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The Question

Will another picking station, or another shuttle, get more orders out the door?

Case study · Retail distribution · Footwear

Client work product

Picking stations waiting on the conveyor

Client
Browns Shoes
Industry
Footwear retail: distribution
Decision type
Throughput and bottlenecks (primary); capital and capacity (secondary)
Geography
Canada (national store network, Montreal headquarters)
Decision
Which equipment change raises picks per picker, before buying it

Client brief

Browns Shoes, a family-owned Montreal retailer founded in 1940, supplies its stores across Canada from an automated distribution center. Shoeboxes live in an automated storage and retrieval system. When an order drops, shuttle systems send a tote of the right shoe family onto a conveyor loop, the tote rides to a picking station, an employee pulls the pairs the order needs, and the tote rides back into storage. At certain times the pickers stand waiting on the conveyor instead of picking. The team can see four ways to fix it: two more picking stations, activating shortcuts on the loop, a fourth shuttle system, or a longer loop. Each one costs capital, and the conveyor's interactions make it hard to tell which would raise productivity.

§ 1

The decision

A picking station is only as fast as the totes that reach it. Every tote leaves storage, travels the loop, waits its turn at a station, and travels back, while many others share the same track. When the loop congests, totes arrive late, and the pickers' time goes to waiting.

The obvious fixes pull in different directions. More picking stations add hands but may add more totes to a loop that is already full. A fourth shuttle system pushes more totes out of storage, which helps only if the conveyor can carry them. Shortcuts and a longer loop change how long each tote circulates. No one could say from a spreadsheet which change, or which pair of changes, would raise productivity per picker.

§ 2

A model of the loop as it runs

SimWell built a discrete-event simulation of the distribution center's picking system in Arena: storage, shuttles, both conveyors, the four station spurs, and the pickers, each running at the system's actual parameters. The model reproduced the system closely enough that Browns' team recognized its own operation in it.

An Excel interface lets an analyst change any parameter and rerun. The model shows where the constraint sits under each configuration and reports the measure the decision turns on: productivity per employee at the picking stations.

Exhibit A · Totes on the loop
Picking loop · Schematic · Scales illustrative Layout Current Tote Picker picking Picker waiting on the conveyor

Results

What changed

SimWell delivered the model to Browns' team to run themselves. Each equipment option, alone or in combination, could be tested against productivity per picker before any capital was committed, and the team could see where the constraint moved after each change.

SimWell helps operators make better decisions under uncertainty. We build simulation and optimization models that reflect operational reality, test scenarios before committing resources, and transfer capability so teams can run decisions independently.

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