| Abstract: | Reducing process variability is presently an area of much interest in manufacturing organizations. Programmes such as Six Sigma robustly link the financial performance of the organization to the degree of variability present in the processes and products of the organization. Data, and hence measurement processes, play an important part in driving such programmes and in making key manufacturing decisions. In many organizations, however, little thought is given to the quality of the data generated by such measurement processes. By using potentially flawed data in making fundamental manufacturing decisions, the quality of the decision‐making process is undermined and, potentially, significant costs are incurred. Research in this area is sparse and has concentrated on the technicalities of the methodologies available to assess measurement process capability. Little work has been done on how to operationalize such activities to give maximum benefit. From the perspective of one automotive company, this paper briefly reviews the approaches presently available to assess the quality of data and develops a practical approach, which is based on an existing technical methodology and incorporates simple continuous improvement tools within a framework which facilitates appropriate improvement actions for each process assessed. A case study demonstrates the framework and shows it to be sound, generalizable and highly supportive of continuous improvement goals. Copyright © 2003 John Wiley & Sons, Ltd. |
