Over the past two decades, I’ve witnessed the mounting pressures facing warehouses. Warehouses have long been the beating heart of supply chains, but the way they’re managed has changed dramatically—and are not always fast enough to keep up with rising complexity. The journey began with clipboards and paper-based workflows, where accuracy was limited, visibility was low and execution was largely reactive.
Today, after multiple waves of digital transformation, the industry is entering the next chapter: intelligent, adaptive warehouse management as the centerpiece for intelligent supply chain execution.
Phase 1: Paper and Manual Processes
In the late 1990s, warehouses ran on pens, paper, and institutional knowledge. Pick tickets were printed and walked across the floor; inventory updates were batched at shift end. Visibility was delayed, errors were common and managers relied on gut feel. As globalization and e-commerce reshaped demand, paper-based management showed its limits.
Phase 2: Digitization and Task Optimization
The first big leap was digitization in the early 2000s. Paper gave way to digital workflows with barcodes, RF scanners, and early warehouse management systems (WMS). Inventory accuracy improved, and processes could be tracked in near real time. Task interleaving, slotting strategies, and conveyor systems streamlined operations. Warehouses have become faster and more efficient, but focus primarily on cost reduction rather than adapting to changing business needs.
Phase 3: Multichannel Flexibility
Multichannel fulfillment in the 2010s redefined warehouse requirements. Pallet-based workflows gave way to piece-picking; networks expanded from single DCs to multi-node strategies. Warehouses had to support multiple fulfillment models and meet escalating expectations for fast delivery. WMS platforms evolved to connect with order management systems (OMS) and transportation management systems (TMS), but coordination remained patchy. Siloed processes meant disruptions cascaded through the network.
Phase 4: Analytics, Automation, and Early Machine Learning
The late 2010s ushered in analytics, machine learning, and data-driven visibility. Cloud-based WMS became common, and labor optimization, predictive slotting and robotics were deployed at scale. Demand forecasting, anomaly detection and transport delay predictions gave leaders better tools to anticipate problems. WMS, OMS and TMS each became “smarter” individually, with dashboards and insights that improved day-to-day decisions. But the gains were still limited by silos. Disruptions, an unexpected order surge, late replenishment, a delayed carrier pickup—continued to trigger reactive firefighting and manual interventions across the execution chain.
Phase 5: AI-Enabled, Adaptive Warehousing
Today, we are at the beginning of the next chapter. The vision now is to make the warehouse itself more intelligent through orchestration—able to anticipate, adapt and act in real time. In this phase, the warehouse stops being a siloed execution hub and becomes an adaptive node in the supply chain.
Three capabilities define this new phase:
Unified Data Layers:
Inventory, orders, and labor data are integrated to provide managers and AI agents with a single source of truth. Execution operates on a shared intelligence layer that interprets signals across OMS, WMS, and TMS. As a result, data becomes consistent, timely, and actionable.
Agentic AI: Embedded AI agents monitor operations, predicting issues like stockouts, replenishment delays or labor bottlenecks. Instead of waiting for problems to escalate, they recommend and even trigger actions—like reprioritizing replenishment or adjusting pick paths—before they affect the customer promise.
Connected workflows: Receiving, storage, picking and shipping are orchestrated in real time. If one task lags, the system adjusts downstream processes to keep orders on track. The WMS, OMS and TMS collaborate in real time, adjusting to shifting demand, inventory and transport conditions.
This is the leap from reactive firefighting to proactive orchestration. Instead of being managed from screens and reports, the warehouse itself becomes intelligent, learning from data, preventing disruptions and enabling teams to focus on exceptions to ship orders on time and meet customer SLAs.
Going Forward
Warehousing has come a long way—from clipboards and paper tickets to predictive analytics and automation. Yet even with these advances, too many warehouses are still fighting fires when inventory shortfalls, labor bottlenecks or replenishment delays ripple through operations.
At Infios, we envision this change. With AI-enabled intelligence embedded directly in warehouse workflows, operations become proactive, orchestrated and adaptive, transforming the warehouse into an intelligent hub.
But the story does not stop at the four walls. When warehouse intelligence relates to order and transportation systems, execution stops working in isolation and starts working in unison. This is the essence of intelligent supply chain execution: not just smarter warehouses, but a connected network where the WMS collaborates with OMS and TMS in real time to anticipate issues, keep promises and build resilience at scale.
Written by Richard Stewart, EVP Product & Industry Strategy
The post From Clipboards to Intelligence: The Next Chapter for Warehouse appeared first on Logistics Viewpoints.