Statistical quality control and improvement

Development of a measure to assess quality management in certified firms

“…Therefore, bearing in mind that experience has shown that some firms fail when they implement TQM (Boje and Winsor, 1993;Spector and Beer, 1994) and the implementation of TQM cannot be successful without the use of suitable quality management methods (Sitkin, Sutcliffe and Schoroeder, 1994;Wilkinson et al, 1998;Zhang, 2000) such as statistical techniques (Stuart, Mullins and Drew, 1996), managers of ISO 9000-certified small and medium firms that really want to progress towards a TQM culture, should implement the following factors, using the following methods: − Specialized training. Training cannot stop at an initial level, and must be of a continued nature (Ishikawa, 1985).…”

Electric motor defects diagnosis based on kernel density estimation and Kullback–Leibler divergence in quality control scenario

“…In 1924, Walter Shewhart designed the first control chart and gave a rationale for its use in process monitoring and control (Stuart et al, 1995). The main concept of QC is the "proactiveness", in order to ensure the product quality, monitoring processes and related signals to detect when they "go out of control".…”

Prioritization and selection of parameters for control chart implementation based on technical criticality and cost criticality

“…They also help us indicate that the process variation is higher than its natural level when a process goes beyond the control limits. Thus, practitioners can determine when to adjust the process before it causes substantial failure expenditures such as the cost of scrap and the cost of rework (Stuart et al, 1996;Does et al, 1997;Kang et al, 1999;Antony & Taner, 2003;Bamford & Greatbanks, 2005;Wang & Zhang, 2008;Joekes & Barbosa, 2013). Schippers (1998) presented some significant factors for the success of statistical process control including the process knowledge, the level of control and the number of products.…”