Warehouse managers track picks-per-hour at the aggregate level but lack the granularity to distinguish between wave planning inefficiency, slotting-driven travel time, and individual performance gaps. Overtime budgets balloon during peak periods because labor allocation models rely on historical headcount rules rather than real-time workload forecasts, and dock-to-stock backlogs accumulate invisibly until they disrupt outbound cut-off times.
Built For
DC Operations Manager tracking productivity across 250 associates in a 3-shift fulfillment operation with a 99.5% SLA target
Deploy a pre-trained specialist agent instantly instead of building one from scratch.
Operations Manager
Decomposes picks-per-hour by wave, zone, and shift to surface productivity bottlenecks and overtime risk before cut-off.
Warehouse Manager
Connects labor productivity gaps to slotting configuration, replenishment timing, and dock-to-stock cycle delays.
Workforce Planner
Builds demand-driven staffing models that align headcount to projected pick volume by wave and day of week.
Labor productivity analytics that decompose picks-per-hour by wave, associate, zone, and shift, with overtime prediction and dock-to-stock cycle time benchmarking.
Drag and drop your CSVs and start analyzing right away, with the complex pipelines handled for you.
Ask questions in plain English, get instant answers.
The second-shift Wave 3 is averaging 142 picks-per-hour against a 175 target, a 19% shortfall that traces to a replenishment stockout in the Zone B golden slots rather than a staffing gap. At the current pace the outbound 6pm cut-off slips by about 40 minutes tonight, threatening your 99.5% SLA. Trigger a Zone B replenishment now and flex two associates from Wave 4 to recover the queue before cut-off.