The number at the bottom of the stocktake
The stocktake finishes and the variance is bigger than last year. Stock is missing, but nobody can say where it went. Theft, damage, mis-picks, receiving errors, unrecorded write-offs, a supplier short-delivering: any of them could explain it, and after a year the trail has gone cold. The loss gets written off and everyone hopes next year is better.
In between stocktakes, the system shows the stock as present. Orders are promised against it. Then a picker finds an empty location, a customer is let down, and someone does a quick adjustment to make the numbers match.
Why shrinkage stays hidden
Stock systems record movements but rarely question them. An adjustment, a write-off, a return to stock or a receipt short of the delivery note all go through without anyone asking whether it looks normal. Across thousands of movements a week, a small, steady loss on certain lines or at certain sites is invisible.
Cycle counts help, but they are usually scheduled by location or rota rather than risk. The counters check shelves that are fine and miss the ones where the problem is. And when a count does find a gap, it is corrected rather than investigated.
The adjustment itself then hides the evidence. Once someone has corrected the system to match the shelf, the loss looks like a routine stock correction among hundreds of others. Unless something is looking across all those corrections for patterns, such as the same product, the same site or the same week of each month, the cause never surfaces.
What late discovery costs
| Hidden loss | What follows |
|---|---|
| Missing stock | Write-offs that cannot be traced to a cause |
| Phantom stock in the system | Orders promised against items that are not there |
| Process errors repeated | The same receiving or picking mistake happens all year |
| Internal theft undetected | Losses grow and trust erodes when finally discovered |
| Blanket controls | Everyone is treated as suspect because the real source is unknown |
Most shrinkage is process error rather than theft, and process errors are fixable, but only if you know which process, which site and which products.
How we flag unusual stock losses early
- We pull stock movement history from your stock system, WMS or ERP: receipts, picks, transfers, adjustments, write-offs, returns and counts, plus till data for retail sites.
- We build a picture of normal behaviour for each product, site and process, such as how often a line is adjusted, typical variance at a count, and normal return rates.
- We run anomaly detection that flags patterns out of line with that normal: repeated negative adjustments on one line, variances concentrated on certain shifts, receipts often short from one supplier, or high-value items with unusual void or refund patterns.
- Each flag comes with the evidence, so an operations manager can see why it was raised and decide whether to investigate.
- We point cycle counts at the flagged locations and products, so counting effort goes where the risk is.
- Investigation outcomes are recorded and fed back, so the system learns which flags matter and which are normal quirks of your operation.
We design the flags around processes, products and sites first. Where people are involved, flags are reviewed by managers with context, and treated as a reason to look, not as proof of anything.
What operations sees
A short weekly list of unusual loss patterns, each with the movements behind it. Cycle counts that go to the places most likely to have a problem. Causes found while they can still be fixed: a receiving check that is being skipped, a supplier who is routinely short, a product that gets damaged in one part of the warehouse.
The stocktake still happens, but it becomes a confirmation rather than a surprise.
And because causes are found, controls can be targeted. Instead of tightening rules for everyone after a bad stocktake, you fix the specific process or supplier that was losing stock, which staff find far fairer.
Is this your situation?
- Stock losses are mainly discovered at stocktake.
- Variances are written off without a known cause.
- The system shows stock that is not physically there.
- Cycle counts follow a rota rather than where problems are likely.
- You have several sites or a large number of product lines.