Defects per million opportunities

In process improvement efforts, defects per million opportunities or DPMO (or nonconformities per million opportunities (NPMO)) is a measure of process performance. It is defined as

${\displaystyle DPMO={\frac {1,000,000\times {\mbox{number of defects}}}{{\mbox{number of units}}\times {\mbox{number of Defects opportunities per unit}}}}}$

A defect can be defined as a nonconformance of a quality characteristic (e.g. strength, width, response time) to its specification. DPMO is stated in opportunities per million units for convenience: Processes that are considered highly capable (e.g., processes of Six Sigma quality) are those that experience fewer than 3.4 defects per million opportunities (or services provided).

Note that DPMO differs from reporting defective parts per million (PPM) in that it comprehends the possibility that a unit under inspection may be found to have multiple defects of the same type or may have multiple types of defects. Identifying specific opportunities for defects (and therefore how to count and categorize defects) is an art^{[citation needed]}, but generally organizations consider the following when defining the number of opportunities per unit:

• Knowledge of the process under study
• Industry standards
• When studying multiple types of defects, knowledge of the relative importance of each defect type in determining customer satisfaction
• The time, effort, and cost to count and categorize defects in process output

Other measures

Other measures of process performance include:

• Process capability indices such as C_pk^[1]
• Natural tolerance limit or sigma level
• PPM defective or defective parts per million
• Process performance indices such as P_pk
• Quality costs or cost of poor quality (COPQ)

References

1. "What is Process Capability?". NIST/Sematech Engineering Statistics Handbook. National Institute of Standards and Technology. Retrieved 2008-06-22.

Further reading

• Adams, Cary W.; Gupta, Praveen; Wilson, Charles E. (2003). Six Sigma Deployment. Burlington, MA: Butterworth-Heinemann. ISBN 0-7506-7523-3. OCLC 50693105.
• Breyfogle, Forrest W. III (1999). Implementing Six Sigma: Smarter Solutions Using Statistical Methods. New York, NY: John Wiley & Sons. ISBN 0-471-26572-1. OCLC 50606471.
• De Feo, Joseph A.; Barnard, William (2005). JURAN Institute's Six Sigma Breakthrough and Beyond – Quality Performance Breakthrough Methods. New York, NY: McGraw-Hill Professional. ISBN 0-07-142227-7. OCLC 52937531.
• Hahn, G. J., Hill, W. J., Hoerl, R. W. and Zinkgraf, S. A. (1999) The Impact of Six Sigma Improvement-A Glimpse into the Future of Statistics, The American Statistician, Vol. 53, No. 3, pp. 208–215.
• Keller, Paul A. (2001). Six Sigma Deployment: A Guide for Implementing Six Sigma in Your Organization. Tucson, AZ: Quality Publishing. ISBN 0-930011-84-8. OCLC 47942384.
• Pande, Peter S.; Neuman, Robert P.; Cavanagh, Roland R. (2001). The Six Sigma Way: How GE, Motorola, and Other Top Companies are Honing Their Performance. New York, NY: McGraw-Hill Professional. ISBN 0-07-135806-4. OCLC 647006794. Pande Six Sigma Way.
• Pyzdek, Thomas & Paul A. Keller (2009). The Six Sigma Handbook, Third Edition. New York, NY: McGraw-Hill. ISBN 978-0-07-162338-4. OCLC 51194565.
• Snee, Ronald D.; Hoerl, Roger W. (2002). Leading Six Sigma: A Step-by-Step Guide Based on Experience with GE and Other Six Sigma Companies. Upper Saddle River, NJ: FT Press. ISBN 0-13-008457-3. OCLC 51048423.
• Taylor, Gerald (2008). Lean Six Sigma Service Excellence: A Guide to Green Belt Certification and Bottom Line Improvement. New York, NY: J. Ross Publishing. ISBN 978-1-60427-006-8. OCLC 271773742.
• Tennant, Geoff (2001). SIX SIGMA: SPC and TQM in Manufacturing and Services. Aldershot, UK: Gower Publishing, Ltd. ISBN 0-566-08374-4. OCLC 44391556.