Applied assessment — finding and confirming root cause

4 of 5 needed to pass

1. 1. A cause-and-effect diagram for skipped stitches lists 15 possible causes across machine, method, material, and manpower branches. The team has limited time before the tollgate. What should happen next?

   Choose one answer.

   Pick the cause the supervisor personally believes is most likely and implement a fixUse a cause-and-effect (X-Y) matrix or quick data checks to prioritize which of the 15 causes have the strongest, most testable link to the defect before investing further analysisDiscard the diagram since it has too many branches to be usefulFix all 15 causes simultaneously to save time
2. 2. Line A (n=40) has a mean output of 620 pieces/day, Line B (n=38) has a mean of 598 pieces/day. A two-sample t-test comparing the lines gives a p-value of 0.24. What should the team conclude?

   Choose one answer.

   The test is invalid because the sample sizes are not identicalA p-value of 0.24 confirms the two lines are exactly equal in performanceLine A is definitely faster because its average is higherAt typical significance thresholds (e.g., 0.05), there is not enough evidence to conclude a real difference between the lines; the observed gap could plausibly be due to normal variation
3. 3. An FMEA rates a cutting-error failure mode with Severity 8, Occurrence 6, Detection 7. Another failure mode (label mismatch) rates Severity 9, Occurrence 2, Detection 2. Using RPN = S x O x D, which failure mode should get priority, and why?

   Choose one answer.

   Neither needs action since detection scores are providedCutting error, because RPN 336 is higher than label mismatch's RPN 36, meaning it is currently a bigger combined riskLabel mismatch, because its severity of 9 is the highest single number in the tableBoth are equal priority since both involve garment defects
4. 4. A regression of sewing defect rate (Y) against machine age in years (X) gives R-squared = 0.09 with a non-significant p-value for the slope. The supervisor insists machine age is 'obviously' the main driver of defects. What is the correct interpretation?

   Choose one answer.

   R-squared of 0.09 with a non-significant slope means machine age explains very little of the variation in defect rate in this data, so it is not supported as the main driver hereR-squared of 0.09 is close enough to 1 to confirm a strong relationshipThe regression is irrelevant because supervisor experience always outweighs statistical evidenceA non-significant p-value means machine age causes zero defects under any circumstances
5. 5. Five Whys on a recurring zipper defect ends at 'because the operator was rushing.' The team wants to close the analysis here. What is the risk of stopping at this point?

   Choose one answer.

   'Rushing' is often a symptom of a deeper systemic cause (e.g., unrealistic line target, upstream starvation, poor fixture) and stopping here risks a fix that blames the operator instead of the processThe team should have stopped after only two Whys to save timeFive Whys is not a valid tool for zipper defects specificallyThere is no risk; 'operator was rushing' is always an acceptable final root cause