期刊
COMPUTERS & INDUSTRIAL ENGINEERING
卷 135, 期 -, 页码 940-949出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.cie.2019.07.002
关键词
Demand forecast; Intermittent demand; UNISON data-driven framework; Supply chain management; Artificial intelligence; Global manufacturing networks
资金
- Ministry of Science and Technology, Taiwan [MOST 108-2634-F-007-001, MOST 108-2634-F-007 -008]
- WPG Holding Ltd., Taiwan
The complexity involved in demand forecast for supply chain management of electronics components is exponentially increasing owing to demand fluctuations in consumer electronics, shortening of product life cycles, continuous technology migration, lengthy production cycle time, and long lead time for capacity expansion. While global manufacturing networks often suffer the risks of oversupply and shortage of key components, the distributor that is the key intermediate participator in electronics product supply chain buys components from the suppliers, warehouses them, and resells different parts to a number of electronics manufacturers with vendor-managed inventories. Thus, the component distributors forecast the demands for large assortments of stock keeping units (SKUs) with distinct dynamics for inventory control and supply chain management. To address realistic needs to enhance demand forecast performance, this study aims to develop a UNISON data driven analytics framework that integrates machine learning technologies and temporal aggregation mechanism to forecast the demands of intermittent electronics components. An empirical study is conducted in a world leading semiconductor distributor for validation. The results have shown practical vitality of the proposed approach with better performance than conventional approaches and the existing practice. Indeed, the developed solution has been employed in this company to support flexible decisions to empower agile logistics and supply chain resilience for smart production.
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