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Manufacturing Intelligence for Decision of Wafer Starts with Demand
Chia-Yu Hsu1, Chen-Fu Chien2*, Wei-Lun Yen2
Department of Information Management, Yuan Ze University, Taiwan
Department of Industrial Engineering and Engineering Management,
National Tsing Hua University, Taiwan
*[email protected]
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