Manufacturing intelligence for semiconductor demand forecast based on technology diffusion and product life cycle

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.