Troubleshooting Analyses of Production Data

Authors

  • Duane Steffey Statistical and Data Sciences Exponent, Inc.
  • Andrew Ostarello Statistical and Data Sciences Exponent, Inc.
  • Jason Clevenger Mechanical Engineering and Materials Science Exponent, Inc.
  • Marta Villarraga Biomechanics Exponent, Inc.

DOI:

https://doi.org/10.23055/ijietap.2009.16.3.266

Keywords:

Hierarchical linear models, nesting, unbalanced data, variance components, regression trees.

Abstract

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.

 

Author Biographies

Duane Steffey, Statistical and Data Sciences Exponent, Inc.

Andrew Ostarello, Statistical and Data Sciences Exponent, Inc.

Andrew Ostarello is the Lead Research Statistician at Scientific Learning Corporation.  Prior to that, he was a Scientist in the Statistical and Data Sciences practice at Exponent, Inc.  Mr. Ostarello is an experienced statistician and database programmer.  He holds an M.S. degree in Statistics from California State University, East Bay.

Jason Clevenger, Mechanical Engineering and Materials Science Exponent, Inc.

Jason O. Clevenger is a Senior Managing Scientist in the Mechanics and Materials practice of Exponent Failure Analysis Associates.  Dr. Clevenger earned his Ph.D. in Physical Chemistry from the Massachusetts Institute of Technology (MIT), and has worked extensively in the field of materials characterization and process engineering for semiconductor, medical device, and pharmaceutical applications.

Marta Villarraga, Biomechanics Exponent, Inc.

Marta Villarraga is a Principal in Exponent’s Biomechanics practice in Philadelphia, PA.  Dr. Villarraga has a Doctoral Degree in Biomedical Engineering from Tulane University. She specializes in spine biomechanics and in failure analysis of medical devices.  She has experience with orthopedic, spinal, reconstructive surgery, and diagnostic medical devices from product liability, intellectual property, regulatory compliance, and product development perspectives.  Dr. Villarraga also has experience in evaluating quality control issues as applied to medical devices and pharmaceuticals, with an emphasis in contamination, manufacturing compliance, and finished device evaluations.

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How to Cite

Steffey, D., Ostarello, A., Clevenger, J., & Villarraga, M. (2009). Troubleshooting Analyses of Production Data. International Journal of Industrial Engineering: Theory, Applications and Practice, 16(3), 206–213. https://doi.org/10.23055/ijietap.2009.16.3.266

Issue

Vol. 16 No. 3 (2009): Special Issue: Six Sigma - Its Application, Practice and Utility

Section

Production Planning and Control

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