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Operational Definition

Defects At a generic level, this refers to the proportion of defects, or instances of non-conformance to quality specifications, in a given context. Measured as a ratio or a percentage (or at times parts per million), the numerator as well as denominator in this ratio have multiple ways of being defined.

  • DPU (Defects Per Unit): Total Defects divided by the total number of units / transactions
  • DU (Defective Units): Number of units with at least one defect, divided by the total number of units
  • DPMO (Defects Per Million Opportunities): Total Defects divided by (Total Units times Opportunities per Unit times 1,000,000)


Ratio / Percentage

Metric type


Related metrics

NPMO (non-conformance per million opportunities) often used synonymously to DPMO

Super Metrics

Sub Metrics

Opportunities per Unit


Causes Solutions
Defects caused by ProcessProcess redesign for defect reduction
Gaps in competencyImprove Line Management Practices, Correct Position Description and Hiring Specifications,Improve on-floor coaching, Improve Training Quality
Gaps in documentation/policiesReview and correct policies, process maps, SoPs, training docs
High variation within segmentsAnalyse segments
Inaccurate/Inadequate measurementSurvey and sampling design, Automation of reporting, Review Ops definitions, Strengthen Measurement system
Inadequate intermediate SLAs, Inadequate End to End SLAs Improve SLAs
Inadequately enforced SLAsImprove governance
Inadequate IT accessImprove IT access
Inadequate Line ManagementImprove Line Management Practices, Workforce Issue Resolution
Inadequate or inappropriate quality control processImprove QC and other controls
Inadequate ownership and accountabilityClarify and enforce accountability, Improve governance
Inadequate Process DefinitionImprove Process Definition
Inadequate Customer CommunicationImprove Training Quality, Improve on-floor coaching, Provide templates or reinforce use of templates
Lack of automatic validations at input stageImplement automated validations at data entry
Lack of motivationImprove remuneration and incentives, Improve Line Management Practices, Improve hiring, Improve Training Quality, Improve Rewards and Recognition, Improve on-floor coaching
Lack of Understanding Customer ExpectationImprove on-floor coaching, Improve Training Quality
Low Mistake ProofingCross Skill / Up Skill, Implement automated validations, Implement manual mistake proofing, Improve on-floor coaching, Improve Training Quality, Refresher Training, UI and Ergonomic improvements for IT, Use Ready Reckoners or Checklists
Low Training QualityImprove Hiring Quality, Improve on-floor coaching, Improve Training Quality, Performance Support during Training, Redesign Training material, re-evaluate training duration/time, Redesign Training Tests, Trainer skill upgrade to improve training effectiveness
Machine / system errors or bugsInvestigate and address IT bugs
Non-Adherence to ProcessReinforce process adherence
Onerous and complex data entry needsSimplify and rationalise data entry requirements and forms
Sub-optimal IT featuresUI and Ergonomic improvements for IT, Implement workflow, Optical character recogntion and other digitisation options, Platform upgrade
Lack of tenure in the teamBenchmark and optimise Salary and other benefits,Correct Position Description and Hiring Specifications,Expectation Setting at the time of hiring,Improve Employee Satisfaction,Improve Hiring,Improve HR Management Practices,Improve Line Management Practices,Improve remuneration and incentives


  • Failure Mode Effect Analysis – FMEA – also called “what could go wrong” reviews
  • Root Cause Analysis – RCA – using techniques like Fishbone Diagrams
  • Cause – Impact Matrix – which plots a many-to-many relationship between multiple causes and multiple impacts / symptoms / effects.
  • Pareto analysis – which plots the top “n” causes of defects in decreasing order of importance (measured usually as the number of defects caused)
  • 5 Whys – an industry practice of asking “why” at least 5 times, so as to get to an actionable root cause rather than try and solve at an interim cause (or just a symptom) and leave the eventual cause unsolved

Cautions / Suggestions

  • Choice of Defects, Defective Units and Defects per Unit:
    • Defective Units are of closest direct relevance to the Customer of this process under review – they expect a defect free unit. Therefore quality as seen by the customer, or wastage of material (with the work item being written off) is best measured with DU data.
    • Defects (or Total Defects) is best used when addressing inefficiency and wastage of labour, because each defect causes waste, regardless of whether they are clustered together in a small number of Defective Units or not.
    • Defects per Unit is a good measure of complexity of design or specifications, because a high number suggests there are “too many ways to get this wrong”.
  • Choice of Discrete vs. Continuous measures:
    • Some specifications, like correctness of names and addresses, have a discrete outcome in terms of accuracy – they are either right or wrong, and “almost right” doesn't help. But others, like being on time, are more complex. In such situations, a basic measure of whether a task was completed on time is a good start, but richer insights can be obtained if one looks at actual time taken.
    • By looking at the actual delay of late cases, one can further segment and conduct special cause analysis and potentially get closer to multiple root causes of delays.
    • By looking at cases that get delivered ahead of time, one can assess if resources could be better used elsewhere, or if more competitive promises on turn around time can in fact be made to the customer.

External References

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defect.txt · Last modified: 2018/09/24 07:30 by insolitus