To fully accommodate the correlations between semiconductor product demands and external information such as the end market trends or regional economy growth, a linear dynamic system is introduced in this chapter to improve forecasting performance in supply chain operations. In conjunction with the generic Gaussian noise assumptions, the proposed state-space model leads to an expectation-maximization (EM) algorithm to estimate model parameters and predict production demands. Since the set of external indicators is of high dimensionality, principal component analysis (PCA) is applied to reduce the model order and corresponding computational complexity without loss of substantial statistical information. Experimental study on certain real electronic products demonstrates that this forecasting methodology produces more accurate predictions than other conventional approaches, which thereby helps improve the production planning and the quality of semiconductor supply chain management.
Zhang, F. (2008), "A principal component analysis-based linear dynamic system for demand forecasting", Lawrence, K.D. and Geurts, M.D. (Ed.) Advances in Business and Management Forecasting (Advances in Business and Management Forecasting, Vol. 5), Emerald Group Publishing Limited, Leeds, pp. 93-113. https://doi.org/10.1016/S1477-4070(07)00206-1
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