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- W2912072877 abstract "The main focus of the research presented in this paper is to propose new methods for filtering and cleaning large-scale production log data by applying statistical learning models. Successful application of the methods in consideration of a production optimization and a simulation-based prediction framework for decision support is presented through an industrial case study. Key parameters analysed in the computational experiments are fluctuating reject rates that make capacity estimations on a shift basis difficult to cope with. The most relevant features of simulation-based workload estimation are extracted from the products' final test log, which process has the greatest impact on the variance of workload parameters." @default.
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- W2912072877 date "2018-12-09" @default.
- W2912072877 modified "2023-09-23" @default.
- W2912072877 title "Production log data analysis for reject rate prediction and workload estimation" @default.
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- W2912072877 doi "https://doi.org/10.5555/3320516.3320914" @default.
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