A multi-factor model for evaluating manufacturing defect opportunities in six sigma analysis

Six Sigma Quality Analysis provides a structured method for manufacturing quality problems and defect opportunities to be defined, measured, analyzed, improved, and controlled. The technique is now being widely used in both the manufacturing and service industries to evaluate the classical "defects per million" metric. An underlying assumption of classical Six Sigma Analysis is that all defects contribute equally to the derivation of the defect rate. In the thesis, it is proposed that this assumption skews and often distorts the derived defect rate. Using classical Six Sigma the user is able to list a large number of often border-line defect opportunities and hence inflate their six sigma capability. Here, a new multi-factor model is developed for calculating the Defect Per Million Opportunities (DPMO). The proposed DPMO equation undergoes a rationalized transition from the normal formulation based on the following factors: (i) defect severity, (ii) occurrence frequency, (iii) detection ease, (iv) correction time, and (v) cost impact. This new equation accounts for all possible differentiating characterizations between possible defects. In effect we get a scaled down number of defect opportunities which eliminates the six sigma inflation problem. For each factor, a Six Sigma Opportunity rating scale is presented in the 0-1 range. An MS-Excel implementation of the proposed multi-factor scheme is presented along with a case-study example.

Name thesis:degree_name

Master of Science in Industrial Engineering - (M.S.)

Discipline thesis:degree_discipline

Industrial and Manufacturing Engineering

Year

2005

Author dc:creator

• Sundaram, Karthikeyan

Contributors dc:contributor

• Sanchoy K. Das
• Athanassios K. Bladikas
• George Hanna Abdou

• Six sigma quality analysis
• Manufacturing quality
• Industrial Engineering

Repository record dc:identifier

https://digitalcommons.njit.edu/theses/513

OAI identifier oai:identifier

oai:digitalcommons.njit.edu:theses-1512