Every component inspected this quarter: which station, shift and previous operator the rejects came from, and the cost.
Filters persist across every tab. Click any bar, dot or row to drill down. Rate rankings are gated to ≥30 inspections. Chips show active filters — click to remove.
No rows match these filters.Nothing is being calculated, because there is nothing to calculate from — a rate over
zero rows is not zero, it is undefined, and showing you a number here would be a wrong one.
Components inspected
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Reject rate
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First-pass yield
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accepted first time
Points out of control
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weeks beyond ±3σ
Total rejects
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carrying a code
Reject rate by week — p-chart with control limits
Weekly reject proportion vs the centre-line and ±3σ limits (limits widen when a week inspects fewer components). Points outside the band are special-cause, not noise. Control limits recompute on your current filter; n small ⇒ wide limits.
Weekly throughput — accepted vs rejected
Components inspected per week, accept / reject split
Top cause
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Top code
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Vital few
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codes driving 80% of rejects
Active codes
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distinct rejection codes
Rejection code Pareto
Each code's share of rejects (bars) + cumulative % (line) · dashed = 80% line · the vital few on the left
Cause mix
Share of rejects by root-cause family · click a slice to drill
Plant first-pass yield
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accepted first time
Worst station
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Best station
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Escapes at final
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rejects at final inspection
First-pass yield by station
Yield = 1 − rejects ÷ inspected, per station in process order · orange bar = worst · each component is inspected at one station
Reject rate by department
Where rejects concentrate across the process · click to drill
Worst previous operator
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Operators above average
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worse than the mean
Plant reject rate
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baseline
Incoming components
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from previous operators
Reject rate by previous operator
Worst 15 previous operators by reject %, min. 30 components · redder = worse
Operator volume vs reject rate
Each dot a previous operator · x = volume · y = reject % · top-right = high-volume & high-reject = act first
Total scrap cost
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cost of poor quality
Scrap / inspection
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across all components
Costliest cause
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Scrap vs rework
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deep-repair vs surface
Scrap cost Pareto — by cause
Total £ scrap by cause (bars) + cumulative % · this re-ranks vs the count Pareto — the most frequent defect is not the most expensive
Scrap cost by station
Where the money is lost across the process
Concentration (Gini)
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across cause × department
Open CAPAs
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corrective actions
CAPA closure
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rejects with closed action
Fix first
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highest frequency × cost
Defect concentration — cause × department
Reject count per cause × department · darker = more · is it one cell or spread everywhere?
Corrective-action priority
Each dot a code · x = reject frequency · y = scrap £ · top-right = fix first · size = open CAPAs
Worst shift
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Shift spread
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best → worst (pp)
Worst inspection level
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Inspector spread
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find-rate range (pp)
Reject rate by shift
A / B / C · night shift typically finds — or makes — more rejects
Reject rate by inspection level
Higher inspection levels carry more risk · dashed = plant average
Reject rate by inspector
Find-rate per inspector vs the plant average (dashed) · variation in inspection, not necessarily fault · min. 30 inspections
Defect records
Aggregated on current filters · click a header to sort · click a row to filter