An algorithm for improved ETAs estimations and potential impacts on supply chain decision making
Procedia Manufacturing(2018)
摘要
Latest years have seen companies in the supply chain dealing with increasing larger amount of data. The ultimate goal is to develop digital ecosystems combined with sensor data to allow companies, including suppliers, logistics service providers, transport carriers, freight forwarders, manufactures to jointly and (almost) openly share data, improve visibility, and optimize operations. In this paper we conceptualize an algorithm that collects maritime transport data, and thereby computes more accurate ETAs. Thereafter, we discuss implications for data-driven management across several functions of a supply chain, e.g. purchasing, marketing, inventory policies, transport synchronization and adaptive process planning.
更多查看译文
关键词
digital supply chains,ETA prediction,predictive/prescriptive analytics,IoT supply chains,CPS supply chains,data-driven management
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络