This data has been adapted from a real warehouse. If you use it for a purpose other than Georgia Tech’s ISyE 6335 class, please include the following acknowledgement “This data courtesy of warehouse-science.com”.

The customers of this warehouse are retail stores. Most of them are relatively small and order much of their stock in piece quantities. The data below shows the resulting activity in the warehouse to pick pieces.

All distance measurements are in centimeters.

- Do all SKUs have dimensional data? (Which do not and how should this be dealt with?)
- What are the shortest and longest dimensions of the unit of storage?
- What are the lightest and heaviest pieces? Cases? (Why is this useful to know?)
- What SKUs occupy the most volume? (Why is this useful to know?)
- What SKUs are the most dense (weight / volume)? (Why is this useful to know?)
- Do any SKUs have case height as their largest dimensions? (Why is this useful to know?)

This is a history of piece-picks, not complete customer orders.

The first several questions are checks to see how stable this data is, how useful it might be in predicting future warehouse activity.

- What is the time span covered by this data?
- Do all the SKUs from the Item Master appear here? (Which do not?)
- Do all the SKUs here also appear in the Item Master? (Which do not and why might this be so?)

The following questions seek to discover patterns of customer orders.

- What are the most popular SKUs?
- What SKUs are ordered in the greatest quantity?
- What SKUs are ordered in the greatest physical volume?
- What fraction of orders are for a single pick-line?
- What is the average number of piece-picks per day? Per day of the week? What are the distributions?
- What is the average number of pieces per pick? The distribution?
- What were the pick rates of the workers?
- Is there any evidence of significant seasonalities in numbers of orders or quantities ordered? Can you have confidence in any seasonalities?
- Are there any significant product affinities?

Up to now pieces have been picked together with cases. This created handling problems and so the decision has been made to create a separate process and area for piece-picking.

The initial design calls for 30 bays of flow rack along each side of a conveyor for a total of 60 bays. Each bay is 5 feet wide and 10 feet deep. Shelf heights can be chosen as necessary.

There would be three work “stations” at equal intervals, with each station staffed by a pair of workers, one on each side of the conveyor. Each worker would be responsible for picking out of five bays.

Each order is assigned to one or more totes and each tote holds pieces for a single order. Totes flow in one direction through the flow rack. When a worker finishes picks from his zone to a tote, the tote will either be diverted across to the worker on the opposite side of the conveyor (the same work station) or else will be transported down to the next station. Product will be restocked from nearby pallet rack.

Assume 120 picks per hour from flow rack and 60 picks per hour elsewhere. Assume about 15 restocks per hour.

Use historical data as forecast for the near future.

- Critique this design. Discuss what can slow the flow of completed orders. What constraints does this design place on shipping?
- Which SKUs should be stored in the flow rack and where? What is the total labor required to move the forecast orders?
- How can you reduce the number of regions each tote must visit? (Assume each tote holds a complete order.)