Stock Inventory Management in Excel, and Where AI Helps

A stock inventory sheet in Excel only needs to answer two questions reliably: how much of each item is on hand right now, and which items need reordering before they run out. Everything else — colour coding, dashboards, charts — is decoration on top of those two numbers. The reorder question is also the one an AI tool can genuinely help with, and the one place a wrong answer is the most expensive: a missed reorder is a stockout, and a false one is money tied up in stock you didn't need yet.

The two columns that do the actual work

A working tracker needs, at minimum: on-hand quantity (updated when stock moves), average daily usage (a rolling average over a recent period, not a guess), and lead time in days (how long a reorder actually takes to arrive from that specific supplier). From those three, a reorder point is one formula: Reorder Point = (Average Daily Usage × Lead Time) + Safety Stock, where safety stock is a buffer you set for how much uncertainty in demand or delivery you're willing to absorb.

A low-stock flag is then a single IF formula comparing on-hand quantity against that reorder point: =IF(OnHand<=ReorderPoint,"Reorder now","OK"). Conditional formatting on the same column turns that into a colour a warehouse team can scan in seconds without reading a single cell value. Quick Analysis in Excel covers the one-click route to that kind of colour scale if you have not set one up before.

A worked example

Say a SKU sells an average of 12 units a day, the supplier takes 5 days to deliver, and you want a 2-day safety buffer:

• —Reorder Point = (12 × 5) + (12 × 2) = 60 + 24 = 84 units.
• —If on-hand quantity is 90, the flag reads "OK" — you have six units of headroom above the reorder point.
• —If on-hand quantity drops to 80, the flag switches to "Reorder now" — four units below the point where a fresh order should already be placed, given the 5-day lead time.

The number that actually needs the most care here is average daily usage. A simple all-time average hides a seasonal spike or a recent slowdown; a rolling 30-day average, recalculated as new sales data comes in, tracks real demand far better than a number set once and forgotten. An Excel user defined function is worth building once you're maintaining this rolling average across dozens of SKUs, so the same logic doesn't get copy-pasted and drift out of sync across rows.

Where AI genuinely helps

Asking an AI tool to draft the IF formula, explain what a reorder point means to someone new to the sheet, or scan a description of your columns and suggest which ones are missing is a good use of it — that is a language and structuring task, and a specific request gets a specific answer. How to write a prompt that works on the first try covers the general habit: describe your actual columns and one worked row, rather than asking it to "build me an inventory tracker" from nothing. Both OpenAI and Anthropic recommend exactly that in their own prompting guidance — a concrete example beats an abstract instruction.

If your inventory sheet started life as a file attached to a chat window rather than opened in Excel directly, can you upload Excel to ChatGPT covers what that upload actually does and does not do with the numbers inside it.

Where it goes wrong

Asking the same tool to compute the reorder point itself — typing in the three numbers and asking for the answer in a chat window, rather than through the actual formula — is the wrong use of it. A model predicts plausible-looking text, and a plausible-looking reorder number is not the same as a correctly calculated one; this is a documented property of how these models generate output, not a rare glitch. A stockout caused by a wrong reorder point is expensive precisely because it looks fine in the chat window right up until the shelf is empty.

There's a related failure specific to inventory decisions: if you tell the model what answer you're expecting — "this SKU should still have plenty of stock, right?" — research on how these models respond to a stated expectation has found a tendency to agree with what the user implies rather than checking independently. Ask what the reorder point is, not whether a number you already believe sounds right.

Let the formula compute the reorder point. Let AI help you describe the sheet and explain the result. Keep those two jobs apart.

Checks before you trust the sheet

1. 01Recompute one reorder point by hand against the formula's output before trusting the column across every row — checking an AI answer when you are not the expert applies just as much to a formula an AI tool suggested as to a number it stated directly.
2. 02Check what your average daily usage is actually averaging over — a 90-day average during a slow season understates a busy one, and the reverse understates a quiet one.
3. 03If lead time varies by supplier, confirm each SKU is pulling the right supplier's lead time rather than one default value copied down the whole column.
4. 04For deciding how much safety stock to hold across many SKUs at once, given a limited storage or cash budget, that is an optimisation problem — Solver's job, not a single formula's.

What to do Monday

Pick one SKU you already track loosely and build the three-column version — on-hand, average daily usage, lead time — with the reorder-point formula and a conditional-format flag. Watch it for a week against what you would have ordered by memory. If the sheet needs to pull data automatically from a point-of-sale or supplier system rather than being updated by hand, data automation tools covers what that connection actually involves. Decide up front what you'd check to notice the formula silently broke — the same monitoring principle behind NIST's AI Risk Management Framework, scaled down to one column instead of a whole system. Coursium teaches this kind of practical judgement — asking a tool the right question and checking what it gives back before you act on it. Stay ahead of AI by learning the tools on your phone.