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Distribution and fulfillment leaders operate vast networks where performance expectations continue to rise while operational complexity quietly compounds. Scale brings exposure: more facilities, more planning signals, more execution variables, and more systems involved in day‑to‑day decision‑making.

Warehousing and storage alone employs approximately 1.8 million workers, while the broader transportation and warehousing sector supports more than 6.5 million jobs nationwide, according to the U.S. Bureau of Labor Statistics. That scale magnifies the financial impact of small inefficiencies across staffing, throughput, and execution.

At the same time, operational data in these environments is typically distributed across warehouse management systems, labor systems, transportation platforms, planning applications, and financial models that were not designed to reconcile.

The Brookings Institution has documented that fragmented and non‑interoperable supply‑chain data delays the detection of disruptions and coordination failures, increasing operating costs and reducing responsiveness before problems become visible to leadership.

The OECD’s Measuring Productivity Manual explicitly states that accurate cost and performance analysis depends on unified definitions, integrated data sources, and consistent measurement frameworks — conditions that are difficult to sustain in large, multi‑system operations.

As a result, leaders across distribution networks are often accountable for outcomes without a single, defensible view of where time, cost, and capacity are actually being consumed.

Emerj recently featured conversations on the AI in Business podcast examining how large‑scale distribution and fulfillment networks are addressing the growing gap between operational complexity and decision visibility. The discussions included Dan Keto, President and Co‑founder of Easy Metrics, and Jerod Hamilton, Director of 3PL Warehouse Strategy at Tyson Foods.

This article highlights the operational insights shared across both episodes, outlining how leaders address visibility gaps, synchronization challenges, and execution risk.

• Unifying warehouse data as the foundation for visibility:  A single, aligned model for robotics, automation, and WMS data restores visibility that leaders can act on during the shift — not after it.
• Sequencing AI investment to avoid costly implementation failures:  Applying the right AI to conditioned operational data prevents unreliable outputs and the high costs of deploying before the data foundation is ready.
• Measuring network economics rather than departmental performance:  Evaluating operations as a connected system prevents local efficiency gains from quietly erasing margin elsewhere in the network.

Listen to the full episode below:

Episode:  Improving Warehouse Efficiency with Unified Data and AI-Driven Visibility – with Dan Keto of Easy Metrics

Guest: Dan Keto, President and Co-founder at Easy Metrics

Expertise: Business Systems Development, Operational Economics, Performance Management, Technical Leadership

Brief Recognition:

Dan Keto is a technology and operations entrepreneur with more than 25 years of experience building large‑scale business systems and operational intelligence platforms. He co‑founded Easy Metrics, where he serves as President and CTO, following his earlier success co‑founding Integrated Management Systems, a major distribution outsourcing company supporting labor‑intensive warehouse environments. He previously served as President of the Board of Trustees for the Millionaire Club Charity, one of the region’s largest workforce‑development organizations. A Distinguished Graduate of the U.S. Naval Academy in Economics and a graduate of Harvard Business School’s Owner/President Management Program

Episode:  Connecting Forecasting and Warehouse Decisions at Scale – with Jerod Hamilton of Tyson Foods

​Guest: Jerod Hamilton, Director 3PL Warehouse Strategy at Tyson Foods

Expertise: 3PL Warehouse Strategy, Distribution & Fulfillment Operations, Capacity & Planning Management, Large‑Scale Warehouse Management

Brief Recognition: Jerod Hamilton is a senior operations leader with more than 20 years of experience across large‑scale distribution, 3PL strategy, and warehouse network management. He leads 3PL Warehouse Strategy at Tyson Foods, following prior roles overseeing OCS warehouse strategy, outside warehousing operations, and dedicated capacity planning across Tyson’s national distribution footprint. Before joining Tyson, he spent over a decade in operational leadership at J.B. Hunt, managing complex transportation and logistics environments. He holds a degree from the University of Arkansas.

Unifying warehouse data as the foundation for visibility

Dan Keto surfaces a core vulnerability in warehouse operations by showing that leaders are accountable for cost, throughput, and service levels without access to a unified view of where labor, time, and margin are actually being consumed.

Robotics systems, automation equipment, WMS/WCS platforms, and equipment logs each capture part of the picture, but none of them reconcile into a single operational model. Keto emphasizes that this fragmentation doesn’t just obscure performance — it forces organizations into reactive management. As he explains:

“We have more data than ever, but we have less visibility than ever… You can’t optimize cost or efficiency when you’re constantly reacting to gaps you can’t see. Until the data is unified, leaders are stuck in crisis management instead of real decision‑making.”

— Dan Keto, President and Co‑founder, Easy Metrics

From the vantage point of day‑to‑day warehouse execution, Jerod Hamilton pushes the issue upstream into the planning environment. He notes that warehouses often absorb problems created earlier in the chain because supply planning, production planning, deployment planning, load planning, and sales forecasting all run on separate systems and timelines.

When these inputs diverge, the result is predictable: misplaced inventory, unexpected labor demand, and avoidable operational drag. Hamilton explains how these mismatches accumulate:

“You’ve got supply planning, production planning, deployment planning, and sales forecasting all running in parallel, each in its own system. None of them fully reconciles. That’s where the leakage comes from — tiny misses across dozens of workflows that add up to real money.”

— Jerod Hamilton, Director of 3PL Warehouse Strategy, Tyson Foods

A unified data model is what closes this gap. By aligning robotics, automation, and WMS data into a single structure that reflects how the operation actually runs, leaders gain visibility they can act on during the shift — not days or weeks later, after the cost has already hit the P&L.

Sequencing AI investment to avoid costly implementation failures

Dan Keto underscores a critical misconception in warehouse AI adoption: leaders often assume that adding AI to existing systems will unlock efficiency, when in reality, the underlying data structures are not ready to support it.

Warehouses generate enormous volumes of transactional data from robotics, conveyors, WMS/WCS platforms, and automation systems — but without transformation layers, conditioning models, and stakeholder‑aligned taxonomies, that data cannot support reliable AI outputs. Keto explains that applying AI too early doesn’t just produce hallucinations — it drives costs to unsustainable levels. As he puts it:

“If you try to apply AI before the data is unified, you’re going to get ridiculous amounts of hallucinations, and the expense structure is off the charts. Running an LLM against mathematical data is not a good exercise. You have to run all of that data through algorithms and conditioning models first.”

— Dan Keto, President and Co‑founder, Easy Metrics

Keto also highlights the cost asymmetry created by premature AI deployment. Without pre‑calculation layers and optimized data structures, the same analytical request can cost “a thousand times” more to compute — a gap that makes AI financially untenable at scale.

He draws a parallel to software development: AI can dramatically accelerate output for senior engineers who understand the context, but it produces unusable results for junior developers who lack the grounding to validate or correct it. Warehouse operations face the same dynamic. AI can surface anomalies, cost drivers, and staffing risks, but only when the underlying data is structured, contextualized, and aligned to how the operation actually runs.

Sequencing AI after the data foundation is built ensures that models operate on conditioned, mathematically coherent inputs, preventing unreliable outputs and runaway compute costs that result from deploying AI before the operation is ready.

Measuring network economics rather than departmental performance

Evaluating operations as a connected system prevents local efficiency gains from quietly erasing margin elsewhere in the network.

Hamilton describes how upstream planning layers move on different cadences and through different systems, and how the warehouse becomes the point where those inconsistencies surface. He notes that the problem isn’t any single workflow — it’s the number of planning streams that operate independently and land on the warehouse at the same time. Those include:

• Supply planning
• Production planning
• Deployment planning
• Load planning

When these inputs don’t line up, the warehouse absorbs the cost — misplaced inventory, unexpected labor demand, and workflow friction that no single department can diagnose in isolation.

Jerod emphasizes that these misalignments rarely appear as a single failure. They accumulate quietly across dozens of workflows, becoming visible only after the fact. As he explains:

“You don’t feel the impact of one bad handoff — you feel the impact of fifty. Each planning layer is doing its job in its own system, but none of them fully reconcile in time for the warehouse to act on it. By the time you see the problem, the cost has already hit your P&L, and it looks like an execution issue when it was really a network issue upstream.”

— Jerod Hamilton, Director of 3PL Warehouse Strategy, Tyson Foods

Keto points to a related dynamic on the automation and data side. High‑velocity robotics and engineered systems can dramatically improve one workflow, but if the surrounding processes can’t absorb the change, the gains evaporate. Keto notes:

“Operations are being asked to perform at levels that were unthinkable 15 years ago. The challenge is that the systems around that automation haven’t kept pace. You end up with one part of the operation looking incredibly efficient while another part is quietly absorbing all the friction.”

— Dan Keto, President and Co‑founder, Easy Metrics

Hamilton offers a concrete example of how this shows up on the floor. Automated storage systems continuously re‑warehouse pallets based on static rules, but they don’t ingest real‑time demand signals. When a fast mover becomes a slow mover overnight, the system doesn’t know. The pallet stays in a high‑traffic zone, adding seconds to every pull and increasing labor cost in a way that never appears in a single department’s KPI.

Keto sees the same issue in the data model itself. Operational, engineering, and executive teams each use different taxonomies and metrics, and without a unified structure, leaders eventually face a reset. As he puts it: “You eventually have to rip apart your data model and start over, because every group is defining efficiency differently. If the model isn’t unified, the operation ends up chasing contradictions instead of solving problems.”

The pattern is consistent: warehouse performance is shaped by how decisions intersect, not by how any single function performs in isolation. Evaluating the operation as a connected system prevents local improvements from quietly eroding margin elsewhere in the network.