Functional Requirements and Supply Chain Digitalization in Industry 4.0

Lu Han (), Hanping Hou (), Z. M. Bi (), Jianliang Yang () and Xiaoxiao Zheng ()
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Lu Han: Beijing Jiaotong University
Hanping Hou: Beijing Jiaotong University
Z. M. Bi: Purdue University Fort Wayne
Jianliang Yang: Beijing University of Chemical Technology
Xiaoxiao Zheng: Beijing Jiaotong University

Information Systems Frontiers, 2024, vol. 26, issue 6, No 15, 2273-2285

Abstract: Abstract Industry 4.0 aims to automate traditional manufacturing and industrial practices with the aids of recently developed information technologies such as cyber-physical systems, Internet of things, big data analytics, and cloud computing. Implementation of industry 4.0 in manufacturing leads to the digitization of all manufacturing businesses including computer aided design and manufacturing, enterprise resource planning, and supply chain management (SCM). This paper focuses on the challenges and solutions in digitizing supply chains in dynamic, distributed, and decentralized business environments. The complexity and dynamics of supply chains in industry 4.0 are discussed, the performance of a supply chain is evaluated from the perspectives of costs and quality of services, and supply chain management is formulated as an optimization problem for higher requirements of quality of services, efficiency, and timeliness. The challenges of developing digitization solutions to data acquisition, data fusion, and data-driven decision-making supports are discussed in detail. The potential solutions to these challenges are proposed and the impacts on supply chain management are assessed using the data from in a list of automotive manufacturers in China. It has been found the proposed solutions will make positive and significant impact on the digitation of supply chains.

Keywords: Supply chain management; Supply chain digitization; Quality of services (QoS); Data acquisition; Data fusion; Data-driven decision-making supports; Industry 4.0; Industrial information integration engineering (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10796-021-10173-1

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