Optimal diagnosis interval for online quality control methods

Abstract

AbstractOnline quality control methods emphasize manufacturing processes to attain maximum conformance with respect to the specifications of the concerned quality characteristics of a product. One key factor that affects the effectiveness of these methods is the diagnosis interval. In this paper, the existing cost model along with its cost components for online quality control methods has been revisited and modified by incorporating new variables like the rate of production, the loss due to false alarm, the loss due to non-detection of process abnormalities, and considering a workable break-up of diagnosis cost for finding the optimal diagnosis interval from the perspective of present-day manufacturing engineering. As already mentioned, the proposed cost model has not ignored the loss due to the generation of defective items as well as the adjustment cost available in the pertinent literature. The modified cost function thus proposed has been appropriately minimized to obtain the corresponding optimal diagnosis interval. The proposed methodology has been compared numerically with other methodologies to establish its effectiveness. The cornerstone of the proposed methodology lies in reinforcing its effectiveness through a real-life case example in manufacturing. Sensitivity analysis has also been carried out for the real-life case example to fortify the proposed methodology.Keywords: Optimal diagnosis intervalloss functiontotal costadjustment costdiagnosis costtime lag AcknowledgmentsThe authors would like to appreciate the editor and the anonymous referees for their constructive comments on the previous version of this work, which improved the content substantially.Disclosure statementNo potential conflict of interest was reported by the author(s).Author contributionsBoth authors contributed equally to this work.Data availability statementThe authors declare that no data is used in this manuscript.Correction StatementThis article has been corrected with minor changes. These changes do not impact the academic content of the article.Additional informationFundingThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.Notes on contributors SandeepSandeep joined as a Junior Research Fellow in the SQC and OR Division of the Indian Statistical Institute on July 17, 2019. On December 1, 2021, he was promoted to the position of senior research fellow. At present, he is pursuing his PhD work in quality, reliability, and operations research from ISI. Before joining ISI as a research fellow, he completed his MSc in Mathematics in 2018 from the Central University of Haryana in India.Arup Ranjan MukhopadhyayDr. Arup Ranjan Mukhopadhyay has been working as a faculty member [at present, Senior Technical Officer (Professor Grade)] in the Statistical Quality Control and Operations Research Division of the Indian Statistical Institute for more than three decades, which involves applied research, teaching, training, and consultancy in the field of quality management and operations research. Dr. Mukhopadhyay was the Head of the SQC and OR Division at the Indian Statistical Institute for two years during 2020–2022. He has published more than 50 papers in renowned national and international journals. He received a B. Tech. from Calcutta University in 1983, a PGD in SQC and OR in 1985 from the Indian Statistical Institute (ISI), and a two-year Specialist Development Fellowship Program from ISI in 1989. He obtained his PhD (engineering) from Jadavpur University in 2007 in the area of quality engineering. Apart from teaching regularly in the two-year M.Tech. (QROR) course offered by ISI, he has successfully guided several students for PhD work in the fields of quality, reliability, and operations research.