Abstract
In the semiconductor industry a reliable delivery forecast is helpful to optimize demand planning. Very often cycle time estimations for frontend, backend production, testing and transits are used to predict delivery times on product level and to determine when products have to be started to fulfill customer demands on time. Frontend production usually consumes a big portion of the cycle time of a product. Therefore a reliable cycle time estimation for a frontend production is crucial for the accuracy of the overall cycle time prediction. We compare two different methods to predict cycle times and delivery forecasts on product and lot level for a frontend production: a Big Data approach, where historical data is analyzed to predict future behavior, and a fab simulation model.
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