Most stockouts are not caused by unexpected demand. They are caused by ordering at the right stock level for a lead time that no longer holds.
Ask why a line went out of stock and the answer is usually "demand was unexpectedly high".
Look at the data and it is usually the supplier. Demand varies within a fairly predictable band for established FMCG lines. Lead times do not — a supplier who normally delivers in five days takes nine over a festival week, and the reorder point that worked all year fails.
That is why the two inputs need to be treated differently: demand you can average, lead time you have to plan for at its realistic worst.
Reorder point = average daily demand × lead time (days) + safety stock
Stock falls as you sell. When it hits the reorder point, you raise a purchase order. The replenishment arrives before you eat into safety stock — that is the whole design. Safety stock exists to absorb the times when demand runs hot or the delivery runs late.
The Reorder Point Calculator does the arithmetic once you have the three inputs.
Average daily demand. Use your own dispatch data over a period long enough to smooth out weekly patterns — a quarter is usually enough for a stable line. Do not use the month with the scheme in it; that is not your baseline.
Lead time. Not what the supplier says. What actually happened, measured from when you raised the order to when stock was available to sell. Include the time it sits at your own inward desk, because that is time you cannot sell it either.
Safety stock. The judgement call, covered below.
Safety stock is a bet about variability. Two questions size it:
A workable starting point for a fast-moving line: cover the gap between your normal lead time and your realistic worst lead time. If your supplier is normally 5 days and occasionally 9, hold roughly four days of average demand as buffer, then adjust from experience.
Worked example, to re-run with your own numbers. A line selling 120 units a day with a 5-day lead time and 200 units of safety stock:
So when stock hits 800, you have about a week of selling left — enough for a 5-day lead time with two days of slack.
Safety stock is insurance you pay for in working capital. Every unit of buffer is money sitting in the godown.
That makes it a real trade-off rather than a free precaution:
The Inventory Turnover Calculator tells you how many times a year each line turns and how many days of stock you are holding. If a line shows 90 days on hand in a category that should turn monthly, the buffer is not the problem — the ordering quantity is.
And because buffer stock is capital, it belongs in the ROI picture: see how to calculate distributor ROI.
Not everywhere. Reorder points are worth maintaining on:
For the long tail, a periodic review beats a maintained trigger. A reorder point that fires twice a year is administrative overhead pretending to be a system.
Reorder points go stale. Demand shifts seasonally, suppliers change, and a festival period breaks both assumptions at once.
Review the top lines quarterly, and specifically after any period where you stocked out. The useful question after a stockout is not "was demand high" but "was the lead time what we assumed" — that is where the answer usually is.
For the wider operating picture this sits inside, see fixing the four leaks in a beat.
The reorder point tells you when. It says nothing about how much, and ordering the wrong quantity undoes the benefit.
Two forces pull against each other:
The practical approach for most distribution businesses is to order to a target cover — enough to last until the next planned order cycle plus the lead time — rather than computing a theoretical optimum. If you order weekly with a five-day lead time, that is roughly twelve days of cover plus safety stock.
Where price breaks are large enough to tempt a bigger order, put the extra cash into the ROI framing before committing. A 3% price break on stock that sits an extra six weeks is usually a bad trade.
The reorder point assumes demand is roughly stable. For several weeks a year in Indian FMCG, it is not.
Festival demand, school reopening, monsoon shifts — these move demand by enough that a reorder point calculated on annual averages will stock you out during the peak and leave you overstocked after it.
Two adjustments that are simpler than modelling seasonality properly:
Recalculate before known peaks, using the same period last year as your demand input rather than a rolling average.
Watch lead times too, not just demand. Suppliers are also busier before a festival, so the lead time you plan around often stretches at exactly the moment you need it to hold. This is the failure that catches people — they scale up the demand input and leave the lead time unchanged.
For anything with a shelf life, safety stock is not free insurance — it is stock most likely to expire, because it sits longest by design.
Three consequences:
The Inventory Turnover Calculator gives you days on hand per line, which is the number to compare against shelf life.
What distributors ask when setting this up.
Reorder point = average daily demand × lead time in days + safety stock. The first term covers what you will sell while waiting for the delivery; safety stock covers the times demand runs hot or the supplier runs late. Both need to come from your own data rather than from a supplier promise.
Enough to cover the worst realistic combination of a demand spike and a supplier delay — not the worst imaginable. Start with a few days of average demand for fast movers, and size it by how variable your lead time actually is. If your supplier is reliable, safety stock is mostly protecting against demand variation and can be smaller.
No. It is worth the effort on lines where a stockout costs you a sale or where overstocking ties up meaningful cash — usually the top 20% of SKUs by turnover. Slow movers are better managed by looking at them periodically than by maintaining a trigger that fires twice a year.
The FlowKartAI team builds WhatsApp-native ordering for Indian B2B distributors and the kirana stores they serve. We write about distribution economics, GST compliance, and the practical side of putting AI in front of retailers who have never opened an app.
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