On September 4, 2026, Spain's National Statistics Institute (INE) published its Q2 stock survey (ECSE): inventory levels in Spanish wholesale trade rose 5.7% year over year — the biggest jump in nearly three years, since Q3 2023. Retail trade, for comparison, rose only 2%.
Some of that increase is real economic activity: commercial sales grew 3.4% year over year over the same period. But more inventory sitting in the warehouse almost always means more capital tied up — and a common cause of that excess isn't having "too much stock" in general, it's miscalculating the reorder point for specific SKUs within the catalog.
It isn't just a Spanish problem
IHL Group's research on "inventory distortion" — the combined cost of stockouts and excess inventory in retail — puts the global figure at roughly $1.77 trillion a year: about $1.2 trillion in lost sales from stockouts and just over $550 billion in excess inventory that ends up marked down or written off. Those are big-retail numbers, not Spanish SME wholesaler numbers — but the root cause is the same math applied to each SKU, at any scale.
What a "flat average" hides
Most businesses that don't use specialized software calculate their reorder point from an average demand figure and, at best, a fixed safety margin applied equally across the whole catalog. The problem: two SKUs with exactly the same average demand can need very different amounts of safety stock if one sells steadily and the other sells erratically.
The correct formula doesn't ignore that variability — it builds it in:
Safety stock = Z (service level) × standard deviation of demand × √lead time
Reorder point = (average demand × lead time) + safety stock
Illustrative example (sample figures, not real data from any business): two SKUs each sell an average of 10 units/day, with a 7-day lead time and a 95% service level (Z = 1.65). SKU A has stable demand (standard deviation of 2 units); SKU B has erratic demand (standard deviation of 6 units) — say, from occasional promotions or strong seasonality.
- SKU A (stable): safety stock ≈ 8.7 units → reorder point ≈ 78.7 units.
- SKU B (erratic): safety stock ≈ 26.2 units → reorder point ≈ 96.2 units.
Apply the same flat margin to both instead — say, average demand × lead time + 10 units of buffer, a common spreadsheet shortcut — and both SKUs land at an identical 80 units. SKU A ends up with more buffer than it needs (capital tied up for no reason). SKU B ends up short: 16.2 units below what its own variability calls for — exactly the gap where a stockout happens.
Multiply that mismatch across a catalog of hundreds of SKUs and you get exactly what shows up in the INE and IHL Group numbers at the aggregate level: excess stock on predictable products and stockouts on erratic ones, at the same time.
Lead time is the other half of the equation: if your supplier's has stretched out (as we covered in our analysis of importing from China), the safety stock you need goes up further still — regardless of how variable your demand is.
Calculate the real reorder point for your whole catalog
Upload your sales history as a CSV and get the reorder point and safety stock for every SKU at once, using its real variability — not one average for the whole catalog.
Go to the bulk catalog calculator →What to do with this
- Don't apply the same safety margin across the whole catalog: first identify which SKUs have erratic demand (high standard deviation relative to their own average) and treat them differently from stable ones.
- Prioritize recalculating your highest-turnover or highest-unit-cost SKUs first — they tie up the most capital when the margin is miscalculated in either direction.
- Redo this calculation every few months: a SKU's demand variability shifts with seasonality, promotions, or supplier changes — it isn't a fixed number.
Sources
- Spain's Q2 2026 stock survey (ECSE), published September 4, 2026: INE, Encuesta Coyuntural sobre Stocks y Existencias and coverage from Press Digital.
- Global cost of stockouts and excess inventory ($1.77 trillion/year, IHL Group): The Food Institute, "Why Inventory Distortion Costs Retailers Trillions".
The SKU A/B example (average demand, standard deviation, lead time, and resulting units) is illustrative, built from sample numbers to explain the calculation — it isn't real data from any business or industry statistic. Swap in your own sales history using the calculator.