Industry 4.0 technologies and managers' decision-making across value chain. Evidence from the manufacturing industry

被引:0
|
作者
Młody M. [1 ]
Ratajczak-Mrozek M. [1 ]
Sajdak M. [1 ]
机构
[1] Poznań University of Economics and Business, Poland
关键词
decision-making; Industry; 4.0; manufacturing industry; technologies; value chain;
D O I
10.2478/emj-2023-0021
中图分类号
学科分类号
摘要
The paper aims to identify how Industry 4.0 technologies affect the quality and speed of the managers' decision-making process across the different stages of the value chain, based on the example of the manufacturing sector. The paper adopts qualitative research, based on nine in-depth interviews with key informants, to capture senior executives' experiences with implementing Industry 4.0 technologies in their organisations. The research is focused on three manufacturing industries: the automotive, food and furniture industries. The research shows that depending on the stage of the value chain, different Industry 4.0 technologies are more suitable for the support of managers' decisions. Various Industry 4.0 technologies support decision-making at different stages of the manufacturing value chain. In the Design stage, 3D printing and scanning technologies play a crucial role. In the case of Inbound Logistics, robotisation, automation, Big Data analysis, and Business Intelligence are most useful. During the Manufacturing stage, robotisation, automation, 3D printing, scanning, Business Intelligence, cloud computing, and machine-to-machine (M2M) integration enable quick decision-making and speed up production. Sensors and the Internet of Things (IoT) optimise distribution in the Outbound Logistics stage. And finally, Business Intelligence supports decisions within the Sales and Marketing stage. It is also the most versatile technology among all particular stages. The paper provides empirical evidence on the Industry 4.0 technology support in decision-making at different stages of the manufacturing value chain, which leads to more effective value chain management, ensuring faster and more accurate decisions at each value-chain stage. When using properly selected Industry 4.0 technologies, managers can optimise their production processes, reduce costs, avoid errors and improve customer satisfaction. Simultaneously, Industry 4.0 technologies facilitate predictive analytics to forecast and anticipate future demand, quality issues, and potential risks. This knowledge allows organisations to make better decisions and take proactive actions to prevent problems. © 2023 Michał Młody et al., published by Sciendo.
引用
收藏
页码:69 / 83
页数:14
相关论文
共 50 条
  • [31] Creating a road map for industry 4.0 by using an integrated fuzzy multicriteria decision-making methodology
    Kaya, Ihsan
    Erdogan, Melike
    Karasan, Ali
    Ozkan, Betul
    SOFT COMPUTING, 2020, 24 (23) : 17931 - 17956
  • [32] Does value chain participation facilitate the adoption of Industry 4.0 technologies in developing countries?
    Delera, Michele
    Pietrobelli, Carlo
    Calza, Elisa
    Lavopa, Alejandro
    WORLD DEVELOPMENT, 2022, 152
  • [33] Systems Integration in the Lean Manufacturing Systems Value Chain to Meet Industry 4.0 Requirements
    Doh, Stephanie W.
    Deschamps, Fernando
    Pinheiro De Lima, Edson
    TRANSDISCIPLINARY ENGINEERING: CROSSING BOUNDARIES, 2016, 4 : 642 - 650
  • [34] CONVERSION FROM TRADITIONAL MANUFACTURING TO DIGITAL MANUFACTURING, AN INDUSTRY 4.0 APPLICATION IN FURNITURE MAKING
    Xiao, Angran
    Chen, Mason
    Gailani, Gaffar
    Zhang, Andy
    PROCEEDINGS OF THE ASME 2020 INTERNATIONAL MECHANICAL ENGINEERING CONGRESS AND EXPOSITION, IMECE2020, VOL 2B, 2020,
  • [35] Hybrid fuzzy multi-attribute decision making model for evaluation of advanced digital technologies in manufacturing: Industry 4.0 perspective
    Medic, N.
    Anisic, Z.
    Lalic, B.
    Marjanovic, U.
    Brezocnik, M.
    ADVANCES IN PRODUCTION ENGINEERING & MANAGEMENT, 2019, 14 (04): : 483 - 493
  • [36] Sustainable Industry 4.0 and corporate social responsibility challenges in environmental decision-making effectiveness
    Raissi, Nizar
    Hakeem, Anas
    Haidar, Hassan Mousa
    JOURNAL OF ASIA BUSINESS STUDIES, 2025,
  • [37] The impact of Industry 4.0 on the reconciliation of dynamic capabilities: evidence from the European manufacturing industries
    Felsberger, Andreas
    Qaiser, Fahham Hasan
    Choudhary, Alok
    Reiner, Gerald
    PRODUCTION PLANNING & CONTROL, 2022, 33 (2-3) : 277 - 300
  • [38] Enabling Technologies for Industry 4.0 Manufacturing and Supply Chain: Concepts, Current Status, and Adoption Challenges
    Raut R.D.
    Gotmare A.
    Narkhede B.E.
    Govindarajan U.H.
    Bokade S.U.
    IEEE Engineering Management Review, 2020, 48 (02): : 83 - 102
  • [39] A novel hybrid decision-making framework for measuring Industry 4.0-driven circular economy performance for textile industry
    Ali, Sadia Samar
    Torgul, Belkiz
    Paksoy, Turan
    Luthra, Sunil
    Kayikci, Yasanur
    BUSINESS STRATEGY AND THE ENVIRONMENT, 2024, 33 (08) : 7825 - 7854
  • [40] Manufacturing Industry Performance Appraisals: Multi-Criteria Decision-Making Model
    Andriani, Debrina Puspita
    Wijayanti, Febry
    Rahmani, Ilma Visi
    Aini, Azizah Putri Nur
    INTERNATIONAL JOURNAL OF INTEGRATED ENGINEERING, 2022, 14 (06): : 28 - 37