PRODUCTION LOG DATA ANALYSIS FOR REJECT RATE PREDICTION AND WORKLOAD ESTIMATION
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.
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