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If a picker walks twelve kilometres a day, whose problem is that?
Warehouse productivity is mostly decided when stock is put away, not when it is picked.
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If your pickers walk twelve kilometres a day, the question is not whether they walk fast enough. It is why items that get ordered together sit at opposite ends of the building.
In most warehouses, walking is the largest component of pick time — far larger than actually handling the goods. And that walking distance is set by two decisions: where stock was put, and how orders were grouped.
Both are data problems, not effort problems.
Slotting that fell behind reality
Most warehouses are laid out by product category or by code. It is easy to understand and easy to run on day one.
The trouble is that ordering patterns keep moving. An item that sold well two years ago still occupies prime space near packing, while today's fast mover sits at the end of aisle twelve, because that was where there happened to be space when it arrived.
Analysing which items are frequently ordered together and moving them near each other is not sophisticated analysis. A great many warehouses have never done it, because nobody owns the question.
What a system contributes
Batching orders. Rather than one trip per order, group orders whose items sit in nearby zones into a single trip. Good batching removes more walking than any change of equipment.
Routing the pick. Once the list is known, the shortest sequence can be computed, instead of having the picker follow the order printed on the pick list — which is usually sorted by item code.
Suggesting put-away locations. When stock arrives, propose a location based on pick frequency and affinity with other items, rather than wherever is empty.
Forecasting volume. Tomorrow's order count is predictable from history plus the calendar and planned promotions, which turns staffing from calling in extra people at eight in the morning into planning.
Think about the people doing the work
A system that measures every movement a picker makes walks straight into surveillance, and what usually follows is not productivity but turnover.
The difference is measuring to improve the system versus measuring to rank people. The same data serves both, and which one you choose determines whether the project gets cooperation or resistance.
A warehouse using data to cut walking gets pickers who point out what the system does not know. A warehouse using it to rank gets pickers who learn how to make the numbers look good in ways unrelated to the work.
Automation comes after
Robots and automated conveyance have their place. But investing in automation on top of a layout nobody has re-slotted only makes the unnecessary walking happen faster.
Get the data and the layout right first, then look at what remains and whether it justifies machinery.
What GIPSIC does here
We build both software and hardware, including in-building robotic transport running in a hospital. That experience taught us the hard part is not making a robot move. It is making it work alongside people and an existing process without creating extra work.
We will say so if we think your problem should not start with machinery, and explain the reasoning and the trade-off.
If you would like to talk about your warehouse, get in touch.
About the author
Film — Wisit. A businessman who still does his own BA work more often than he probably should, and writes a fair bit of code, front and back. Runs two or three small businesses. Follows technology and business obsessively, in Thailand and everywhere else. Off the clock: physics, astronomy, DIY, and anything to do with networks. Music always on, though he cannot sing. Plays instruments anyway, badly. Plays a lot of sport, racket sports above all. Not much of a traveller by himself, but happy to take Mint anywhere in the world. A man who fears — sorry, loves — his wife. One flaw: he barely touches video games.
Written with Claude Opus 5