A heatmap for the warehouse

In the records every position is an ordinary line, but one is picked fifty times a day and another five. A picker walks all day, and a large part of that walking may be down to the fastest-moving goods standing far from the exit. That is hard to see in a table of numbers but visible at a glance on a floor plan. A heatmap shades the warehouse plan by colour: the busier the spot, the darker the cell.

Collecting the data

Each pick line needs a location code, for example aisle C, bay 5. If 1C does not track locations, have the code written by hand on the pick sheet for a week or two. The period should be at least 4 weeks; look at a holiday week separately.

In Excel: a pivot table with aisles in rows, bays in columns and the number of picks as values; then switch on a colour scale in conditional formatting. Google Sheets and other programs work the same way.

An example

Picks per week, with bays numbered from nearest to the exit to farthest, 1 to 6:

• Aisle A: 420, 310, 180, 90, 60, 40 (total 1,100).
• Aisle B: 380, 260, 150, 80, 50, 30 (total 950).
• Aisle C: 90, 110, 150, 210, 340, 490 (total 1,390).
• Aisle D: 60, 70, 90, 100, 110, 130 (total 560).

There are 4,000 picks in all. In bays 1-2 of aisles A and B there are 420 + 310 + 380 + 260 = 1,370, or 34 percent: fast goods sit close to the exit, which is good. But in bays 5-6 of aisle C there are 340 + 490 = 830, or 21 percent, and that is the spot farthest from the exit. Such a "hot spot" usually appears when a new fast-moving category is put on whatever space was free.

Let us calculate. Bay 1 is 10 m from the exit and bays are 4 m apart, so bay 6 is 30 m away. The round trip is 60 m and 20 m. If we swap the goods of bay 6 and bay 1 (bay 1 in aisle C is picked 90 times): (490 − 90) × (60 − 20) = 400 × 40 = 16,000 m, which is 16 km a week. At 4 km an hour that is 4 hours, and about 16 hours a month.

What to do after the map

1. Move the "hot" positions closer to the exit and push the "cold" goods farther away.
2. Check whether pickers get in each other's way in the busiest bays: if needed, split fast goods between two aisles.
3. Put "hot" spots with frequent errors into cycle counting: an error costs more there.
4. Before moving, count the balance and update the new location in the document, or the goods will get "lost".

Rules for colour

• Light and dark shades of a single colour, not a rainbow.
• Use the same scale on maps you intend to compare, otherwise two weeks cannot be set side by side.
• Write the number in the cell too: the colour is approximate, the number exact.
• If bays differ in size, divide the number of picks by the number of bins.
• The map should stay readable on a black-and-white printer: keep the difference between light and dark, since reports are often printed on paper.

What the map does not show

• The number of pick lines reflects walking, while weight depends on quantity.
• Goods that are picked together should sit together; the map does not say so.
• Season: a November map is no use for March.
• Height: the "hottest" goods should be at waist level, not on the floor or under the ceiling.

The same technique works for count results. Discrepancies per 100 positions counted: aisle A 1.1; B 1.4; C 3.9; D 1.8. In aisle C there are 2-3.5 times more errors than elsewhere, which matches the activity map: where there are many picks there are many errors too. Put the two maps side by side and look for the cause.

Conclusion

A heatmap turns a table of numbers into a plan: where the fast goods lie and where errors gather is visible at a glance. From stock-count results CountLab builds a map of errors by zone and presents it with the management report.