Article - 014: Predicting Demand for Smarter Storage with Machine Learning

Charles Barrett
May 31
2 min read

The Challenge: Poor Slotting Slows Everything Down

In many warehouses, fast-moving products are often stored far from dispatch areas due to fixed or outdated slotting strategies. This results in:

Longer pick paths
Wasted travel time
Increased labor cost
Bottlenecks during peak fulfillment

Especially in high-volume environments like retail, FMCG, or e-commerce, every extra second spent retrieving items compounds into hours of lost efficiency.

Enter Machine Learning: Predicting SKU Velocity

Machine Learning (ML) offers a smarter way forward by predicting SKU movement based on multiple data points. It enables your WMS to pre-position fast movers closer to high-traffic zones like outbound staging or picking areas.

Here’s what ML models typically analyze:

Past Sales Data: Understand SKU velocity across stores, regions, or channels.
Seasonality and Promotions: Identify items that surge during festive seasons or sales events.
Warehouse Movement History: Track pick frequency, replenishment cycles, and dwell times.

How It Works in Practice

Train the Mode l ML algorithms are fed historical warehouse and sales data (3–12 months is typical for good accuracy).
Generate Predictions The model identifies which SKUs are likely to move fastest in the upcoming weeks.
Feed into WMS These predictions guide your WMS to:
- Re-slot inventory closer to pick lines
- Allocate prime zones for high-demand items
- Set replenishment thresholds more precisely
Continual LearningAs real-time data flows in, the model adjusts — refining predictions over time.

Benefits of Predictive Slotting

Faster Picking Workers travel shorter distances, completing tasks more quickly.
Lower Labor Costs Time savings add up, especially in large or multi-shift operations.
Better Space Utilization Prime slots are reserved for what really matters: fast movers.
Improved KPI Performance Boost metrics like order cycle time, pick accuracy, and lines picked per hour.

Final Thought

With tools like Infor WMS and embedded ML models, warehouses no longer need to rely on static slotting or guesswork. Predictive storage strategies help operations stay agile, efficient, and scalable — especially in fast-changing retail or omni-channel environments.