Reference
Planning stock you
have not sold yet
Inventory planning has a reputation for being hard, and the arithmetic is not. It is one short formula that has been standard practice in operations for decades, and you can do it on paper for a single line in about a minute.
What is hard is the inputs. Every one of them is an estimate someone has to be honest about — and a planning system fed comfortable inputs produces confident numbers that are wrong in exactly the direction the person who supplied them was hoping.
Every commercial relationship, declared
Shopify. StoreBuilder Ltd is a Shopify Partner and earns referral commission when a merchant moves to or upgrades a Shopify plan through the practice.
No planning or forecasting vendor pays us anything. No referral fee, no commission, no rebate, no affiliate link, no sponsored placement. Nothing on this page recommends a product, and the method below is standard operations practice rather than anything of ours.
The reorder point is a moment, not a stock level
The question planning answers is not how much stock to hold. It is when to place the order — and the answer is the amount you will sell while you are waiting for it, plus a cushion for the times either of those estimates is wrong.
The standard formula
Reorder point = (average daily demand × lead time in days) + safety stock
That is the whole of it. When stock on hand plus stock already incoming falls to that number, the order goes. Everything a planning module does beyond this is either working out the three inputs more carefully, or doing it for ten thousand lines instead of one.
A worked example — invented figures, for illustration only
Suppose a line sells 8 a day on average, the supplier takes 21 days, and you hold 60 as a cushion. The reorder point is (8 × 21) + 60 = 228. At 228 units of stock on hand and on order, you buy — not at 60, and not when the shelf looks empty.
Now let the supplier slip to 30 days, which suppliers do. The reorder point is (8 × 30) + 60 = 300, and a business still buying at 228 has quietly become a business that stocks out on its best line every time the supplier is late. Nothing in the storefront reports this; the first symptom is a gap.
Three inputs, and how each one lies
The formula is only ever as good as these, and each fails in a characteristic direction. Knowing which way each one leans is most of the skill.
Average daily demand
Usually taken from sales history, which is a record of what you sold — not what customers wanted. Every day the line was out of stock reads as a day of zero demand, so the average is biased downward by exactly the periods when demand was highest.
The fix is to exclude out-of-stock days from the average rather than count them as zeros, which requires knowing when you were out — a fact most operations do not keep.
Lead time
Almost always the number the supplier quoted, which is their best case. The number you want is your own measured average from order placed to goods available to sell — including the customs step, the delivery slot you missed, and the two days it sat in goods-in before anyone booked it.
The gap between quoted and measured is frequently the single largest error in the whole calculation.
Safety stock
The cushion, and the one people set by feel. It is really a decision about how often you are willing to run out — a higher cushion buys a lower stockout rate and costs cash sitting on a shelf.
Worth setting deliberately per line rather than as one global number: a signature product and a slow accessory do not deserve the same protection, and treating them alike is how cash ends up in the wrong stock.
Four situations where the formula stops working
It assumes demand is roughly steady and history is a guide. Where that does not hold, more arithmetic will not rescue it, and the honest answer is judgement with the numbers as one input rather than the decision.
Seasonality
A trailing average walks into a peak underweight and out of it overweight, every year, because it is always describing the season that just ended. The usable version compares the same weeks last year rather than the last few weeks.
A line with no history
New products have no demand data by definition, so the first buy is a judgement and should be named as one. The useful discipline is deciding in advance what result would change the second order.
Lumpy demand
A line that sells nothing for three weeks and then forty to one trade customer has an average of about two a day and never sells two in a day. The average is arithmetically correct and operationally meaningless.
An unreliable supplier
When lead time swings between 14 and 60 days, no single figure represents it, and safety stock is being asked to absorb a commercial problem. That is a conversation with the supplier, not a bigger cushion.
All of this depends on the incoming figure being real, which is one of the four numbers a stock system has to hold — stock control software covers which system gets nominated to hold them. If the buying record itself is the constraint, what Shopify already covers is the narrower read.
When a spreadsheet is the right answer
For a few dozen lines, the formula in a spreadsheet with honestly measured inputs will out-perform a planning module fed the supplier's quoted lead time and an average that counts stockouts as zeros. The arithmetic is not what software buys you.
What software buys you is doing it for thousands of lines, keeping the inputs current without anyone remembering to, and noticing the ones that moved. That is a real purchase at a real scale — and it is a waste below it.
The cheapest improvement available to most operations is not a system. It is measuring actual lead times for a quarter and replacing every quoted figure with the measured one.
Book the diagnosticWhat this reference does not yet contain
The statistical layer — service-level targets, demand variability, and safety stock derived from those rather than set by judgement — is deliberately not here. It is well-established practice and it is genuinely useful above a certain catalogue size, but it needs the measured inputs above to be real first, and almost nobody arriving at this question has them yet.
Also not here: which planning products do this well at this merchant size, and what each costs to run. We have not implemented across that field, and a comparison assembled from listings would be worth nothing.
Tell us what is breaking
What the systems are doing now, and what you need them to do. We will tell you whether it is a platform problem or something cheaper.