Replenishment Strategies: Min/Max vs Demand-Driven

Replenishment is the quiet background process that keeps pick faces stocked so pickers never arrive at an empty location mid-wave, and choosing the wrong replenishment strategy either buries the floor in excess inventory or lets pick faces run dry at the worst possible moment.

Min/max replenishment sets a minimum quantity that triggers a replenishment task and a maximum quantity that defines how much to bring forward from reserve storage. It is simple to configure, easy for staff to understand, and works well for SKUs with reasonably stable, predictable demand. Its weakness is that fixed thresholds do not adapt automatically to sudden demand shifts, meaning a min level calibrated for normal months can trigger too late during a demand spike or too early during a lull, in both cases wasting either fulfillment continuity or labor.

Demand-driven replenishment adjusts trigger points dynamically based on recent order velocity, open order commitments, or even forecasted demand rather than a static threshold. This approach requires more sophisticated calculation but responds to a promotional spike or a sudden slowdown without needing someone to manually recalibrate min/max values for every affected SKU. It suits fast-moving, high-variability items where a static threshold would either overstock or understock too often to be reliable.

Rather than replenishing purely on quantity thresholds, time-phased replenishment stages pick faces ahead of a scheduled wave based on that wave's known order content, ensuring the exact quantity needed for the next planned wave is in place before pickers start. This works well in high-throughput operations with predictable wave schedules, since it avoids both premature restocking that consumes labor too early and reactive restocking that risks a mid-wave stockout.

Replenishment logic also needs to decide which reserve location supplies a given pick face when multiple reserve locations hold the same SKU. FEFO or FIFO rules typically govern this choice for perishable or lot-controlled goods, while for non-perishable items the decision often optimizes for shortest travel distance or clearing a location entirely to free up reserve space, a consideration that matters more in warehouses running tight on storage capacity.

A common operational failure is a replenishment task assigned to the same pick face a picker is actively working, creating a physical collision in a narrow aisle or forcing one worker to wait for the other. WMS task sequencing that checks for active pick tasks at a location before releasing a replenishment task there, or that schedules replenishment during natural lulls between waves, avoids this friction without requiring supervisors to manually coordinate every replenishment run.