Abstract
The rapid development and adoption of sensors and data storage solutions such as IOT (Internet of Things) has enabled the collection of a large amount of data in production facilities. The data may come from different sources such as process parameters and quality characteristics. However, traditional statistical process control (SPC) tools were not built to take the full advantages of the data provided. Traditional SPC tools such as control charts are often applied to critical quality characteristics (QCs) on a product rather than incorporating process parameters associated with the critical QCs. This chapter proposes a method that is capable of monitoring all process and quality data simultaneously. The proposed method adopts precontrol and group control chart ideas to pinpoint change location and timeframe in a production system. After the change location and timeframe have been identified, more elaborate models or data analytics methods can be used to identify potential assignable causes. Simulation studies are conducted to establish the properties of the proposed method. Guidelines are provided to help users how to implement the proposed method in any production facility including those facing big data issues.
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Koppel, S., I Chang, S. (2019). Statistical System Monitoring (SSM) for Enterprise-Level Quality Control. In: Lio, Y., Ng, H., Tsai, TR., Chen, DG. (eds) Statistical Quality Technologies. ICSA Book Series in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-030-20709-0_3
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DOI: https://doi.org/10.1007/978-3-030-20709-0_3
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