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Manufacturing Intelligence for Decision of Wafer Starts with Demand Uncertainty Chia-Yu Hsu1, Chen-Fu Chien2*, Wei-Lun Yen2 1 Department of Information Management, Yuan Ze University, Taiwan 2 Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Taiwan *[email protected] Abstract The wafer starts for production schedule significantly influences the production effectiveness and profitability for semiconductor fabrication. Demand forecast provides critical input to support the decision of wafer starts. The underestimate of demand leads to the loss of profit and customer satisfaction and the overestimated of demand results in the increase of stock cost. Customers often overestimate the actual order for sales to ensure the order fulfillment. In practice, most companies forecast the demand by the sales information from different customers and then adjust the demand by their domain knowledge. Moreover, the involved demand uncertainty and fluctuation in semiconductor supply chain also increases the difficulty of demand forecast. Most of existing studies regarding the demand forecast focused on improving forecast accuracy by applying time series model, soft computing methods based on historical data. However, little research has been done on incorporating the risk of capacity shortage and capacity surplus with the forecast model. Focused on the realistic needs for manufacturing intelligence, this study aims to construct a manufacturing intelligence framework to develop the statistical decision model for forecast customer demands based on demand adjustment and decision under uncertainty theories. In particular, the customer fulfillment ratio is developed as the basis of demand estimation and predicted interval is built under different fabrication utilization. Then, max-min return and min-max regret strategies are used to determine the demand under overestimate and underestimate, respectively, for reduce the potential business loss. To estimate the validity of the proposed approach, an empirical study was conducted in a leading semiconductor manufacturing company, in which 72 months were applied to construct the demand adjustment model and the following 12 moths were used to examine the forecast accuracy and cost-benefit analysis. Based on the evaluation results, average error of forecast is decreased from 20% to 7% and max error of forecast is decreased from 50% to 20%. The result showed the practical visibility to employ the proposed approach for demand adjustment with little error as a basis to enhance the decision quality for wafer start to reduce the risk of capacity shortage or surplus. Keywords: manufacturing intelligence, statistical decision, demand uncertainty, wafer start, fulfillment, semiconductor manufacturing