From Six Big Losses to a ranked backlog
As a Quality/CI specialist you already speak the Six Big Losses. This module shows how KaizenFlow maps each loss to a measurable factor and ranks the resulting backlog by dollars and confidence so you stop optimizing the loudest loss instead of the costliest one.
Every loss is a factor, not a feeling
KaizenFlow decomposes OEE into the Six Big Losses and ties each one to logged factors: Availability losses split into Breakdowns and Setup/Adjustment; Performance losses into Minor Stops and Reduced Speed; Quality losses into Startup Rejects and Production Rejects. Each factor is fed by a concrete data source — downtime tiles, reason-code logs, cycle-time deltas versus ideal, and scrap counts by station. The point is that nothing on the backlog is an opinion; every item traces back to a quantity the platform measured.
Ranking by dollars and confidence, not by noise
The AI converts each loss into an annualized dollar figure using the line's throughput value and the measured loss minutes or reject units. It then attaches a confidence score reflecting data density and signal stability. A frequent, well-instrumented minor-stop loss can outrank a dramatic but rare breakdown.
- ›Dollars = loss magnitude × value of recovered capacity, normalized to a comparable annual rate.
- ›Confidence reflects sample size, sensor coverage, and how stable the pattern is over time.
- ›Sort by expected value (dollars × confidence) before committing scarce Kaizen capacity.
Rank the backlog by expected value — dollars times confidence — so you work the costliest verifiable loss, not the most visible one.
Your ranked backlog shows three items. A: a dramatic weekly breakdown, $40k/yr, confidence 0.45 (logged by only one operator, sparse sensor coverage). B: chronic minor stops on the labeler, $52k/yr, confidence 0.85 (dense MTConnect data). C: reduced-speed loss on a downstream cell, $30k/yr, confidence 0.9.
What do you tackle first?
Which pairing of a Big Loss to its primary KaizenFlow factor is correct?