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The Consequences of Implementing Statistical Process Control (open access)

The Consequences of Implementing Statistical Process Control

This study evaluated the changes which occur in manufacturing organizations in the plastic molding industry which implement statistical process control (SPC). The study evaluated changes in product quality, consistency, cost, changes in employee attitudes, and changes in the organization structure which occur after the implementation of SPC. The study was conducted in two phases. Phase 1 consisted of an exploratory field study of a single manufacturing company. Phase 2 consisted of a field survey of three manufacturing companies in the same industry. An unexpected opportunity to evaluate the differences in effects of successful and unsuccessful SPC implementations occurred during the field survey. One plant, whose management assessed their SPC program as being unsuccessful, reported no economic or quality benefits from SPC. Neither did this plant report any changes in the attitudes or behavior of their employees. Neither of these findings was surprising since this plant was the only one of the four study plants which implemented SPC as a quality control program with no participation from the production department. The three plants whose management assessed their SPC programs as being successful reported reduced product variation and a decrease in the proportion of defective product produced as a result of SPC. …
Date: August 1990
Creator: Sower, Victor E.
System: The UNT Digital Library
Economic Statistical Design of Inverse Gaussian Distribution Control Charts (open access)

Economic Statistical Design of Inverse Gaussian Distribution Control Charts

Statistical quality control (SQC) is one technique companies are using in the development of a Total Quality Management (TQM) culture. Shewhart control charts, a widely used SQC tool, rely on an underlying normal distribution of the data. Often data are skewed. The inverse Gaussian distribution is a probability distribution that is wellsuited to handling skewed data. This analysis develops models and a set of tools usable by practitioners for the constrained economic statistical design of control charts for inverse Gaussian distribution process centrality and process dispersion. The use of this methodology is illustrated by the design of an x-bar chart and a V chart for an inverse Gaussian distributed process.
Date: August 1990
Creator: Grayson, James M. (James Morris)
System: The UNT Digital Library