Performance characterization of Integrated Statistical Process Control systems
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Business competition requires organizations to increase their products’ quality and reduce cost at the same time. Statistical process control (SPC) techniques are important tools for monitoring process performance over time to detect special causes. Automatic process control (APC) systems, on the other hand, are utilized to regulate performance relative to a specified target. The literature indicates that combining APC and SPC systems result in integrated SPC (ISPC) systems offering an effective approach to process improvement. The objective of this research was twofold. The first objective was aimed at identifying the various process factors likely to affect the long-term performance of ISPC systems. The process considered was one of discrete parts manufacturing characterized by the integrated moving average model IMA (1, 1). A simulation model was developed to represent system performance in terms of the mean squared error (MSE) of the resulting output and the average run length (ARL) of the SPC chart utilized. Simulated results were analyzed to identify influential factors likely to affect the system performance. The second objective targeted the development of criteria for the economic performance of ISPC systems. Two mathematical cost models were developed utilizing Taguchi’s quadratic loss function and accounted for key characteristics of the process and system design factors. These two models were used to derive criteria for the economic selection of the SPC chart design parameters. It is hoped that the proposed criteria will help practitioners select appropriate charting alternatives to minimize the total cost of operation.
Thesis (Ph.D.)--Wichita State University, College of Engineering, Dept. of Industrial and Manufacturing Engineering