Setting Reorder Points That Actually Work

Ask most warehouse managers how a SKU's reorder point was set, and the honest answer is usually "it felt about right" — a round number picked once, years ago, never revisited. That works fine until demand shifts or a vendor's lead time changes, at which point it quietly stops working and nobody notices until a stockout blocks an order.

Here's a method that takes ten minutes per SKU and actually accounts for the two things that matter: how long it takes to get more stock, and how much demand varies while you're waiting.

The formula, in plain terms

reorder_point = (average_daily_demand × lead_time_days) + safety_stock

Two components, each doing a different job:

• average_daily_demand × lead_time_days — how much stock you'll sell while waiting for a new order to arrive, assuming demand is perfectly steady.
• safety_stock — a buffer for when demand isn't steady, or the vendor's lead time runs longer than usual.

Most reorder points that fail are missing the safety stock term entirely — they're calculated for the average case and have zero margin for a bad week.

Calculating safety stock without overcomplicating it

You don't need a statistics degree for this. A workable approximation:

safety_stock = (max_daily_demand - average_daily_demand) × lead_time_days

This asks: "in the worst realistic week, how much more than average would I sell during the lead time window?" That's your buffer. It's not statistically perfect, but it's dramatically better than no buffer at all, and it's simple enough that a warehouse manager can actually maintain it without a dedicated analyst.

Plugging in real numbers

Illustrative SKU and figures, not real inventory data — worked through so the formula isn't just abstract.

Say a fictional distributor sells a SKU — call it Cat-6 Cable, 305m box — with these observed figures:

Working through both formulas:

safety_stock    = (15 − 8) × 6  = 42 boxes
reorder_point   = (8 × 6) + 42  = 90 boxes

So the reorder trigger sits at 90 boxes — not a round number someone picked, but 48 boxes covering expected demand during the 6-day lead time, plus a 42-box buffer for a demand spike while the order is in transit. If this SKU's old reorder point was "50, felt about right," that gap is exactly where a stockout during a busy week comes from.

Getting lead time right is more important than getting demand right

Businesses tend to spend all their analytical effort on demand forecasting and treat vendor lead time as a fixed constant. In practice, lead time is usually the more volatile of the two — a vendor that normally ships in 5 days can take 12 during a supply crunch, and that swing affects your reorder point far more than a modest demand forecasting error does.

If you're tracking one thing manually, track actual vendor lead time per order, not just the quoted lead time. The gap between quoted and actual is where most stockouts originate.

Why per-warehouse matters, not just per-SKU

If you run multiple warehouses, a single reorder point per SKU across all locations will systematically fail somewhere — one warehouse's demand pattern doesn't represent another's. A SKU that moves fast in one region and slowly in another needs its own reorder point per location, not a blended average that's wrong for both. This is one of the most common gaps we see in multi-warehouse operations: stock accuracy problems that look like a tracking issue are often actually a reorder-point-granularity issue.

A minimal review cadence that actually gets followed

Reorder points that are "set once and forgotten" drift out of accuracy within a couple of quarters as demand and lead times shift. A sustainable cadence:

• Review your top 20% of SKUs by revenue quarterly.
• Review everything else twice a year, or whenever a vendor relationship changes materially.
• Recalculate immediately after any stockout — a stockout is direct evidence the current reorder point was wrong.

None of this requires sophisticated software to start — a spreadsheet with these two formulas is a real improvement over "felt about right." Where a platform like DexVee's Inventory module adds value is triggering the reorder automatically once the threshold is hit, and keeping lead-time and demand data current without a manual recalculation exercise every quarter.