Quality Metrics & Defect Pareto Dashboard: See What to Fix First

The problem this kills

Every quality review, somebody spends a day wrestling spreadsheets. You pull inspection results from one place, the NCR log from another, and then start the manual grind: count how many units passed first time, work out the defect rate, build a Pareto chart by hand to find the worst offenders, and try to figure out whether the problem is one product, one line, or one supplier. By the time the slides are ready, the numbers are already a week old, the "top defect" was computed differently than last month, and nobody can agree on what to fix first.

It's slow, it's inconsistent, and the decisions that come out of it are only as good as the spreadsheet that fed them. You don't need to live like this, and you don't need to be a developer to fix it.

What you'll build

A simple internal web tool. You import two things: your inspection results and your NCR / defect data (product, defect type, quantity inspected, quantity defective, and the source — line or supplier). The tool dedupes records on their inspection/defect id, then computes the metrics that actually drive a quality review: first-pass yield, defect rate as PPM or DPMO, and a Pareto ranking of your worst defects — sliced by defect type, by product, by line, and by supplier. It flags worsening trends versus your last period. Then AI drafts a short, plain-language "what to fix first" summary — the two or three improvement targets that would move the needle most. Your quality manager reviews the metrics and the draft priorities, edits them, and clicks Approve. Only then does the tool publish the dashboard, send a Resend digest to the review distribution list, and let you export the metrics and Pareto data as 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 business — how you define yield and PPM today, exactly what your inspection and NCR columns are named, what counts as a "defect" versus a "non-conformance," whether you measure by unit or by opportunity (DPMO), your products / lines / suppliers and how they're coded, your typical and peak inspection volumes, and your messy edge cases — and then it tailors the data model, the metric formulas, and every later step to your answers. This is not a generic template; the agent reflects a short spec back to you and waits for your thumbs-up before it builds anything. From there it walks the agent through the import, the yield / defect-rate / Pareto calculations, the trend flagging, the AI-drafted priorities, the manager review-and-approve screen, the dashboard publish, the Resend digest, and the metrics CSV export — each step with a ready-to-copy prompt. There's also a fallback so you can build the whole thing today even with no API to your QC system.

The governance it includes (this is the point)

This is data that drives real improvement decisions and gets reported up, so it ships with the controls a quality team needs: login so only your team can use it, row-level security so you only ever see your own organization's data, a complete audit trail of who imported, edited, approved, and published which metrics and when, a hard human-approval gate so no dashboard or digest goes out until the quality manager signs off on the numbers and the priorities, and duplicate guards keyed on the inspection/defect record id so the same record can't be counted twice and quietly distort your yield.

Who it's for

Quality managers and supervisors who report quality performance and have to pick improvement targets — the people who own the monthly quality review and are tired of rebuilding the same fragile Pareto spreadsheet every cycle. If you can describe how your plant defines yield and counts defects, you can build this.

You've got this — start with the plan, paste the first prompt, answer the interview, and you'll see your first Pareto chart take shape the same afternoon.