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”.
An office-products wholesaler
This distributor receives orders until early evening and then picks and ships in the night so that the product is at the customer’s site at the start of business the next day. Because response time is critical, this DC set up a fast-pick area.
Most items are relatively small (staplers, toner cartridges, pens, etc.) and are stored in bin-shelving. Here is a small subset of SKUs. The data fields are as follows:
SKU ID: The unique identifier for this SKU
Length of the storage unit, in inches
Width of the storage unit, in inches
Height of the storage unit, in inches
Pieces per case (that is, the standard number of pieces in each storage unit)
Pieces sold during recent reporting period
Picks (number of times this SKU was requested by a customer)
during recent reporting period
Maximum number of storage units likely to be held in the
Use the fluid model to answer the following questions. All computations can be done on a spreadsheet.
Rank the SKUs by the strength of their claim to forward storage if slotting to minimize restocks.
Assume you have decided to store 10 SKUs in a forward pick location and will give each one the same amount of space. Which SKUs should you choose to minimize total labor costs (picking plus restocking)?
Suppose five sections of static shelving are available, with 5 shelves to a section. Each shelf opening is 12-3/4 inches high x 18 inches deep x 41.25 inches wide. What SKUs would you store here and in what amounts if you want to minimize total labor? Assume picks from the shelving are estimated to average 0.5 minutes each, compared with 2 minutes each if picking from bulk storage. Restocks are estimated to average 2.75 minutes each.