Abstract
Total Quality Management (TQM) is a widely known and applied concept by organizations for continuous improvement in the workplace. This philosophy is based on eight main principles: customer focus, leadership, involvement of employee, system approach, continuous improvement, process approach, and facts based decision making, and mutually beneficial supplier relationship. TQM includes statistical analysis of data, implementation of corrective and preventive actions, measurement of performance indicators of process and also advancement on actions for continuous improvement. The aim of Statistical Process Control (SPC) is to detect the variation and nonconforming units for improvement in the quality of process. While manually collecting data, the ambiguity or vagueness exists in the data which are called fuzzy data from measurement system or human experts. Fuzzy data can be analyzed by fuzzy control charts in SPC. Fuzzy control charts are implemented for monitoring and analyzing process, and reducing the variability of process. Also, an intelligent system is developed to eliminate or reduce uncertainty on data by using a fuzzy approach.
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Erginel, N., Şentürk, S. (2015). Intelligent Systems in Total Quality Management. In: Kahraman, C., Çevik Onar, S. (eds) Intelligent Techniques in Engineering Management. Intelligent Systems Reference Library, vol 87. Springer, Cham. https://doi.org/10.1007/978-3-319-17906-3_16
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DOI: https://doi.org/10.1007/978-3-319-17906-3_16
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