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• W2028248052 abstract "Maintaining the best quality is essential for the survival of a company in a globally competitive world. Six Sigma activity has been widely accepted as one of the most efficient and powerful problem-solving methods for quality issues. The define-measure-analyze-improve-control (DMAIC) approach based on data has been very powerful for identifying, defining, solving a problem, and controlling the solution. However, for many process engineers, the DMAIC approach is so general an approach that they have difficulty defining and solving problems. To solve this limitation, we need to introduce the knowledge of chemical industries to the DMAIC. This paper presents a hierarchical decision procedure for quality improvement that can enhance the power of the Six Sigma approach dramatically by capturing the domain-specific knowledge for the design, operation, and control of petrochemical processes and integrating them into a hierarchy of decisions. The proposed hierarchical decision procedure offers a well-organized knowledge structure so that a user with little experience in the chemical industry can solve a quality problem. The procedure progresses through a hierarchy of decision levels reflecting the structural information of large-scale petrochemical processes. At every level, the systematic procedures offer heuristics and/or techniques to generate candidates, evaluate generated candidates, and choose the most promising candidates. The procedure allows us to save significant amounts of time and cost, since a user can find the cause of the quality problem and its solution in an efficient and systematic manner. Each level effectively evaluates the candidates and validates the results. We believe that the procedure can be applied to many other large-scale petrochemical processes." @default.
• W2028248052 created "2016-06-24" @default.
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• W2028248052 date "2008-04-01" @default.
• W2028248052 modified "2023-09-25" @default.
• W2028248052 title "A hierarchical decision procedure for productivity innovation in large-scale petrochemical processes" @default.
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• W2028248052 doi "https://doi.org/10.1016/j.compchemeng.2007.06.007" @default.
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