Monitoring high complex production processes using process capability indices
The increasing demand and the globalization of the market are leading to increasing levels of quality in production processes, and thus, nowadays, multiple product characteristics must be tested because they are considered critical. In this context, decision makers are forced to interpret a huge amount of quality indicators, when monitoring production processes. This fact leads to a misunderstanding as a result of information overload. The aim of this paper is to help practitioners when monitoring the capability of processes with a huge amount of product characteristics. We propose a methodology that reduces the amount of data in capability analysis by structuring hierarchically the multiple quality indicators obtained in the quality tests. The proposed methodology may help practitioners and decision makers of the industry in three aspects of statistical process monitoring: to identify the part of a complex production process that presents capability problems, to detect worsening over the time in multivariate production processes, and to compare similar production processes. Some illustrative examples based on different kinds of production processes are discussed in order to illustrate the methodology. A case of study based on a real production process of the automotive industry is analyzed using the proposed methodology. We conclude that the proposed methodology reduces the necessary amount of data in capability analysis; and thus, that it provides an added value of great interest for managers and decision makers.
KeywordsProcess monitoring Process capability Multivariate statistics Automotive industry Machining process
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- 4.Ostasevicius V, Jurenas V, Augutis V, Gaidys R, Cesnavicius R, Kizauskiene L, Dundulis R (2016) Monitoring the condition of the cutting tool using self-powering wireless sensor technologies. The International Journal of Advanced Manufacturing Technology, pp 1–15. doi: 10.1007/s00170-016-8939-z
- 5.Seemuang N, McLeay T, Slatter T (2016) Using spindle noise to monitor tool wear in a turning process. International Journal of Advanced Manufacturing Technology, pp 1–10. doi: 10.1007/s00170-015-8303-8
- 11.Sullivan LP (1985) Letters. Qual Prog 18:7–8Google Scholar
- 12.Kane VE (1986) Process capability indices. J Qual Technol 18(1):41–52Google Scholar
- 24.Shaoxi W, Mingxin W, Xiaoya F, Shengbing Z, Ru H (2013) A multivariate process capability index with a spatial coefficient. J Semicond 34(2). doi: 10.1088/1674-4926/34/2/026001
- 30.Wang FK, Tamirat Y (2015) Process Yield for Multivariate Linear Profiles with One-sided Specification Limits. Quality and Reliability Engineering International. doi: 10.1002/qre.1834
- 31.De-Felipe D, Klee T, Folmer J, Benedito E, Vogel-Heuser B (2016) A multivariate process capability index that complies with industry requirements. Paper presented at the Conference of IEEE Industrial Electronics Society (IECON), Florence, doi: 10.1109/IECON.2016.7793509
- 33.De-Felipe D, Benedito E (2017) A review of univariate and multivariate process capability indices. The International Journal of Advanced Manufacturing Technology, pp 1–19. doi: 10.1007/s00170-017-0273-6