Order Picking Methods: Choose by Lines per Trip, Then Test

Four methods cover most warehouses. Discrete picking collects one order per trip. Batch picking collects several orders per trip. Zone picking keeps each picker in one area while orders pass between pickers. Wave picking releases orders to the floor at set times. All four decide how many pick lines share a trip.

A pick line is one item and quantity on an order. A trip runs from the packing area through the aisles and back.

Picking is a large share of cost. A 2006 literature review from Erasmus University Rotterdam, citing earlier estimates, puts order picking at up to 55% of warehouse operating expense.

When orders are large, picking each on its own works. When orders are small, grouping a set of them into one trip cuts travel. Orders can be grouped by the proximity of their pick locations or by time window, meaning all orders that arrive in the same interval (De Koster, Le-Duc and Roodbergen).

Walking per trip climbs steeply for the first 20 pick lines, then flattens at about 254 m, the full sweep of every aisle in our 10-aisle model. The flattening follows lines per aisle: at 20 lines about 88% of aisles hold a pick, and at 40 lines about 99% do.

The first line costs 47 m, but lines 21 to 40 add only 21 m in total. In our model the walk flattens at about 2 to 4 pick lines per aisle. A 30-aisle warehouse needs roughly three times as many lines per trip to reach the same point.

Anyone can rebuild this in a spreadsheet or short script from these rules. Real racking differs, and picks cluster in fast-moving aisles, so treat the lines-per-aisle figure as a starting point to test, not a law.

Batch picking saves most when orders are short. In our model, a five-line order walks 129 m when picked alone, 58 m in a batch of 4 and 32 m in a batch of 8. That is 55% and about three quarters less.

Once a trip holds about 4 lines per aisle (40 lines here), walking per order is roughly the full sweep of 254 m divided by the orders in the batch. Five-line orders reach that point at a batch of 8. A batch of 12 walks 21 m per order, only 11 m better, while adding sorting work and waiting.

Our model groups orders at random. We expect grouping by shared locations to do better, so treat these figures as a floor (our inference).

Batching has two costs the walking figures leave out. The first is sorting items into the right order, either at the end or at each pick if the cart carries one tote (the container holding one order) per order. The second is waiting. At 25 orders an hour, orders arrive about every 2.4 minutes (60 ÷ 25), so the first order in a batch of 4 waits about 7 minutes (3 × 2.4) for the fourth.

Our recommendation: size batches at about (2 to 4) × your aisle count ÷ lines per order, capped by the tote slots on your cart. For 5-line orders in 10 aisles, that is 4 to 8 orders. Discrete picking is the right choice when one order fills the cart or pallet, or when orders arrive too slowly to group without delaying dispatch.

Zone picking is meant to cut elapsed time, and our model measures only walking. In our model, five-line orders walk 129 m in total with one zone, 118 m with two zones and 98 m with five, about a quarter less. At 20 lines the totals are 232, 229 and 210 m.

In our reasoning, the elapsed-time gain comes from pickers working in parallel and learning their own area, and zones can also be assigned by handling needs such as high-value or temperature-controlled stock. The cost is handing orders between zones and keeping every zone equally busy, which our model leaves out. A 1988 simulation by Mellema and Smith, summarised in the De Koster review, suggested that combining batching and zoning raises output per picker-hour. That is not a result from our model.

Wave picking answers a different question: when, not where. It releases orders at set times, for example before a carrier collection. Batch inside each wave.

Our recommendation: choose zone picking when the aisles are too many for one picker to sweep within your dispatch window. Otherwise batch.

For 200 five-line orders a day, batches of 4 save about 4 hours of walking. That is worth roughly ₹265 to ₹420 a day at the assumed Tier 1 metro pay and ₹210 to ₹300 at Tier 2 pay, before sorting time.

The cash is small at this scale. The value is capacity: 3.9 hours is about half a picker's day. At 1,000 orders a day the same batching frees nearly 20 hours, worth about ₹1,300 to ₹2,100 a day at the metro proxy. Swap in your own wage and walking speed.

Start with batch picking for short orders at enough volume to group them, and discrete picking otherwise. Then prove it on your own floor. These starting points are our recommendations.

Before testing, number bins along the walking route so a ticket sorted by bin is the route. Place fast sellers nearest the packing area, using ABC analysis, which ranks stock by how much it sells. Treat every bin a picker empties as a free check: confirm it is empty, and count it if it is not, as part of a cycle counting routine.

Our warehouse management feature generates pick tickets that list the items to pick and the warehouse bin location to pick each from, so pickers do not search for stock.

For lot or batch-tracked products, we direct picks to the lots that expire first, known as first-expired, first-out (FEFO); see batch expiry software for FEFO operations. For other items you can use first-in-first-out (FIFO) picking. We support barcode-based receiving, palletising, pallet labels and putaway into bins, and pick tickets with packing slips for shipments, across multiple warehouses.

Today our pick tickets list the items and bins to pick for each shipment. If you want orders grouped into batches, zones or waves, we can build that workflow to your rules.

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