Composite Case Study: From Recurring Downtime to Verified Improvement

Context: A mid-sized manufacturing line experienced repeated stoppages on one shift. Operators reported a jam roughly six times per shift. Earlier attempts (extra vigilance, brief retraining) temporarily reduced jams but they returned within days.

Approach: The team used an A3 to capture the problem, collected shift logs and photos (evidence), and ran a fishbone to surface possible causes. They selected three promising lines: worn feeder roller (equipment), inconsistent material feed width (materials), and a nonstandard operator step during start-up (process).

Analysis: For the equipment line, 5 Whys revealed that the feeder roller replacement interval was based on hours rather than measured wear; maintenance had been deferred due to production pressure. Evidence from maintenance logs supported this. The team then proposed a testable countermeasure: install a low-cost wear indicator and adjust the replacement schedule based on measured wear instead of hours.

Verification plan: Using the Verification Plan template, they defined the metric (jams per shift), baseline (6), target (≤1), measurement method (operator tally recorded in daily log), data owner (line supervisor), and test period (2 weeks). They also tracked secondary effects (throughput and scrap) to watch for unintended consequences.

Outcome: After a two-week test, jams dropped to 0–1 per shift and throughput improved slightly. The wear indicator identified rollers that were nearing failure earlier than the hours-based schedule. The team standardized the new replacement rule, updated maintenance work orders, and added the measurement to the daily huddle board. Lessons learned were shared across other lines.

Note: This is an anonymized composite example meant to show the pattern: evidence → focused hypothesis → small test → verification → standardize. Your context will differ; the key is to define measurable verification before you implement.