Pick Path Optimization

Picking is the single most labor-intensive activity in most warehouses, and walking accounts for a large share of a picker's time — commonly estimated at 50% or more of the total pick cycle. Pick path optimization is the practice of sequencing pick tasks so a worker (or robot) travels the shortest sensible route to collect everything on a list, and it is one of the highest-return improvements a WMS can deliver.

A pick task has four components: travel to the location, search/confirm the item, extract the quantity, and travel to the next location. Of these, travel is usually the biggest and most reducible cost — extraction time is fairly fixed by the product itself, but travel time depends entirely on the order in which locations are visited and how the warehouse is laid out. A picker given a list sorted by SKU number rather than by physical location might walk back and forth across the same aisle five times to fulfill one order; the same order sequenced correctly might take one pass down the aisle.

• S-shape (serpentine) routing: the picker travels down each aisle that contains a pick and crosses to the next only at the ends — simple to implement and works well when picks are spread evenly across aisles
• Largest gap / return routing: the picker enters and exits each aisle from the same end when picks are clustered near the entrance, skipping the far end entirely when nothing there needs picking
• Midpoint routing: the warehouse is split into a near half and far half, and the picker chooses to enter from whichever end minimizes total distance for the picks in that aisle
• Optimal/algorithmic routing: more advanced WMS platforms run a shortest-path or traveling-salesman-style calculation across all pick locations for the order, which can outperform simple heuristics for orders that span many scattered locations

To generate a pick path, the WMS needs three pieces of accurate data: the physical coordinates of every storage location, the current velocity and popularity of each SKU (so frequently picked items can be prioritized in the routing logic), and the layout constraints — aisle width, one-way sections, elevation for multi-level racking. It then combines the picks required for one order (or a batch of orders, depending on the picking method) into a single route and pushes that sequence to the worker's handheld device or voice headset, one stop at a time, confirmed by a barcode scan at each location.

Even a perfect routing algorithm can't overcome a bad layout. Cross-aisles (short connecting aisles partway down a long rack row) let the algorithm cut across instead of always walking to the end, which meaningfully shortens routes in wide warehouses. Placing high-velocity SKUs near the start of the most common routes, keeping heavy or bulky items on lower shelves to avoid unnecessary reach or lift time, and avoiding one-way aisle bottlenecks during peak hours are all layout decisions that either amplify or waste the benefit of good routing software.

The metric to track is picks per labor-hour (or lines per hour), alongside average travel distance per order. Warehouses moving from unoptimized, SKU-sorted pick lists to a properly sequenced routing method commonly report picks-per-hour improvements in the range of 15-30%, without adding staff or equipment — purely from cutting wasted walking. Combined with wave, batch, or zone picking strategies (covered in a companion article), pick path optimization is typically the single biggest lever a warehouse has to increase throughput without capital investment.