文章基本信息

标题：Application of machine learning on plan instability in master production planning of a semiconductor supply chain
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作者：Tim Lauer ; Sarah Legner ; Michael Henke 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2019
卷号：52
期号：13
页码：1248-1253
DOI：10.1016/j.ifacol.2019.11.369
语种：English
出版社：Elsevier
摘要：The progress of digitalization enables new potentials to supply chain management by available data as well as by analysis methods like machine learning. This paper focuses on the master production planning matching demand and supply for a midterm time horizon, in a volatile, diverse and capacity constrained environment. Therefore, a framework for measuring instability is outlined, a machine learning approach to predict instability is developed and applied using the CRISP-DM methodology on real data of a semiconductor manufacturer. The evaluation and results foster the concept and the field of application, but request the next step of prescriptive instability minimization.
关键词：Keywordsmachine learningproduction planninginstabilitysupply chaindigitalization