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Predicting the output of a tube-bending process: A case study [An article from: International Journal of Production Economics]
Description
This digital document is a journal article from International Journal of Production Economics, published by Elsevier in 2005. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.
Description:
This paper uses the methodology of an intervention case study to examine a manufacturing process for bending metal tubes into oven flues. We found that the manufacturer, Stanley Engineered Components (SEC), was producing parts that were not acceptable to its major customer yet its processes were in control. Our analysis of the situation led us to theorize that: (1) manufacturers do not distinguish between out of control and out of specification situations, (2) once this distinction is made then process capability indices should be part of the decision making about the quality of the process, (3) various univariate and multivariate models can be fitted to sample data, and (4) the choice of the best model fit should be based upon the smallest error term and this error term should be chosen to directly relate to the managerial decisions that must be made. In the case of SEC where the decision was to determine if the process drifted from a specified target then the appropriate error measure is the standard deviation.
Description:
This paper uses the methodology of an intervention case study to examine a manufacturing process for bending metal tubes into oven flues. We found that the manufacturer, Stanley Engineered Components (SEC), was producing parts that were not acceptable to its major customer yet its processes were in control. Our analysis of the situation led us to theorize that: (1) manufacturers do not distinguish between out of control and out of specification situations, (2) once this distinction is made then process capability indices should be part of the decision making about the quality of the process, (3) various univariate and multivariate models can be fitted to sample data, and (4) the choice of the best model fit should be based upon the smallest error term and this error term should be chosen to directly relate to the managerial decisions that must be made. In the case of SEC where the decision was to determine if the process drifted from a specified target then the appropriate error measure is the standard deviation.
