Authors: Paul L. Goethals; Byung Rae Cho
Addresses: Department of Industrial Engineering, Clemson University, Clemson, SC 29634, USA ' Department of Industrial Engineering, Clemson University, Clemson, SC 29634, USA
Abstract: In a manufacturing environment where concerns for product quality or excessive cost are often present, process optimisation is typically an engineering objective. One method that supports the reduction of nonconformance costs and loss in product quality is the identification of the optimal process mean. Given the specification limits and associated costs for a process, the traditional method uses assumed values for the process parameters to estimate the ideal location of the mean. In contrast, this paper proposes using a methodology that removes the need to make assumptions on the process parameters. Furthermore, while most research examines the role of the nominal-the-best characteristic in the design of the optimal process mean, this paper specifically looks at smaller-the-better and larger-the-better quality characteristics. The skew normal distribution, which is relatively new to engineering applications, is considered in modelling these characteristics. A non-linear programming routine with economic considerations is used to facilitate this study.
Keywords: optimal process mean; response surface methodology; RSM; skew normal distribution; manufacturing industry; process optimisation; nonlinear programming; economic considerations; asymmetrically distributed processesd.
International Journal of Productivity and Quality Management, 2012 Vol.9 No.1, pp.82 - 102
Published online: 31 Oct 2014 *Full-text access for editors Access for subscribers Purchase this article Comment on this article