Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/228930
Authors: 
Zander, Bennet
Lange, Kerstin
Haasis, Hans-Dietrich
Year of Publication: 
2020
Citation: 
[Editor:] Kersten, Wolfgang [Editor:] Blecker, Thorsten [Editor:] Ringle, Christian M. [Title:] Data Science and Innovation in Supply Chain Management: How Data Transforms the Value Chain. Proceedings of the Hamburg International Conference of Logistics (HICL), Vol. 29 [ISBN:] 978-3-7531-2346-2 [Year:] 2020 [Pages:] 459-485
Abstract: 
Purpose: In order to keep up with the automation Smart Factories will bring into the market, procurement logistics has to be redesigned to ensure self-organizing production. The purpose of this paper is to examine the future changes of the procurement processes as well as the further role of logistics service providers in the procurement network with references to the building industry. Methodology: Using an in-depth literature analysis focusing on the needs of a Smart Factory and the state of art of its procurement logistics current gaps are identified. Subsequently, a modified concept for the delivery of the inbound materials is developed. Findings: The outcome shows, that the traditional truck delivery of the needed goods to a Smart Factory fails to deal with the in-house processes. Solutions have to be generated which provide packaging-free transport to move the already unpacked materials to the production lines more quickly. Furthermore, efficiency gains are identified, which can be generated through the newly adapted procurement logistics concept. Originality: To-date, Smart Factory research has predominantly focused on internal production processes, without taking the externally required procurement logistics processes into closer consideration. However, significant changes due to wireless communication technologies can be expected in the ordering, transportation, un-loading and storage of goods.
Subjects: 
Logistics
Industry 4.0
Digitalization
Innovation
Supply Chain Management
Artificial Intelligence
Data Science
Persistent Identifier of the first edition: 
Creative Commons License: 
https://creativecommons.org/licenses/by-sa/4.0/
Document Type: 
Conference Paper

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.