Manufacturing intelligence for semiconductor demand forecast based on technology diffusion and product life cycle Chen-Fu Chien a,n , Yun-Ju Chen a , Jin-Tang Peng b a Department of Industrial Engineering & Engineering Management, National Tsing Hua University, Hsinchu 30043, Taiwan, ROC b Department of Business Administration, Yuanpei University, Hsinchu 30015, Taiwan, ROC article info Article history: Received 31 December 2009 Accepted 5 July 2010 Available online 18 July 2010 Keywords: Manufacturing intelligence Demand forecast Technology diffusion Product life cycle Manufacturing strategy Semiconductor abstract Semiconductor industry is capital intensive in which capacity utilization significantly affect the capital effectiveness and profitability of semiconductor manufacturing companies. Thus, demand forecasting provides critical input to support the decisions of capacity planning and the associated capital investments for capacity expansion that require long lead-time. However, the involved uncertainty in demand and the fluctuation of semiconductor supply chains make the present problem increasingly difficult due to diversifying product lines and shortening product life cycle in the consumer electronics era. Semiconductor companies must forecast future demand to provide the basis for supply chain strategic decisions including new fab construction, technology migration, capacity transformation and expansion, tool procurement, and outsourcing. Focused on realistic needs for manufacturing intelligence, this study aims to construct a multi-generation diffusion model for semiconductor product demand forecast, namely the SMPRT model, incorporating seasonal factor (S), market growth rate (M), price (P), repeat purchases (R), technology substitution (T), in which the nonlinear least square method is employed for parameter estimation. An empirical study was conducted in a leading semiconductor foundry in Hsinchu Science Park and the results validated the practical viability of the proposed model. This study concludes with discussions of the empirical findings and future research directions. & 2010 Elsevier B.V. All rights reserved. 1. Introduction Semiconductor industry is capital intensive, in which most chip makers focus on core competence of wafer fabrication to enhance the effectiveness of capital investments for technology migration and capacity expansion requiring long lead-time (Wu and Chien, 2008). Indeed, corporate manufacturing strategic decisions involve the interrelated elements including pricing strategies (P), demand forecast and demand fulfillment planning (D), capacity planning and capacity portfolio (C), capital expenditure (C), and cost structure (C), which will affect the overall return (R) of a company, as illustrated in the PDCCCR conceptual framework of Fig. 1 (Chien, 2009). Thus, semiconductor manufacturing companies have to forecast future demands to provide the basis for manufacturing strategic decisions including new fab construction, technology migration, capacity transformation and expansion, tool procurement, and outsourcing (Cakanyildirim and Roundy, 2002; Chou et al., 2007). Given demand uncertainty and forecast errors, companies often carry a safety stock in terms of the days of in the semiconductor supply chain. As shown in the Bullwhip Effect (Lee et al., 1997), the variations are amplified as moving upstream in the supply chain. Thus, it is critical for high-tech industry to develop flexible forecasting systems that allow them to quickly respond to mitigate the negative impacts of the Bullwhip Effect involved in the supply chain to maintain robust demand fulfillment strategies. However, the demand fluctuation due to shortening product life cycle and increasing product diversification in the consumer electronics era make the demand forecast problem increasingly difficult and complicated. Demand forecast errors cause either inefficient capacity utilization or capacity shortage that will significantly affect the capital effectiveness and profitability of semiconductor manufacturing companies. In practice, most companies forecast the demand by combin- ing regional sales inputs from various customers and then adjusted it with their domain knowledge and market insights. However, sales inputs tend to be biased by the customers and the Bullwhip Effect in the supply chain. Different forecasting methods have been applied in different areas. Most of the existing demand forecasting studies employ time series methods (Hamilton, 1994). However, these methods have difficulty for expressing the adoption process of new products. In addition, forecasting methods that are designed for a single generation cannot consider inter-generational substitution involved in semiconductor indus- try. Driven by Moore’s Law, the semiconductor industry has continued technology migrations and wafer size enlargement to Contents lists available at ScienceDirect journal homepage: www.elsevier.com/locate/ijpe Int. J. Production Economics 0925-5273/$ - see front matter & 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.ijpe.2010.07.022 n Corresponding author. Tel.: + 886 3 5742648; fax: + 886 3 5722685. E-mail address: cfchien@mx.nthu.edu.tw (C.-F. Chien). Int. J. Production Economics 128 (2010) 496–509