{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T00:39:00Z","timestamp":1778114340426,"version":"3.51.4"},"reference-count":41,"publisher":"Emerald","issue":"1","license":[{"start":{"date-parts":[[2022,5,31]],"date-time":"2022-05-31T00:00:00Z","timestamp":1653955200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["GS"],"published-print":{"date-parts":[[2023,1,25]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>The purpose of this paper is to solve the problem of quality prediction in the equipment production process and provide a method to deal with abnormal data and solve the problem of data fluctuation.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>The analytic hierarchy process-process failure mode and effect analysis (AHP-PFMEA) structure tree is established based on the analytic hierarchy process (AHP) and process failure mode and effect analysis (PFMEA). Through the failure mode analysis table of the production process, the weight of the failure process and stations is determined, and the ranking of risk failure stations is obtained so as to find out the serious failure process and stations. The spectrum analysis method is used to identify the fault data and judge the \u201cabnormal\u201d value in the fault data. Based on the analysis of the impact, an \u201coffset operator\u201d is designed to eliminate the impact. A new moving average denoise operator is constructed to eliminate the \u201cnoise\u201d in the original random fluctuation data. Then, DGM (1,1) model is constructed to predict the production process quality.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>It is discovered the \u201coffset operator\u201d can eliminate the impact of specific shocks effectively, moving average denoise operator can eliminate the \u201cnoise\u201d in the original random fluctuation data and the practical application of the shown model is very effective for quality predicting in the equipment production process.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Practical implications<\/jats:title><jats:p>The proposed approach can help provide a good guidance and reference for enterprises to strengthen onsite equipment management and product quality management. The application on a real-world case showed that the DGM (1,1) grey discrete model is very effective for quality predicting in the equipment production process.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>The offset operators, including an offset operator for a multiplicative effect and an offset operator for an additive effect, are proposed to eliminate the impact of specific shocks, and a new moving average denoise operator is constructed to eliminate the \u201cnoise\u201d in the original random fluctuation data. Both the concepts of offset operator and denoise operator with their calculation formulas were first proposed in this paper.<\/jats:p><\/jats:sec>","DOI":"10.1108\/gs-09-2021-0143","type":"journal-article","created":{"date-parts":[[2022,5,30]],"date-time":"2022-05-30T06:31:40Z","timestamp":1653892300000},"page":"34-57","source":"Crossref","is-referenced-by-count":5,"title":["A moving average denoise operator and grey discrete production process quality prediction 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