The Basics    

In warehouses that process a complex product mix, the placement of arriving inventory is fast and furious. When a truck unloads its shipment at the dock door, there’s little time to identify the perfect location to store everything in the load.

Until now, the solution to this challenge has been found in a mix of pre-planned slot locations for items that have predictable long-term distribution patterns, and more random placement of other inventory, which typically accounts for 70% — or more — of a facility’s capacity. When it’s time to pick products from those slots, data is processed to make the best of a sub-optimal storage situation.

Warehouse labor and/or robots are deployed to pick products as efficiently as possible, but this requires complex movements and sometimes-lengthy travel routes to assemble outbound shipments. Managers watch slotting inefficiencies grow over a period of months. Only when performance starts to significantly decline is it worth requesting a plan to overhaul a facility’s slotting system — a task that often needs to be outsourced to sophisticated and costly consultants. Once a new plan is finally implemented, market and other changes quickly start to degrade its effect.

Managers have learned to live with this reality, but today that’s changing.

The Future

Systems powered by machine learning (ML) now can make slotting changes feasible to accomplish on a daily basis. For the first time, warehouse managers can make continuous slotting improvements that cut labor costs, boost throughput, and open new opportunities to meet customer demands. Warehouses that fail to adapt risk losing their competitive advantage.

Though some facilities — those that process just a few SKUs or bulk commodities — can continue to be managed with only periodic storage system reviews, other warehouses must start to explore the new generation of slotting solutions. Facilities that churn through thousands of SKUs, experience frequent seasonal product variations, or sudden surges caused by sales promotions and influencer-driven demands are prime targets for the new slotting systems.

Solving slotting challenges requires the management of immense volumes of data. Maturing ML technologies, married with good forecasts, are up to the task.

Going forward, managers will generate a list of slotting updates with the push of a button at the start of each day, rather than implementing complex changes months apart. These daily recommendations can be prioritized based on their benefits and the time needed for implementation. Then, labor can be deployed to effect the changes when normal activity fluctuations free a few team members during normal shifts.

ML-driven slotting systems available today can increase throughput 20–40% by recommending the best inventory locations based on SKU velocity, SKU affinity, product/slot information, pick paths, and other data.

A major outdoor equipment parts supplier is an early adopter of such a system and its COO says, “The ability to quickly and frequently make optimal slotting decisions is extremely impactful because it contributes to optimal picking efficiencies and meeting our customer promises. If we are making those decisions in an agile way — dynamically — we are gaining the most efficiencies.”

The future has arrived. It’s high time to turn a sub-optimal slotting system into a new competitive advantage.

Resource Link: https://www.lucasware.com/slotting/