[2026 Update] The Urgent Need for AI-Driven Visualization of "Walking Loss" Reduction and Route Optimization in Warehouses

The biggest factor hindering productivity at logistics sites is actually not the work itself, but the "movement" associated with it. In our consulting projects, it is not uncommon to see cases where more than 50% of picking time is spent "just walking." How can we reduce this non-value-added "walking loss"? In the logistics DX of 2026, route optimization using AI is no longer just an option, but an essential strategy for surviving labor shortages. This article explains data utilization techniques tailored to practical operations.

1. Why "Walking Loss" is Left Unaddressed: The Limits of Intuition-Based Operations

In actual support projects, veteran workers often firmly believe that "the current layout is the most efficient." However, when comparing shipping frequency (ABC analysis) with actual walking distances, contradictions statistically emerge, such as high-frequency items being placed deep within the shelves. A common occurrence on-site is that shelf allocation remains fixed even though shipping trends have changed due to specific seasons or campaigns, resulting in operational delays.

In "Own EC Site Construction and Growth Support," shipping delays due to increased orders directly lead to brand damage. Walking loss is a "hidden cost" that is difficult to see, and eliminating it requires objective log data that removes subjectivity. In our consulting projects, we start by measuring the total daily walking distance, which often reaches several kilometers.

2. Quantifying "Waste" with AI Cameras and Heatmaps

The first step in AI route optimization is the visualization of the current status. The latest AI camera solutions identify workers' helmets or clothing and automatically record their movement trajectories. The acquired data is output as a "heatmap," making it possible to identify at a glance which aisles are congested and which areas have become "dead space."

As the data above shows, if movement time—which accounts for more than half of the work time—can be reduced, picking efficiency will improve dramatically. In actual support projects, we identify points with frequent "crossings" or "backtracking" through AI analysis and immediately achieve a 10–15% efficiency improvement by introducing one-way rules or sliding shelf layouts.

3. Automatic Generation of Shortest Paths Combining SLP Methods and AI

The next step after visualization is layout optimization. We update Systematic Layout Planning (SLP), a traditional factory layout method, for the modern era and combine it with reinforcement learning via AI. Specifically, based on combinations of products likely to be purchased together (basket analysis), we run tens of thousands of simulations to place them in close proximity.

In this process, the data analysis expertise cultivated through Own EC Site Construction and Growth Support is utilized. The AI presents an "optimal picking sequence" that considers not only the order of shipping volume but also ease of packing and weight balance. Losses common on-site, such as "heavy items coming last, requiring re-packing," can also be incorporated into the logic at this stage.

4. Data-Driven Warehouse Management Required in 2026

In the logistics sites of 2026, optimization is not a one-time event; "dynamic slotting" is required. AI performs real-time shipping forecasts, and operations where Automated Guided Vehicles (AGVs) rearrange shelves at night to minimize the next day's workload are becoming a reality. In our consulting projects, we are convinced that as a preliminary step to such advanced automation, it is crucial to first establish a "culture of changing layouts based on data." Sites that communicate through numbers rather than intuition demonstrate the highest resilience (adaptability).