ORDER PICKING OPTIMIZATION

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World-Class Warehousing and Material Handling

Table 8.5 Bin-Shelving Slotting Example

Item ID

Popularity

Cube Movement Pick Density

7 ft 3 /month 4 ft 3 /month 3 ft 3 /month

20 requests/ft 3 27 requests/ft 3 25 requests/ft 3

A

140 requests/month 108 requests/month 75 requests/month

B

C

families of items is critical to success in managing the overall work content in the warehouse. Unfortunately, many warehouse managers and analysts stop with popu larity in their search for slotting criteria. Popularity is used singly to assign items to storage modes, to allocate space in storage modes, and to locate items within storage modes. Let’s consider the example of golden zoning a section of bin shelving. The objective is to maximize the amount of pick ing activity that is done at or near waist level. Assume that 7 cubic feet of space is available in the golden zone. Suppose that there are three items we are considering for slotting. The slotting statistics for the three items are recorded in Table 8.5. Suppose that we decide to store a month’s supply of material in bin shelving. Item A requires 7 cubic feet, item B requires 4 cubic feet, and item C requires 3 cubic feet. Suppose that we rank the items based on popularity alone to determine the order of preference for assignment into the golden zone. (Remember, the golden zone has 7 cubic only feet of capacity.) With popularity ranking, item A will be assigned to (and will exhaust the avail able space in) the golden zone. There will be 140 visits to the golden zone per month. (Remember, we are trying to maximize the number of trips to the golden zone.) Is this the best we can do? Absolutely not! Suppose that we assign items B and C to the golden zone. In this case, there will be 183 trips to the golden zone. Had we used pick density as the criterion for preference ranking, we would have maximized the activity in the golden zone. This is why this measure of picking activity is so critical to the success

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