Troubleshooting Analyses of Production Data - Steffey, Duane; Ostarello, Andrew; Clevenger, Jason; Villarraga, Marta (International Journal of Industrial Engineering: Theory, Applications and Practice, 2009)
<!–[if gte mso 9]> <![endif]–><!–[if gte mso 9]> 0 false 18 pt 18 pt 0 0 false false false <![endif]–><!–[if gte mso 9]> <![endif]–> <!– /* Font Definitions */ @font-face font-family:Optima; panose-1:2 0 5 3 6 0 0 2 0 4; mso-font-charset:0; mso-generic-font-family:auto; mso-font-pitch:variable; mso-font-signature:3 0 0 0 1 0; /* Style Definitions */ p.MsoNormal, li.MsoNormal, div.MsoNormal mso-style-parent:""; margin:0in; margin-bottom:.0001pt; mso-pagination:widow-orphan; text-autospace:none; font-size:10.0pt; font-family:"Times New Roman"; mso-fareast-font-family:"Times New Roman"; mso-bidi-font-family:"Times New Roman"; @page Section1 size:8.5in 11.0in; margin:1.0in 1.25in 1.0in 1.25in; mso-header-margin:.5in; mso-footer-margin:.5in; mso-paper-source:0; div.Section1 page:Section1; –> <!–[if gte mso 10]> <![endif]–> <!–StartFragment–> Production data present substantial challenges for statistical analysis, but they may hold information of great value in resolving persistent manufacturing deficiencies. Because these data are not generated in controlled experiments, frequently key factors are confounded or nested, observations are unbalanced across factor levels, and a substantial number of values may be missing. On the other hand, these data represent the most realistic characterization of the manufacturing process on a production scale. This paper aims to describe and illustrate, with a case study adapted from an industrial project, how production data can be analyzed to provide clues to the sources of quality problems. Although such observational studies cannot definitively prove the existence of a cause-and-effect mechanism, results of troubleshooting analyses may suggest potential targets for corrective actions, as well as off-line experiments or further measurements and analyses to confirm the root cause of the manufacturing problem. These investigations may be regarded as part of the analysis step in a six sigma DMAIC methodology. They are undertaken to improve the process mean with respect to specification limits and to control process variation, and their effectiveness can be measured in subsequent capability studies. <!–EndFragment–>