An empirical study of demand forecasting of non-volatile memory for smart production of semiconductor manufacturing

Abstract

As high-speed computing is crucial to empower intelligent manufacturing for Industry 4.0, non-volatile memory (NVM) is critical semiconductor component of the cloud and data centre for the infrastructures. The NVM manufacturing is capital intensive, in which capacity utilisation significantly affects the capital effectiveness and profitability of semiconductor companies. Since capacity migration and expansion involve long lead times, demand forecasting plays a critical role for smart production of NVM manufacturers for revenue management. However, the shortening product life cycles of integrated circuits (IC), the fluctuations of semiconductor supply chains, and uncertainty involved in demand forecasting make the present problem increasingly difficult in the consumer electronics era. Focusing on the realistic needs of NVM demand forecasting, this study aims to develop a decision framework that integrates an improved technology diffusion model and a proposed adjustment mechanism to incorporate domain insights. An empirical study was conducted in a leading semiconductor company for validation. A comparison of alternative approaches is also provided. The results have shown the practical viability of the proposed approach.

Keywords:

• non-volatile memory
• semiconductor manufacturing
• multi-generation diffusion model
• decision framework for demand forecasting
• manufacturing intelligence
• business analytics

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This research was supported by Ministry of Science and Technology, Taiwan [grant number MOST 103-2218-E-007-023], [grant number MOST 106-2218-E-007-024], [grant number MOST 106-2622-8-155-004-TM1]; Macronix International Ltd.