First, the levels of adherence to quality standards should fit in a normal distribution curve, such that most products are within range and as few as possible are on both extremes; poorer quality and better quality than required. These instances of extremes are as a result of production processes affected by chance, which should be reduced as much as possible. The level of standard deviation in the normal distribution curve should be as minimal as possible to ensure that product quality is similar with little variation among them.The most commonly used distribution curves are noncumulative discrete and continuous distribution curves (Wheeler, 2011: 298). The most suitable distribution curve is the normal distribution; if product quality fits in a normal curve it means that the firm is on the correct path to achieving total quality management. However, many factors must be considered when interpreting distribution curves including range, average, and standard deviation among others. The range of the data indicating product conformity should be minimal, which means that products must have as little differences between them as possible. For instance, the firm should define the maximum difference in length or weight of products, which should be as small as possible depending on the level of quality assurance the firm intends to achieve. The average of product qualities should be an indicator of the level of adherence to standards, such that the value should be equal to the standard. In this case, measurement values vary such that the products that do not meet standards equal those that surpass the standards, and the majority of products are in between.According to Wheeler (2011: 374), SPC uses charts classified as control charts for variables and. SPC and Acceptance Sampling.
Leitner, A. (2007) Statistical process control, GRIN Verlag.
Rajput, R.K. (2008) A textbook of manufacturing technology: manufacturing processes, Firewall Media.
Schilling, E.G., & Neubauer, D.V. (2009) Acceptance sampling in quality control, CRC Press.
Wheeler, D.J. (2011) Understanding statistical process control, Spc PR.
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