Share:

Uncovering Hidden Costs in Supply Chain Planning: Tom Moore of ProvisionAI on What Companies Miss

In today’s increasingly complex global supply chain landscape, Tom Moore keeps his message refreshingly straightforward: ProvisionAI helps large companies discover hidden costs and eliminate them. Organizations such as Procter & Gamble, Nestlé, and Unilever have leveraged the company’s technology to uncover and eliminate inefficiencies—particularly in transportation and warehousing—that traditional systems fail to detect. The outcome is significant and often delivers immediate savings.

But Moore believes many of these problems stem from misunderstandings about the very technologies companies rely on.

 

Misnamed Systems & Misaligned Expectations

Before the interview officially began, Moore reflected on the surprisingly inaccurate names assigned to modern supply chain technologies. ERP systems rarely plan resources across the enterprise, despite what their name suggests. Warehouse Management Systems, while certainly used in warehouses, don’t actually “manage” much at all. People behind keyboards still make most of the critical decisions.

This disconnect in terminology shapes faulty expectations. Many organizations believe their planning systems will truly plan the supply chain, yet most tools merely react to demand signals. If ABC Company orders ten cases, the system automatically replenishes—without considering warehouse capacity, transportation availability, downstream implications, or cost-to-serve.

Moore characterizes this as both an old problem and a new one, and it remains a costly challenge.

 

Volatility, Blind Spots & Self-Inflicted Costs

Because planning technologies operate in disconnected silos, companies unintentionally create large pools of hidden costs. Moore shared examples such as networks shipping 23 loads on a lane one day and only two the next—simply because planning logic triggers replenishment without coordination.

At times, these inconsistencies can force warehouses to overstaff. In one case, a Southern California operation carried twelve additional employees strictly to handle unpredictable peaks, at a cost to the organization of about $1 million in annual labor expense.

Moore emphasized that these hidden costs aren’t just financial; they place unnecessary stress on teams through overtime, firefighting, and constantly shifting workloads. “Planning shoots virtually every part of the company in the foot,” he noted. The real issue: most organizations don’t recognize the full cost of volatility or the impact of their own decisions.

 

AI: Promise, Hype & the Gap in the Middle

When asked about newer challenges confronting supply chain planners in 2025, Moore pointed to AI. While AI is often positioned as the next great leap, he believes the technology is still widely misunderstood. Current systems—planning, ordering, transportation, warehousing—do not speak a common language. Until companies implement true agentic AI, capable of orchestrating all functions through a cohesive, cross-functional master plan, the technology will not achieve its full potential.

Despite industry claims, Moore says no one has fully reached this level of capability yet. Companies must “transact in reality” and recognize where AI is today—not where marketing suggests it is.

 

Real Progress: Leveling the Network

While Moore acknowledged that he hasn’t yet solved the problem of organizational apathy—the tendency to cling to the status quo—ProvisionAI has cracked one of the industry’s most persistent issues: supply planning volatility.

The company’s patented LevelLoad® technology levels inventory and transportation activity across an entire network for the next 30 days. It is a complex mathematical and AI challenge, but according to Moore, the results speak for themselves: fewer peaks, fewer staffing emergencies, smoother flows, and dramatically reduced transportation and warehousing costs.

 

Learn More

Moore encourages anyone interested in learning more to visit ProvisionAI.com or reach out to him on LinkedIn. His practical, reality-first approach stands out in a noisy landscape—proving that sometimes the biggest breakthroughs start with seeing the costs hiding in plain sight.

We also invite you to listen to the full audio version of this interview with Scott W. Luton and Tom Moore: click here.

More Blogs

multi-echelon inventory optimization
Blogs
July 2, 2026

Multi-Echelon Inventory Optimization Explained: How to Cut Inventory Without Cutting Service

When service levels slip, most organizations respond the same way: add more inventory. A little extra safety stock here, a buffer there, and to many businesses, that feels like the safest path forward. But it rarely works. Instead of solving the problem, businesses end up with bloated inventory in one location and stockouts in another. Working capital climbs, service is still inconsistent, and planners are stuck reacting rather than improving outcomes. It’s like trying to fix traffic by adding more cars to the road, and congestion just gets worse. This is the inventory trap: chasing service by piling up stock instead of managing it strategically.   What is Multi-Echelon Inventory Optimization? Multi-Echelon inventory optimization (MEIO) helps you put the right inventory in the right places across your network to hit service goals with less total stock. Instead of optimizing each site in isolation, MEIO looks at your entire network of plants, distribution centers, suppliers, and customers as one connected system.   The three most common inventory issues businesses face At its core, MEIO answers three questions every supply chain leader is asking: Where should we hold inventory? Not every location needs the same level of protection. MEIO determines the most…
demand sensing
Blogs
March 3, 2026

Key Demand Sensing and Forecasting Use Cases Across Industries

Special Guest Blog Post written by Chris Cunnane with InterSystems   In a world defined by rapid market shifts, volatile supply chains, and unpredictable customer behavior, traditional forecasting methods often fall short. Relying primarily on historical data is no longer enough. To stay competitive, organizations are increasingly turning to demand sensing and forecasting, an approach that blends real-time data, advanced analytics, and AI to anticipate demand more accurately and respond faster to change. This shift is not limited to retail or manufacturing. Demand sensing is transforming how organizations across industries plan operations, allocate resources, and improve service levels. Below, we explore key industry use cases where demand sensing is delivering measurable value, and why businesses should care.   Why Demand Sensing Matters Demand sensing moves beyond static historical trends. It incorporates current, high-velocity data signals such as sales transactions, weather patterns, logistics feeds, economic indicators, and even social sentiment to generate short-term demand forecasts that reflect real-world conditions. The benefit is clear. Organizations gain better visibility and responsiveness across procurement, production, inventory, and distribution. Instead of reacting to outdated forecasts, they can make timely decisions that reduce costs, prevent stockouts, and improve customer satisfaction. FMCG, CPG, Retail, & E-Commerce Fast-moving…