Negative/Zero On-Hand Exception Hunter: Catch the Stock-Data Gremlins

The problem this kills

A SKU shows -7 on hand. Another reads 0 but it sold three times yesterday. A third has 40 units sitting in the system with no bin location attached. None of these is physically possible — they're data-integrity gremlins — and every one quietly breaks something downstream. Pickers get sent to empty shelves. Forecasts reorder the wrong amounts. Replenishment skips items that are actually out. Customer promises get made against quantities that don't exist.

The maddening part is that these errors are sitting right there in your on-hand snapshot, completely catchable, the moment you know what pattern to look for. But nobody has time to eyeball tens of thousands of rows every morning, so the gremlins accumulate until a cycle count or an angry customer surfaces them weeks later. You don't need to be a developer to build something that runs that scan for you every single day — and queues the worst offenders for a count before they cause damage.

What you'll build

A simple internal web tool for your inventory-control team. You import two files: your on-hand snapshot by SKU and location, and your recent transactions (sales or movements). The tool scans every record against a set of rules you define and flags the exceptions: negative on-hand, zero-on-hand-but-recently-sold, on-hand-with-no-location, and any other pattern you care about. It categorizes each issue, prioritizes it by item velocity and value so you fix the costly ones first, and shows a plain-English reason for every flag. Your inventory-control lead reviews each exception and chooses the corrective action — create a count task, post a correction, or refer to write-off — and only then does anything get committed. The tool exports a prioritized exception list, a set of count tasks, and an action log of every decision.

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 business — your stock-accuracy process, the WMS or ERP and spreadsheets you use, the exact columns and SKU/location naming in your data, your typical and peak row counts, and your real rules for what counts as an exception and how to prioritize it. It reads a short spec back to you for a thumbs-up, then builds the tool around your answers instead of a generic template. From there it walks the agent through the data model, the CSV imports, the rule-based exception scanner, the velocity/value prioritization, the review-and-approve screen, the human gate, and the exception, count-task, and action-log exports. Every step ends with a ready-to-copy prompt.

The governance it includes (this is the point)

This isn't a toy. The plan builds in the controls a real warehouse operation needs: login so only your team can use it, row-level security so people only see their own organization's stock data, a complete audit trail of every approval and override (who, what, when, and why), a hard human-approval gate so nothing is written back to your system of record until a lead decides, and duplicate guards so the same snapshot can't be scanned twice for the same day. The whole tool exists to make a careful human decision easy — the scan raises the suspicion, a person makes the call.

Who it's for

Inventory-control analysts, data stewards, and warehouse managers who own stock accuracy and are tired of finding impossible quantities only after they've broken picking or forecasting. If you can describe what makes a stock record "wrong" in your world, you can build this.

You've got this — open the plan, paste the first prompt, and you'll be hunting your first batch of stock-data gremlins this afternoon.