Manufacturing Scrap Rate Root Cause Analysis Builder

You are a manufacturing data analyst helping operations reduce scrap and improve yield. Build a scrap rate root cause analysis plan for the production context below.

Production process: [line, product family, plant, shift, machine, batch process]
Scrap problem: [scrap rate, defect type, recent spike, chronic issue, target threshold]
Data fields available: [date, shift, product, SKU, batch, machine, operator, material lot, supplier, defect code, quantity, cost]
Time period: [date range]
Production volume: [units produced, good units, scrap units, rework units]
Cost data: [material cost, labor, disposal, rework, downtime, lost capacity]
Process context: [changeovers, new supplier, equipment maintenance, recipe change, training, weather, demand surge]
Quality system: [MES, ERP, QMS, spreadsheets, manual logs]
Known data quality issues: [missing defect codes, inconsistent units, duplicate logs, delayed entry]
Decision needed: [containment, corrective action, supplier review, maintenance, training, process change]
Audience: [plant manager, quality, finance, production supervisors, executives]

Create:
1. Data validation checklist before analysis.
2. Scrap KPI definitions for rate, cost, yield, rework, and avoidable scrap.
3. Pareto analysis plan by defect, product, line, machine, shift, lot, and supplier.
4. Trend and control-chart approach to separate common cause from special cause variation.
5. Root-cause hypothesis tree connecting process, material, machine, method, people, and environment.
6. SQL or pseudocode for the core cuts if tables are available.
7. Visualization plan for an operations dashboard.
8. Corrective action tracker with owner, due date, evidence, and expected impact.
9. Financial impact model for recovered yield and reduced scrap cost.
10. Executive summary template with findings, confidence, and next experiments.

Do not blame operators based on correlation alone. Call out where process observation or engineering review is required.