Stock Accuracy (IRA) KPI Tracker: Trust Your Inventory Numbers Again

The problem this kills

Everyone in the warehouse says the inventory is "pretty accurate," but nobody can put a defensible number on it — and the moment a stockout, a short pick, or a write-off lands on leadership's desk, "pretty accurate" stops being good enough. Inventory Record Accuracy (IRA) is the standard answer: of the locations you counted, what percentage matched the system within tolerance? It's the one number that tells you whether your stock records can be trusted to plan, promise, and ship.

The trouble is that the number usually lives in a spreadsheet someone rebuilds by hand. Counts get pasted from a scanner export, tolerances are applied inconsistently (sometimes units, sometimes dollars, sometimes "eh, close enough"), the same count event gets entered twice, and the trend chart is whatever last person had time to drag a formula across. When a zone quietly starts slipping, nobody sees it until the misses pile up. And because the headline number is hand-built, the first instinct of anyone who doesn't like it is to argue with the math instead of fixing the process.

IRA is too important to be a fragile workbook. It deserves to be a real, governed tool — one number, computed the same way every period, that leadership can actually trust.

What you'll build

A simple internal web app for your inventory control team. You import your count history — each count event with the item, location/zone, the system quantity, the counted quantity, the unit cost, the ABC class, and who counted it. You set your tolerance rules: a unit tolerance (e.g. within ±2 units or ±2%) and/or a dollar tolerance (e.g. within ±$25). The tool decides, for every count, whether it landed within tolerance — a hit — or not.

From there it computes Inventory Record Accuracy as the within-tolerance hit rate, and slices it the ways that actually drive action: by zone, by ABC class, and by counter. It trends each segment over time so you can see whether accuracy is climbing or sliding, and it flags declining zones before they become a problem. At the end of each period, a manager reviews the snapshot — and any tolerance changes — and approves it. Only then does the tool email a weekly accuracy summary to leadership and produce a clean CSV.

What's inside the Implementation Plan

The downloadable plan is a step-by-step file you paste into an AI coding agent. It opens by interviewing you about your warehouse — how you cycle count today, what your count export actually looks like (real column names and all), how you define a "count event," whether you measure tolerance in units or dollars (or both), how you assign ABC classes and zones, and the messy exceptions (recounts, zero-on-hand locations, multi-location SKUs). It reads a short tailored spec back to you and waits for your thumbs-up before building anything — so the tool computes IRA your way, not a generic template's way.

From there it walks the agent through the data model, the count-history import with duplicate guards, the within-tolerance engine (unit and dollar methods), the IRA-by-segment calculations, the trend charts, the declining-zone flags, the manager approval gate, and the Resend weekly summary. Every step ends with a ready-to-copy prompt. There's a full "No API yet?" path that uses a Google Sheet / CSV as the data source and produces a clean CSV export — so you can build and run the whole thing this afternoon, with no integration to your WMS or ERP required.

The governance it includes (this is the point)

A KPI that leadership acts on needs to be trustworthy, so the controls aren't optional. The plan builds in login so only your team can use it, row-level security so you only ever see your own organization's count data, a complete audit trail of who imported what, who changed which tolerance, and who approved which snapshot, a hard human-approval gate so no accuracy number reaches leadership until a manager signs off on the period's snapshot, and duplicate guards so the same count event can't be scored twice and quietly skew the number.

Who it's for

Inventory control managers, operations leadership, and continuous-improvement teams — anyone who is asked "how accurate is our inventory?" and wants a defensible answer, computed the same way every period, that points straight at the zones, classes, and counters that need attention. If you can export your count history and tell us how you define "close enough," you can build this.

You've got this — start with the plan, paste the first prompt, and answer the interview. You'll have your first IRA dashboard on screen before the afternoon's out.