under uncertainty. In other words, the future supply chain leader may look less like a data analyst and more like a sage strategist who leverages judgement and discernment skills.

The executives’ insights point to a small number of practical shifts and they track what the underlying research points to:

• Embed risk directly into core decisions such as sourcing, inventory, and capacity rather than treating it as a separate review process.
• Invest in structural flexibility, not just informational visibility, by building supplier relationships, optionality, and capacity buffers before disruptions occur.
• Redesign roles to emphasize problem framing and decision quality rather than incremental gains in analysis.
• Develop judgment through cross-functional experiences that expose managers to tradeoffs across the system.

Organizations will always need better visibility, stronger governance, and more sophisticated technology. Those investments remain essential.  But resilience ultimately depends on something more fundamental.  When conditions change, information becomes incomplete, and pressure intensifies, someone still has to decide.  The organizations that navigate disruption most effectively will not necessarily be those with the best tools.  They will be the ones with the best judgment.

About the authors

Alex Solis is a global executive with over 30 years of leadership experience across some of the world's most recognized Fortune 100 companies, including Procter & Gamble, The Coca-Cola Company, and Tyson Foods. His career has spanned operations, end-to-end supply chain management, innovation, general management, and corporate strategy across domestic and international markets.

After holding director and officer roles, Alex transitioned to strategy consulting as a Partner and Advisor in Kearney's Global Consumer, Retail, and Strategic Operations Practices. There, he advised C-suite leaders across the consumer-packaged goods, retail, food and beverage, and agribusiness sectors on supply chain resilience, growth strategy, route-to-market transformation, revenue growth management, and capability building. He has also co-authored thought leadership on topics including CPG-retailer collaboration, AI value creation in animal protein, and the U.S. labor shortage.

Today, Alex serves as Executive in Residence for Supply Chain Management at the Sam Walton College of Business at the University of Arkansas, where he helps connect industry, research, and student development through supply chain education, executive engagement, and applied research initiatives. He is a doctoral candidate in the Doctor of Business Administration program at the University of North Alabama. Alex also serves on the Harvard Business Review Advisory Council, is a member of the Private Directors Association®, and contributes as a guest lecturer and advisory board member at John Brown University's Soderquist College of Business.

Rodney Thomas, Ph.D. is co-Editor-In-Chief of Journal of Business Logistics and a professor in the Department of Supply Chain Management in the Walton College of Business at the University of Arkansas.

Executive takeaways

Over the past five years, innovation in logistics robotics has accelerated at a phenomenal pace. DHL Supply Chain has deployed more than 8,000 robots across its worldwide operations. Tradeshow floors and online videos showcase impressive displays of the latest technology, selling businesses a vision of fully autonomous, “dark warehouse” operations. With the advent of “Physical AI” and the claim that the “ChatGPT moment for robotics and automation is close,” it’s important to draw a line between headline-grabbing early-stage hype and real, scalable value. Operational impact and resilience is not achieved by chasing the next robotic showcase installation or deploying a series of rigid, unconnected, single-process use cases that won’t scale.

Tim Tetzlaff

Instead, to realize sustainable impact in an environment defined by continuous volatility, uncertainty, complexity and ambiguity, businesses need to strike the right balance. By standardizing core processes, technological building blocks (IT and operational technology) and the interfaces to connect these building blocks, logistics businesses can thrive in this unpredictable landscape. Achieving this balance begins with realizing that the logistics operation of the future won’t be fully automated nor fully human—it will be both.

Orchestrating an evolving—and hybrid—workforce

The foundation for effectively orchestrating a workforce composed of humans, robots, automation and AI is integration. By building reusable standard interfaces between physical automation, for example robots on the warehouse floor, and core IT systems, businesses acquire the capability to performance manage a blended workforce of people and technology working in unison. Rather than scaling headcount or square footage, this digital thread links warehouse management systems (WMS), labor management systems (LMS) and Robotic or Automation Fleet Managers to provide total visibility over operational throughput. In turn, this enables businesses to flexibly reallocate both technology and human resources in real-time, expanding capacity without expanding the physical footprint.

On the warehouse floor, automation covers a range of technologies, from established and scaled tools like autonomous mobile robots (AMRs) to next-generation developments in robotic grippers and mobile manipulators. While AMRs have successfully optimized warehouse transport routes across warehouses all over the world, heavy investment in robotics has led to improvements in how machines interact with individual products. These advanced physical AI systems can reliably identify and grasp items out of warehouse storage totes. By taking over these repetitive and physically demanding tasks, collaborative robots and physical AI systems support teams and free up workers for higher-skilled, technology-assisted roles.

Beyond the warehouse floor, data analytics and artificial intelligence (AI) are significantly improving day-to-day office workflows. In areas like transport planning and customer service, AI Agents can process massive amounts of data across hundreds of carrier networks. This saves office teams from manual web searches, repetitive interface entries, or making dozens of phone calls every day, massively speeding up operations and at the same time also improving quality.

Standard base, sector-specific customisation

Whether optimizing office workflows or scaling operations in the warehouse, managing a hybrid workforce requires a structured approach. Otherwise, operations quickly fracture into a hard-to-manage mix of disconnected tech solutions creating unnecessary technical debt.

A common mistake in designing supply chain solutions is assuming that every new project is 100% unique. In reality, building a responsive supply chain relies on a smart, composable architecture where a foundation of standardized, pre-connected solution modules handles roughly 80% of the requirements, and customized software or hardware adjustments handle the remaining 20% of sector-specific needs.

The baseline technologies, such as WMS, LMS, TMS and a standard integration layer can be reused across different operations. However, the specialized adjustments change based on the industry’s unique demands.

Take omni-channel e-commerce, for example. Here, the focus is on handling sudden demand spikes and fast consumer trends. A sample deployment in a geography where space is scarce may feature dense and high-speed goods-to-person technologies that bring inventory directly to the worker, boosting productivity, complemented with fit-for-purpose packing robotics. In contrast, while speed is still essential for life sciences, absolute quality control and strict compliance are the top priorities. Here, the core composable automation blueprint is paired with precise product tracking software, temperature controls and validation logs to protect product integrity.

Integration as enabler of innovation

While the operational advantages of supply chain automation are clear, businesses often face barriers to deployment. Rapidly evolving tools make it difficult to identify the right solutions, a challenge amplified by complex system integration. At DHL, we therefore decided to introduce a standard integration layer that connects our IT (WMS, LMS, ec.) to our OT (Operational Technology), such as robotics and automation.

We standardize connectors to our strategic IT and OT providers to enable reuse. This massively reduces complexity, speeds up deployment by a factor of up to 12, and maximizes resilience. Centrally managing the build and run of this integration layer while scaling across operations worldwide allows us to lower the financial and technical burdens for our customers. Our global scale converts previously rigid, long-term investments into flexible operational capabilities that can safely scale based on real-time demand shifts. It enables our customers to safely benefit from cutting-edge robotics and draw on a massive, connected ecosystem that can unlock additional volume and insights as needed.

Flexible stability as competitive advantage

Ultimately, the engine that powers this scalability and removes operational friction is data. The warehouse of the future continuously gathers real-time insights from every operational stage, allowing advanced analytics and AI to build precise forecasts that outperform traditional historical projections. Connected tools like Internet of Things (IoT) sensors and asset tracking complement this by maximizing inventory visibility and boosting productivity.

In modern logistics, flexible stability means transforming these digital insights into immediate physical action on the shopfloor. Real visibility ensures that technical equipment and human resources are never locked into one specific use case. If an unexpected bottleneck in inbound processing is observed, robotic and human teams can be instantly redistributed across warehouse processes and levels to absorb the surge.

Flexibility is not just about speed, it’s about the structural freedom to adjust resources seamlessly, based on real-time data and without operational friction, turning market unpredictability into a distinct competitive advantage.

Executive takeaways

The supply chain risk management plans most enterprises are running today are going to fail.

The teams are sharp. The plans are crisp and clear. The dashboards are lit up with alerts. But the trade environment—laden with complex tariffs, rigorous compliance expectations, and geopolitical fragmentation—has changed faster than traditional SCRM could evolve. What started as monitoring for discrete supply chain disruptions now has to support a different set of trade demands.

Evan Smith

Trade regulations now require true N-tier visibility. The Uyghur Forced Labor Prevention Act (UFLPA), complex component-based tariffs, the National Defense Authorization Act (NDAA)—all require upstream traceability, far beyond Tier 1 suppliers, to the components, materials, origins, and inputs that make up products.

Geopolitical competition and fragmentation have made trade a fault line and a weapon. U.S.-China decoupling, Russian sanctions, and rising protectionism are creating upstream risks and product-line disruption that most importers can’t see.

These demands are creating real challenges that traditional SCRM plans and tools cannot address. Landed costs are exploding, as tariff stacking, shifting origin rules, and hidden upstream exposure compress margins. Leadership teams can’t answer basic questions like “What’s our exposure to geopolitical hotspots?” or “Where are our single-sourced risks?” Teams then spend months manually stitching together answers that are wrong by the time they reach executives. SCRM tools were a critical first step for visibility into supplier risks, but blinking red lights on a dashboard don't support the demands of modern business.

SCRM’s outdated and ineffective approach

Pure SCRM tools have inherently limited capabilities. Three tactical problems make even the most well-laid traditional SCRM plan outdated and ineffective:

1. SCRM offers supply chain visibility, but doesn’t put it in the context of actual products. SCRM tools surface risks at the supplier and entity level, not the product level. The result is a high volume of low-context alerts disconnected from products, revenue, or compliance obligations—distant connections between unrelated entities buried alongside a forced-labor-flagged supplier three tiers upstream that could trigger a border detention. Teams either drown in meaningless flags or stop trusting the system altogether.
2. SCRM tools identify risks, but don’t give companies the ability to act on them. SCRM stops at identification—a flagged supplier, a risk score, an alert—without allowing teams to take action within the tool. And many of the costs hitting importers today are driven by changes to trade compliance, which a supplier risk score doesn’t address at all. The result widens the gap between seeing a risk and doing something about it—which is where most companies bleed margin and time. It’s the business equivalent of going to the doctor, being told “you might be sick,” being given no medicine, and then having the exact same experience again and again.
3. SCRM gives companies AI-derived visibility, but doesn’t allow them to verify issues. AI visibility has value in identifying and prioritizing potential risks. But alone, it’s insufficient. The insights generated by AI on public data are noisy and meaningless if they can’t be verified and reinforced with information from trusted sources—supplier data, PLMs, ERPs, global trade systems. Real traceability on actual product value chains is essential, especially when what’s in a product anchors both attestations to regulators and decisions involving millions of dollars in revenue.

Global trade means making decisions about products, and SCRM tools are disconnected from them

To improve margins and efficiency, importers must make decisions about individual products. But SCRM tools don’t offer a complete, verifiable product record, which makes it impossible to reach ground truth.

Consider the various internal teams—from design to sourcing to procurement to supply chain to logistics to trade compliance—that touch a product on its journey from raw materials to store shelves. There is no single source of product truth that they can contribute to, which means there is no single source of product truth from which to pull the information regulators need to clear shipments of goods.

The same goes for the suppliers, logistics providers, and regulators involved in a product’s creation and movement. With SCRM, they share no common operating picture and no ability to exchange information at the product level.

Instead, importers and their supply chain partners get stuck in a reactive cycle that feels productive but isn’t. Teams spend their days triaging endless alerts; suppliers take months to return surveys that feed static, outdated reports; and regulators only have the means to flag issues after shipments have already been detained.

Moving from broken SCRM to trusted trade through an AI-powered source of product truth and collaboration

Moving from broken SCRM into a system that reflects modern trade demands is possible, but requires three elements:

1. A product-level foundation. Importers need a system of record for their products—what they’re made of, where the parts come from, and who supplies them.
2. The ability to connect with supply chain partners. Once a product-level foundation exists, it has to connect to all the partners involved in getting a product from raw materials to the final customer. This includes suppliers, who collaborate on and verify actual product value chains; logistics providers, who are tasked with moving goods efficiently; and regulators, to whom compliance must be proven.

AI workflows that improve with use. Visibility alone isn’t enough. The system has to do the trade work that follows—classification, country of origin, duty calculation, scenario modeling—and it has to get smarter as teams use it. AI on top of a verified product foundation produces results that get more accurate over time; AI on top of public data alone produces alerts that go stale the moment they're generated.

In practice, this looks different from the current model in several specific ways. Suppliers stop responding to disconnected surveys and start collaborating on records tied to actual products. Internal teams stop maintaining separate versions of supplier and product data in separate systems and start working from one shared source. Regulators receive verified product-level information before goods reach the border, turning enforcement from a surprise into a formality. And the data itself improves with use: each verification, each shipment, each interaction with a supplier or a regulator strengthens the underlying record rather than aging it. The result is an operating model that compounds in accuracy over time, instead of one that decays the moment a survey is filed.

The trade environment has fundamentally reset, and isn’t going back to the free trade status quo. Every new tariff, regulation, and geopolitical escalation adds costs that compound across products, suppliers, and geographies—and the pace is accelerating.

Companies operating on fragmented product data will continue to pay a costly tax. Overpaid duties, detained shipments, invisible concentration risks, and hours burned stitching fragments together instead of making strategic decisions—these are the inevitabilities of running on a traditional SCRM plan.

What’s needed to meaningfully change this equation is a network built around products, one that builds trust within the business and with supply chain partners. When companies know their products end-to-end—and when their suppliers, logistics providers, and regulators share and contribute to that understanding—they move goods faster, pay less to get them where they need to go, and adapt fast when rules change and disruption threatens to depress margins.

About the author

Evan Smith is the CEO and Co-Founder of Altana, the AI-powered network for trusted trade. Altana provides an artificial intelligence model of the global supply chain to help governments, enterprises, and financial institutions improve global commerce. Prior to Altana, Evan led enterprise solutions and strategic partnerships for Panjiva, a trade data science company, and led the sale of Panjiva to S&P Global in 2018. Before Panjiva, Evan co-managed a private equity partnership under a family office sponsor, and served as the CEO of IMBU Technologies, a wholly-owned portfolio company, providing textile supply chain automation software. Evan holds a Bachelor’s Degree in Economics from Yale University.

Executive takeaways

Editor’s note: This is the second of a three-part series on manufacturing risk appearing on scmr.com. Part 3 will publish on Tuesday, July 14. You can read part one here.

A production disruption triggered by a component issue. Not a large OEM-scale shipment. A fragmented secondary-market purchase—several lots, each in the tens of thousands of dollars. MLCC capacitors that passed incoming inspection and failed months later in the field. Nothing in the dashboards signaled a problem.

The failure only became visible when return rates started to climb—slowly at first, then fast enough to trigger a full investigation. By that point, the damage was already done.

This is not an unusual case. It is a category of risk that most supply chain systems are not designed to detect.

Most risk frameworks focus on supplier-level metrics: financial stability, delivery performance, certifications, capacity. These are necessary. But they do not guarantee component-level integrity.

A supplier can be reliable. The documentation can be valid. The transaction can be compliant. And the physical component can still be wrong.

In practice, these failures are almost never introduced and detected at the same point. Misrepresented MLCC components typically enter through secondary-market intermediaries—where lots are aggregated, repackaged, and redistributed. They pass through the warehouse intake. They pass through incoming inspection. And they are only identified later—in production anomalies or field failures.

The point of introduction and the point of detection are almost never the same.

Part 1: The hidden supply chain risk no dashboard shows

In the MLCC case, the issue was not a fake component, but a misrepresented one: a lower-grade part sold as higher-grade; a reprocessed component repackaged as new; a lot with compromised storage history presented as compliant. That is precisely why it passed.

What verification looks like in practice

Tuesday morning, warehouse intake. The shift does three things differently from standard receiving:

• Each lot is separated and treated as its own unit—not merged with others from the same supplier
• Documentation is cross-checked against independent sources, not accepted at face value
• Storage history is reconstructed where possible; sampling is adjusted based on source risk profile

This adds friction. It is slower. And it is the only method that consistently catches the category of failure described above—not because it is more sophisticated, but because it operates at the right level.

The three shifts that change outcomes

Organizations that break the pattern do not do so through better technology alone. They make three operational shifts:

• They separate the verification function from procurement. Not organizationally separate, necessarily. But functionally separate—so that the people under pressure to close the purchase are not the same people assessing whether it should close.
• They treat the lot as the unit of risk, not the supplier. A reliable supplier can ship a compromised lot. Treating all lots as equivalent is what allows misrepresented components to pass.
• They apply judgment before testing, not instead of it. Directed verification—based on source risk, supply path complexity, and market conditions—outperforms random sampling for this category of failure.

The trade-off most teams underestimate

Operationalizing component-level integrity adds friction. It slows intake. It requires judgment that cannot be fully automated. It creates exceptions that are uncomfortable to manage under production pressure.

That friction is exactly why most organizations avoid it. And it is exactly why the failures keep occurring.

The speed-versus-visibility trade-off is not theoretical. Every organization that has experienced this category of failure has, in retrospect, identified the moment when process discipline gave way to urgency. Each exception looked justified in isolation. Taken together, they recreated the original exposure.

For supply chain leaders, the real question is no longer whether this risk exists. It is whether their organization operates at the level where the failure actually occurs because the most expensive failures are not the ones that appear in your metrics. They are the ones that pass through them.

About the author

Alexander Litvin is a supply chain executive with 27 years of experience in electronic component distribution. He is an IEEE Senior Member and the originator of the CILM (Component Integrity & Lifecycle Management) methodology. He may be reached at a67444152@outlook.com.

Executive takeaways

Mapping your supply chain from finished products through multiple tiers has become an important function within supply chain operations. The idea of mapping global suppliers took root, particularly after the pandemic, to identify and address potential vulnerabilities and risks at every link and tier in the chain. This is no easy task and often requires the use of software and services that identify supplier networks and connections.

Uyghur Forced Labor Prevention Act (UFLPA)

In 2021, the U.S. passed the Uyghur Forced Labor Prevention Act (UFLPA)—another reason, among many, to map global supply chains. Because the UFLPA law presumes that any goods or materials originating from China’s Xinjiang Uyghur Autonomous Region (XUAR) are made with forced labor, you must overcome this presumption with clear and convincing evidence to be able to enter your shipments through U.S. Customs. The only way to comply is to map and document every link in the supply chain. For apparel importers, this means from the cotton fields to processing, textile production, apparel manufacturing, and all labor involved along the way. This new U.S. regulation digs deep into China’s supply chains, and the Chinese government has recently retaliated because this meddling was thought to be extraterritorial.

Extraterritoriality

Extraterritoriality is the legal principle by which a nation asserts its authority, laws, or jurisdiction in a foreign country. In this case, it is the U.S. asserting import laws on Chinese suppliers.

The Chinese government recently passed two new laws that have U.S. trade professionals alarmed. The Chinese 834 and 835 regulations are supposed to protect Chinese supply chains from intrusion and sabotage by foreign countries—extraterritorialism.  These new laws restrict foreign countries, companies, and individuals from investigating the origin of raw materials, parts, and finished products manufactured, grown, or mined in China, creating an opaque shield for any company mapping its supply chain. The Chinese government intends to protect the sovereignty of Chinese companies from extraterritoriality—the application of U.S. laws on Chinese domestic industry.

The problem is that now U.S. import regulations for goods such as textiles, especially cotton, electronics, tomato products—anything that may originate in Xinjiang Province—must be mapped, documented, and confirmed that these goods are not made by forced labor. If this cannot be proven, the goods will not be allowed to enter the U.S. But the new Chinese laws will not allow this mapping and documentation. So U.S. importers are caught between a rock and a hard place. 

Denied entry into the U.S.

Companies caught in this scenario, where they cannot map the origins of their supply chains and are denied entry into the U.S., must arrange for the return of the goods or for destruction of the goods under supervision by U.S. Customs.

China has also imposed countermeasures against the United States by placing 10 U.S. companies on an export control list and barring Chinese government agencies from purchasing products from 46 other U.S. companies.

All of this tit-for-tat trade war action may seem like Washington policy folly, but it is causing real impact to thousands of U.S. importers. China is now playing hardball in the trade wars.

On one side is a rock, and on the other side is a hard place. Importers are stuck in the middle until our trade relationship with China improves or U.S. importers start sourcing in other countries or at home.

Executive takeaways

The Strait of Hormuz may be opening again slowly, but supply chain leaders should not mistake that for a return to normal.

According to Eric Fullerton, vice president of data insights at project44, the recent disruption serves as another reminder that supply chains are operating in what many leaders now describe as a never-normal environment—one where resilience is less about recovering from disruption and more about adapting to constant change.

Speaking on a recent episode of the Talking Supply Chain podcast, Fullerton said the reopening of the critical trade corridor is only the first step in a much longer recovery process that will include clearing port congestion, repositioning equipment, restoring commercial confidence, and determining which emergency workarounds developed during the crisis become permanent operating practices.

Energy is only part of the story

The Strait of Hormuz is one of the world’s most important trade corridors, handling more than 20% of globally traded oil and natural gas. But Fullerton noted that focusing solely on the energy impact overlooks a much broader supply chain story.

“What is also really significant about this region is that it’s not just oil and natural gases; it’s the petrochemicals, it’s the byproducts,” he said.

Those materials include petrochemical feedstocks used in manufacturing as well as fertilizers critical to global agriculture. Fullerton noted that roughly 40% of global urea exports originate in the Persian Gulf region, making the disruption a food security issue as much as a transportation challenge.

Diverted shipments

Project44 tracked more than 81,000 shipment diversions during the disruption, revealing a response pattern unlike many previous supply chain events. Rather than seeing diversions spike immediately and then decline, rerouting activity continued building for weeks as carriers, shippers, and logistics providers struggled to navigate uncertainty surrounding ceasefires, security risks, and changing operating conditions.

“The actual high for the number of weekly diversions was week four,” Fullerton said. “That is very unique.”

Related Podcast

 

Talking Supply Chain: The Strait of Hormuz is open, but normal is not near

The reopening of the Strait of Hormuz is welcome news for global supply chains, but according to Eric Fullerton, vice president of data insights at project44, the industry’s focus should not be on returning to normal. Instead, supply chain leaders should be asking what they learned from the disruption and how they can build more resilient operations before the next crisis arrives.

Click to listen today

 

The company’s data showed that even as conditions improved, weekly diversions remained more than 250% above pre-conflict levels.

The disruption also demonstrated how quickly local events can create global consequences. While attention remained focused on the Middle East, some of the most significant operational impacts appeared elsewhere in the network.

India’s Navi Mumbai port, for example, experienced dwell times nearly three times higher than normal as diverted cargo flowed into alternative trade routes. Vessel traffic around the Cape of Good Hope surged as carriers sought alternatives to both the Strait of Hormuz and ongoing disruptions in the Red Sea.

“What we saw with India, I think, was quite surprising,” Fullerton said. “The disruption did not stay in the Middle East and propagated outwards into those Asian port networks.”

Local disruptions mean global impacts

Those secondary impacts highlight the reality that disruptions rarely remain isolated.

Even as vessel traffic begins returning to the Strait, Fullerton believes many of the changes developed during the disruption may persist. Carriers have established new transportation lanes; shippers have developed alternative sourcing strategies; and logistics providers have built new routing playbooks.

Some of those changes may prove more resilient than the operating models they replaced, he said.

That reality raises a broader strategic question for supply chain organizations. Rather than asking how quickly conditions return to normal, leaders may need to determine which new practices should become part of future operations.

The companies that managed the disruption most effectively, according to Fullerton, were not necessarily the ones with the largest logistics teams or resources. They were the organizations that could quickly understand their exposure and make informed decisions before disruptions spread throughout their networks.

“It’s not like they were short on people or effort,” Fullerton said. “They were short on signal.”

Visibility matters, but ...

Organizations with visibility into inventory, supplier networks, transportation lanes, and shipment status were able to identify risks and act quickly. Others found themselves reacting after problems had already propagated through their operations. But Fullerton argued that visibility alone is no longer enough.

“Visibility has been a long journey for many companies and organizations,” he said, but quickly adding that visibility alone was not enough for some companies to respond quickly.

The next stage of supply chain maturity, he said, is connecting that visibility with decision-making and execution. That means not only understanding which inventory is exposed to disruption, but also knowing what actions should be taken, when they should be taken, and whether intervention is even necessary.

It is a lesson that organizations learned after Covid. It was reinforced at the start of the Russia’s invasion of Ukraine. And now it is showing itself again with the Strait of Hormuz.

2026 second-half outlook

Looking ahead, Fullerton expects continued volatility driven by geopolitical tensions, trade policy uncertainty, weather disruptions, and changing transportation patterns. He described the near-term outlook as “cautious stabilization with ongoing volatility.”

For supply chain leaders, the most important question may not be what happened during the latest disruption, but whether they are prepared for the next one.

If a major event occurs tomorrow, can the organization immediately identify its exposure?

“What I would say is, okay team, next time a disruption like this happens at this scale—and there will be a next one—I want to know what our risk exposure is to in-transit inventory instantly,” Fullerton said.

That capability, he argued, is becoming the foundation of modern supply chain resilience.

Executive takeaways

If you have spent more than five minutes in the supply chain industry, you know the drill of a typical trade conference. You walk into a massive convention center, grab a complimentary branded tote bag, and brace yourself for the vendor gauntlet. The interactions mostly happen between practitioners searching for solutions and sales teams promising that their latest “AI-enabled control tower” will fix everything—perhaps even your sleep schedule.

Don’t get me wrong: vendors matter. The ecosystem depends on them. But that is not the only way professionals learn and advance. Many of us want something deeper. We want to exchange ideas directly with peers, examine real technical implementations, and ask granular questions about how organizations actually solved the exact operational nightmare we are currently facing.

That desire recently led me to the INFORMS Analytics+ Conference.

I’ll admit, my expectations were mixed. INFORMS—the Institute for Operations Research and the Management Sciences—is widely recognized as the leading professional association for analytics, operations research, AI, and data science. Its community includes renowned academics, industry innovators, and some of the most sophisticated analytics teams in the world. As more of a “regular” supply chain practitioner, I initially wondered: Is this going to be too technical? Will they revoke my coffee privileges if I can’t solve a non-linear optimization problem in my head?

What I discovered instead was one of the most intellectually energizing professional experiences I’ve had in years.

The Nobel Prize meets the Super Bowl of operations research

Unlike many vendor-driven trade shows, Analytics+ is centered on ideas, applied innovation, and real-world impact. The focus is not on flashy demos, but on substantive case studies, candid technical discussions, and the measurable business value created through analytics and operations research.

My involvement with the event was twofold, as I judged and coached finalists for the prestigious Franz Edelman award.

If you are unfamiliar with the Edelman Award, you should not feel bad, but you should know about it. Within the analytics and operations research community, it is one of the highest honors in the field. Often described as the equivalent of a Nobel Prize for applied analytics, the award recognizes organizations that have successfully deployed advanced analytical methods to create extraordinary real-world impact.

To even qualify, teams must demonstrate that their solution is not theoretical or experimental, but fully implemented and delivering measurable operational and financial results at scale. The level of rigor is extraordinary. Teams endure multiple rounds of scrutiny, validation, and technical review before ever reaching the finalist stage.

Each year, only six projects make the shortlist.

The finalists then deliver deeply transparent, under-the-hood presentations of their work. They share the realities behind implementation: team structures, organizational challenges, years of development effort, modeling approaches, deployment hurdles, and the specific algorithms and methodologies used. Judges challenge assumptions and probe technical details while practitioners in the audience furiously take notes.

The 2026 finalists showcased exceptional work. Three presentations especially stood out to me:

• Microsoft (the eventual winner) demonstrated how it developed an internal, explainable LLM-powered supply chain planning assistant—precisely the kind of capability supply chain executives across industries are racing to build.
• ECCO Shoes detailed its evolution from a basic one-to-one replenishment model into an Intelligent Auto Replenishment (IAR) system that significantly improved revenue performance while integrating seamlessly into a broader enterprise analytics ecosystem.
• NVIDIA presented a fascinating case study on scaling its supply chain at an unprecedented pace amid explosive AI-driven growth. Interest in the session was so intense that attendees packed the room beyond capacity.

What became abundantly clear throughout these presentations was the power of collaboration between academic expertise and operational experience. Nearly every elite team combined advanced analytical talent with seasoned business practitioners working side by side.

That intersection—where rigorous analytics meets real operational execution—is where the future of supply chain is being built.

The terrifying technology gap

But as I walked the conference halls, talked with attendees, and drank my hard-earned coffee, a more sobering realization emerged: the gap between industry leaders and everyone else is widening at an alarming rate.

While many organizations are still wrestling with fragmented systems, spreadsheet consolidation, and decades-old ERP limitations, leading companies are deploying cutting-edge analytics and AI to generate transformative operational advantages.

We are now living in a world where some companies have LLMs engaging in real-time conversations with demand planners, explaining forecast logic and scenario tradeoffs, while others are still struggling to locate physical inventory in a warehouse.

That gap is no longer theoretical. It is operational, strategic, and increasingly existential.

The uncomfortable reality is that the window to catch up is closing faster than many executives realize. Waiting for vendors to hand over a polished roadmap is no longer enough. Leaders need to actively educate themselves, develop internal analytical literacy, and identify the highest-value opportunities to apply AI and advanced analytics within their own supply chain networks.

The companies doing this well are not simply buying technology; they are building organizational capability.

Where to go from here

The Analytics+ Conference may be over, but the learning does not stop there.

For practitioners looking to move beyond surface-level sales pitches and better understand how advanced analytics is actually transforming supply chains, INFORMS offers an enormous amount of accessible and practical content—even for those without a PhD.

I highly recommend exploring resources such as the Resoundingly Human podcast, the INFORMS Journal on Applied Analytics, and the organization’s extensive video library of Franz Edelman Award presentations and case studies. These materials provide rare visibility into how leading organizations are solving some of the world’s most difficult operational challenges.

Most importantly, the conference reinforced a powerful idea: advanced analytics is no longer a niche specialty reserved for elite technical teams. It is rapidly becoming a core competitive capability for modern supply chains.

If you have been waiting for a sign to stop treating your supply chain as a cost center and start treating it as a data-driven strategic weapon, this is it.

About the author

Marianna Vydrevich is manager of operations research & network optimization at GAF, North America’s largest roofing manufacturer. Vydrevich is a seasoned supply chain expert with a decade of global experience, specializing in supply chain network design and data science. She can be reached at LinkedIn or at marianna.vydrevich@gmail.com

To dive deeper, read: Managing human and AI teams across the supply chain

For more information, visit apqc.org.

 

Executive takeaways

Editor’s note: This is the first of a three-part series on manufacturing risk that will appear on scmr.com over the coming weeks. Part 2 will publish on Tuesday, July 7 and Part 3 will publish on Tuesday, July 14.

Most supply chain risk frameworks focus on visible risks: logistics disruptions, supplier financial health, demand volatility, and geopolitical exposure. These risks are real, and the frameworks that address them have become more rigorous over time.

But there is a layer closer to the production line that almost no dashboard captures—the integrity of the component itself.

A counterfeit capacitor. A remarked chip with a falsified date code. A genuine part from a compromised storage environment. None of these failures announce themselves. They enter the supply chain quietly, pass through procurement systems that were not designed to detect them, and surface only when a production line stops—or, worse, when a product fails in the field.

The layer standard frameworks miss

After 27 years in electronic component distribution across the EAEU region, I have watched this pattern repeat—in companies of every size and sophistication level. The ones most exposed to component integrity risk are rarely those with weak procurement processes. They are often the ones with highly effective processes built to address the risks they were designed for, while leaving other exposures largely unexamined.

Standard supplier risk assessments evaluate financial stability, delivery performance, quality certifications, and capacity. These are necessary. What they do not address is whether the specific lot of components in this specific shipment is what the documentation says it is.

The gap between supplier-level risk management and component-level integrity is where counterfeit parts live. In a market where the global counterfeit electronic component problem is measured in billions of dollars annually, this is not a theoretical gap.

What component integrity risk looks like in practice

The failure mode is rarely dramatic. It is usually quiet and delayed. A batch of passive components from an unfamiliar secondary-market source tests within spec in incoming inspection because incoming inspection is sampling-based, and the counterfeit rate in the batch is low enough to survive the sample. The components go into production. Most work. Enough of them fail in the field, over time, under load conditions that the acceptance test did not replicate.

By the time the failure pattern is identified and traced back to the source, the downstream costs—warranty claims, recall logistics, reputational damage, regulatory exposure—have long since exceeded what more rigorous upfront verification would have cost.

The organizational response is almost always the same: tighter incoming inspection, stricter approved vendor lists, better contracts with indemnification clauses. These are rational responses. They are also responses to the last failure, not the next one.

A framework for the layer that’s missing

The CILM (Component Integrity & Lifecycle Management) methodology addresses this gap through a reframing of the core question. Where supplier risk management asks, “Is this supplier reliable?,” component integrity management asks, “Is this specific component what it claims to be, from the source it claims, in the condition it claims?” Related questions; but they require different data, different verification processes, and different risk models.

(Photo: Author)

In practice, this means verification is performed at the component lot level, not just at the supplier level. The framework does this through three elements: digital traceability (a verifiable chain of custody for each lot), independent verification (separating the verification function from procurement), and risk-weighted assessment—scoring decisions based on quantified exposure rather than a simple approved/rejected binary.

Why this matters now

The pressure on electronic component supply chains has increased substantially over the past five years. Pandemic-era shortages drove procurement teams toward non-traditional sources. Trade policy shifts and export controls have complicated established supply routes. CHIPS Act implementation and related policy changes are reshaping the competitive landscape for sourcing.

Each of these pressures increases the probability that components enter supply chains from sources that have not been subject to the verification rigor that established channels provide. ERAI, which tracks electronic component fraud reports from industry participants, has continued to document reported incidents throughout this period.

Supply chain managers who have spent the last decade building sophisticated risk frameworks now face a new question: are those frameworks measuring the right things? For component integrity, the answer is frequently no—not because the frameworks are poorly designed, but because they were designed for a different set of risks.

The risk that does not appear in your metrics will eventually appear on your production floor.

About the author

Alexander Litvin is a supply chain executive with 27 years of experience in electronic component distribution. He is an IEEE Senior Member and the originator of the CILM (Component Integrity & Lifecycle Management) methodology. He may be reached at a67444152@outlook.com.

Executive takeaways

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

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

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

Make technology useful before asking for belief

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

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

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

Stop accepting maturity as an excuse

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

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

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

Reframe the investment conversation

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

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

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

Use governance to end site-by-site drift

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

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

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

About the author

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

Executive takeaways

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

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

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

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

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

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

Moving beyond rooftop navigation

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

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

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

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

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

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

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

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

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

Why seconds matter in modern delivery networks

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

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

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

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

AI still struggles with geospatial reasoning

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

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

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

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

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

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

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

From generative AI to physical AI

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

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

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

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

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

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

Executive takeaways

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

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

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

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

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

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

Visibility alone isn’t enough anymore

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

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

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

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

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

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

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

Reliability may now matter more than speed

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

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

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

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

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

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

Returns data is becoming operational intelligence

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

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

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

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

End-to-end data is reshaping fulfillment strategy

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

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

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

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

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

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

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

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

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

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

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

Panelists:

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

Executive takeaways

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

Examining inefficiencies at the Port of New York and New Jersey

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

Our research identified three key challenges:

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

Model and insights

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

Our research revealed the following key insights:

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

Conclusions

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

Executive takeaways

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

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

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

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

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

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

Speed has become the primary constraint

The traditional cadence of data center development is under pressure.

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

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

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

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

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

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

The era of fragmented delivery models is ending

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

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

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

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

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

Standardization is emerging as a competitive advantage

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

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

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

For logistics providers, this changes the nature of execution.

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

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

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

Lifecycle logistics is mission-critical

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

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

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

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

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

Why global coordination will define AI infrastructure deployment

Perhaps the most important shift underway is conceptual.

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

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

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

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

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

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

About the author

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

Executive takeaways

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

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

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

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

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

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

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

Eli Lilly, Tractor Supply also to keynote

Mar Gimeno, associate vice president-U.S. Supply Chain for Eli Lilly, and Craig Ledbetter, Senior Vice President, Chief Supply Chain Officer, Tractor Supply for Tractor Supply Company, will also provide Keynote addresses during the three-day event in the heart of Nashville at the W Nashville hotel.

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

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

The 2026 agenda is organized around four industry focus areas:

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

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

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

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

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

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

For more information on sponsorship packages, click here.

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

Executive takeaways

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

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

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

AI layoff narrative lacks data

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

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

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

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

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

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

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

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

Why workers fear AI

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

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

From AI layoffs to AI strategies

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

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

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

The hidden risk: experience starvation

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

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

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

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

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

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

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

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

Automation, not AI, is the biggest threat

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

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

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

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

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

Executive takeaways

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

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

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

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

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

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

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

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

Unified planning

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

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

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

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

That planning transformation produced measurable operational results.

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

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

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

Growth grows

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

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

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

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

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

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

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

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

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

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

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

For Tillamook, that bet appears to be paying off.

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

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

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

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

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

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

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

Three trends behind the rankings

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

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

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

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

Executive takeaways

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

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

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

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

Execution lapses

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

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

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

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

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

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

Organizational gaps

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

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

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

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

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

A reset is underway

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

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

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

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

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

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

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

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

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

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

Executive takeaways

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

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

The launch moment when plans meet reality

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

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

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

Yet revenue starts falling behind projection.

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

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

Why analytics without execution infrastructure fail

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

Explore more on this topic at the NextGen Supply Chain Conference

 

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

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

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

Register to attend today

 

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

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

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

The 4 components of supply chain execution infrastructure

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

1. Decision architecture: Clear ownership of allocation under constraint

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

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

Decision architecture establishes three things before the launch:

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

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

2. Real-time supply demand integration loops

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

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

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

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

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

3. Structured operating rhythm and cross-functional governance

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

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

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

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

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

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

4. Transparent accountability and execution tracking

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

For patent cliff launches, this includes several mechanisms.

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

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

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

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

Where supply chains lose value during launches

Across failed patent cliff launches, three patterns emerge consistently.

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

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

Measuring what matters in launch execution

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

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

What supply chain leaders should do now

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

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

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

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

About the author

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

Executive takeaways

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

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

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

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

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

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

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

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

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

Data analysis is key

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

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

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

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

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

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

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

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

The challenges of forecasting

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

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

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

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

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

Controlled AI deployments

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

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

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

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

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

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

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

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

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

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

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

Executive takeaways

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

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

Shifting demands

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

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

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

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

Operational, financial disconnect

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

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

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

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

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

Scenario planning

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

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

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

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

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

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

Speaking CFO

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

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

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

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

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

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

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

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

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

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

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

Executive takeaways

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

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

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

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

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

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

Executive takeaways

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

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

The gap nobody warned you about

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

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

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

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

What actually breaks

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

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

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

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

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

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

Why it keeps getting missed

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

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

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

The question to ask before you sign

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

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

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

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

How organizations close the gap

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

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

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

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

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

What to do before the business case closes

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

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

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

A final word

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

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

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

About the author

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

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