Manifest Vegas | Dan Keto, Easy Metrics, on Warehouse Performance Management
•February 18, 2026
0
Why It Matters
A unified data model is the prerequisite for extracting value from AI and achieving scalable, manufacturing‑style efficiency in modern warehouses, directly impacting supply‑chain profitability.
Key Takeaways
- •Warehouse management evolving into performance engineering model
- •Unified data model essential before AI deployment
- •Fragmented robotics data hampers profitability insights
- •AI investments risk sunk costs without data foundation
- •Next 18 months focus on data, not new tools
Pulse Analysis
The warehouse sector is undergoing a paradigm shift, moving away from labor‑centric metrics toward a performance‑engineered framework reminiscent of manufacturing plants. This transition demands real‑time visibility into every transaction, from inbound receiving to outbound shipping, and requires integrating data streams from robotics, automation software, and manual processes into a single analytical layer. By treating the distribution center as an engineered system, operators can pinpoint bottlenecks, balance throughput, and align labor with machine capacity, unlocking higher utilization rates and lower operating costs.
Despite the buzz around artificial intelligence, many supply‑chain leaders stumble because their data architecture is fragmented. Disparate sources—warehouse execution systems, robotic controllers, and legacy ERP modules—often speak different languages, preventing the creation of a unified KPI dashboard. Without a consolidated data model that maps transactional events to business outcomes, AI algorithms lack the clean, contextual inputs needed for accurate forecasting and optimization. Consequently, AI initiatives risk becoming sunk costs rather than strategic differentiators.
Looking ahead, the next 18 months will be defined by disciplined data transformation rather than the rollout of new tools. Companies that prioritize building a robust, unified data foundation will be positioned to leverage advanced analytics, predictive maintenance, and autonomous decision‑making at scale. This data‑first approach will enable distribution centers to operate like high‑precision factories, where throughput is fixed and efficiency gains come from continuous engineering improvements rather than incremental labor adjustments. Executives should therefore invest in data governance, integration platforms, and cross‑functional KPI alignment to future‑proof their operations.
Original Description
BizClik attended Manifest in Vegas to speak with Dan Keto on the shift from traditional labor tracking to a holistic, manufacturing-inspired engineering model for distribution centers.
Dan Keto is the Co-Founder and President of Easy Metrics, a leader in warehouse performance and labor management. With deep expertise in warehouse optimization, Keto helps organizations navigate the transition from simple human-led operations to complex, high-tech environments. His perspective is critical for leaders attempting to reconcile the "hype" of AI with the practical reality of fragmented data across robotics, automation, and manual systems.
In this discussion, Dan Keto outlines the fundamental evolution of the warehouse from a labor-centric hub into a complex "Warehouse Performance Management" environment. As facilities increasingly adopt robotics and diverse software systems, the challenge has shifted from simply managing people to ingesting data from dozens of disparate sources. The goal now is to create a unified picture of cost, profitability, and performance for every single transaction within the business.
Keto issues a sobering warning regarding the current industry obsession with Artificial Intelligence. He notes that while the push for AI is massive, many companies are hitting a wall because they haven't solved their "data problem." Before AI can be effective, organizations must build a heavily transformed, unified data model that aligns transactional data with the specific KPI requirements of business stakeholders. Without this foundational layer, AI investments risk becoming a "sunk cost" rather than a competitive advantage.
Looking ahead, Keto predicts a major paradigm shift: the transformation of the distribution center into a manufacturing-style operation. Because automation and robotics have fixed throughput capacities—unlike human labor, which can be "dialed up or down"—supply chain leaders must adopt a holistic engineering model. The next 18 months will not be defined by flashy new tools, but by the "data story"—the disciplined process of getting data properly organized and transformed to optimize facility-wide throughput.
Follow the Supply Chain Digital stories further:
Twitter: https://twitter.com/SupplyChainD
Newsletter: https://mailchi.mp/supplychaindigital.com/signup
0
Comments
Want to join the conversation?
Loading comments...