Supplier Performance Monitoring and Measurement
What this domain tests
Supplier Performance Monitoring and Measurement tests how you define, collect, normalize, and act on supplier metrics. Expect questions on PPM, DPMO, on-time delivery (OTD), OTIF, SCAR responsiveness, scorecards, data integrity, capability indices, acceptance sampling, and business reviews. The exam rewards measures that drive decisions and penalizes metrics that hide severity or compare unlike suppliers.
Core rules and concepts
- Define the metric, formula, data source, period, owner, and target before collecting data.
- PPM = defective units / total units x 1,000,000. DPMO = defects / (units x opportunities) x 1,000,000. They answer different questions: defective units versus defect opportunities.
- OTD = on-time lines / total lines x 100. OTIF adds completeness. State the tolerance window and whether early delivery counts.
- Confirm process stability with a control chart before claiming capability. Cpk uses within-subgroup variation; Ppk uses overall variation. A high Cpk from an unstable process is not valid.
- Acceptance sampling per ANSI/ASQ Z1.4 uses AQL and OC curves; passing a sample does not prove zero defects in the lot.
- Scorecards should weight quality, delivery, cost, and responsiveness, with escalation triggers and a quality business review (QBR) cadence.
- Normalize for volume, complexity, and commodity before ranking suppliers.
Worked example
A supplier ships 120,000 units and 36 are rejected: PPM = 36 / 120,000 x 1,000,000 = 300 PPM, better than the 500 PPM target. OTD is 94% against a 98% target, so the delivery index is 94/98 x 100 = 95.9. SCAR closure on time is 8 of 10 = 80%. Cost index is 90. With weights 40/30/20/10, the score is (100 x 0.40) + (95.9 x 0.30) + (80 x 0.20) + (90 x 0.10) = 40 + 28.8 + 16 + 9 = 93.8. Reasoning: cap quality at 100, use the same period for all metrics, and investigate the SCAR closure gap even though the total looks strong.
Traps that cost marks
- Using PPM alone when a single defect is catastrophic.
- Comparing PPM across different commodities without normalization.
- Calculating Cpk from an unstable process or mixing Cp, Cpk, and Ppk.
- Treating a zero-defect sample as zero lot risk.
- Changing scorecard weights mid-cycle or using inconsistent data periods.