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An algorithm for improved ETAs estimations and potential impacts on supply chain decision making
KTH, School of Industrial Engineering and Management (ITM). (INDEK)ORCID iD: 0000-0002-9417-9421
2018 (English)In: Procedia Manufacturing, E-ISSN 2351-9789, Vol. 25, p. 185-193Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2018. Vol. 25, p. 185-193
Keywords [en]
ETa, Estimated Time of Arrival, Forecast, decision making, supply chain management, maritime transport
National Category
Other Engineering and Technologies Economics and Business
Research subject
Industrial Economics and Management; Transport Science; Production Engineering; Planning and Decision Analysis
Identifiers
URN: urn:nbn:se:kth:diva-233359DOI: 10.1016/j.promfg.2018.06.073Scopus ID: 2-s2.0-85065659599OAI: oai:DiVA.org:kth-233359DiVA, id: diva2:1239356
Note

QC 20180824

Available from: 2018-08-16 Created: 2018-08-16 Last updated: 2020-03-09Bibliographically approved

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