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

Author

Listed:
  • Młody Michał

    (2 Poznań University of Economics and Business, Poland)

  • Ratajczak-Mrozek Milena

    (3 Poznań University of Economics and Business, Poland)

  • Sajdak Maja

    (1 Poznań University of Economics and Business, Poland)

Abstract

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.

Suggested Citation

  • Młody Michał & Ratajczak-Mrozek Milena & Sajdak Maja, 2023. "Industry 4.0 technologies and managers’ decision-making across value chain. Evidence from the manufacturing industry," Engineering Management in Production and Services, Sciendo, vol. 15(3), pages 69-83, September.
    • Handle: RePEc:vrs:ecoman:v:15:y:2023:i:3:p:69-83:n:4
    DOI: 10.2478/emj-2023-0021
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    References listed on IDEAS

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    1. Lucianetti, Lorenzo & Chiappetta Jabbour, Charbel Jose & Gunasekaran, Angappa & Latan, Hengky, 2018. "Contingency factors and complementary effects of adopting advanced manufacturing tools and managerial practices: Effects on organizational measurement systems and firms' performance," International Journal of Production Economics, Elsevier, vol. 200(C), pages 318-328.
    2. Marco Savastano & Carlo Amendola & Fabrizio D’Ascenzo, 2018. "How Digital Transformation is Reshaping the Manufacturing Industry Value Chain: The New Digital Manufacturing Ecosystem Applied to a Case Study from the Food Industry," Lecture Notes in Information Systems and Organization, in: Rita Lamboglia & Andrea Cardoni & Renata Paola Dameri & Daniela Mancini (ed.), Network, Smart and Open, pages 127-142, Springer.
    3. Yugal Nauhria & Makarand S. Kulkarni & Sunil Pandey, 2018. "Development of Strategic Value Chain Framework for Indian Car Manufacturing Industry," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 19(1), pages 21-40, March.
    4. Koc, T. & Bozdag, E., 2017. "Measuring the degree of novelty of innovation based on Porter's value chain approach," European Journal of Operational Research, Elsevier, vol. 257(2), pages 559-567.
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