The Pareto rule in the warehouse

The stock count is over: shortages in 74 positions. The report is thick, and checking every line would take a week. Where to start? The answer comes from a rule noticed by the Italian economist Pareto: a small number of causes produce most of the result. Applied to shortages it is called Pareto analysis: you find out which positions, zones and causes make up most of the loss.

By position

The warehouse counted 1 200 positions, and 74 of them show a shortage, 21.6 million so'm in total (at cost). Sort the shortages in descending order of value. The first ten positions: 4.1, 2.6, 1.9, 1.5, 1.1, 0.9, 0.8, 0.7, 0.6 and 0.5 million so'm, 14.7 million together, which is 68 percent. The remaining 64 positions add up to 6.9 million, about 108 thousand so'm each on average. The cumulative share shows the same: the first position is 4.1 ÷ 21.6 ≈ 19 percent, the first three 8.6 million ≈ 40 percent, the first five 11.2 million ≈ 52 percent.

So only 0.8 percent of the 1 200 counted positions, and 13.5 percent of the 74 with a shortage, account for almost two thirds of the loss. If instead of checking everything evenly you concentrate on these ten, the workload drops sharply.

By zone and cause

Split the same 21.6 million so'm by zone. Suppose zone A has 3.4 million, zone B 2.6, zone D 3.7, and zone C, by the receiving and dispatch door, 11.9 million so'm. Zone C produces 55 percent of all the shortage although it occupies about a fifth of the floor area. The routine in that zone (paperwork, receiving, access by outsiders) deserves a separate look. Inside a zone the same trick can be repeated: by aisle or rack.

The most useful cut is the cause. Go through those ten positions. Suppose that in four, goods that arrived short were recorded in full at receiving, in three similar items were mixed up, in two goods left without documents, and in one no cause was found. Three causes explain nine cases out of ten. The remedies are clear too: count at receiving, store and label similar items separately, and ban dispatch without documents. Do not abandon the position whose cause was not found: mark it so that it is checked first at the next count.

A Pareto table in Excel

1. Two columns: the position and the shortage value (difference × unit cost).
2. Sort by value, largest first.
3. Share = value ÷ total. Cumulative share is the running sum of the shares.
4. Build a chart with columns and a cumulative line (newer versions of Excel also have a ready-made "Pareto" chart type).

Value the shortage at cost, not at selling price: otherwise high-margin goods will rise to the top of the list artificially.

If the "tail" of many small positions is long and flat, that is normal: the real work lies in the first few columns.

What to watch out for

• 80/20 is not a law but a rough picture. Your warehouse may be 70/30 or 90/10, so calculate your own numbers.
• A list by value hides cheap small items that come up short every time. For example, a 12 thousand so'm extension lead is 3-5 pieces short at every count: the sum is small, but the repetition signals a systemic problem (say, goods issued without documents). Make a second ranking: in how many counts a position showed a difference.
• One count is one snapshot. Compare the results of two or three counts: if the same positions are at the top again, the problem is persistent.
• If the shortage is spread evenly and no "head" is visible, the issue is not individual items but the whole process (receiving, record keeping).

Next step

Put the top ten positions and zone C into a monthly cycle count for the next quarter: counting ten positions is half an hour of work. Introduce one rule and one owner per cause. Repeat the analysis in two or three months: is the list shorter, and has the shortage not simply moved elsewhere?

Conclusion

Pareto analysis turns the fight against shortages from checking everything into fixing a few specific points. With its discrepancy analysis and zone map, CountLab shows which positions, zones and causes the shortages concentrate in.