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Logistics 5.0 Implementation Model Based on Decision Support Systems

Author

Listed:
  • Maja Trstenjak

    (Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb, Ivana Lucica 5, 10000 Zagreb, Croatia)

  • Tihomir Opetuk

    (Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb, Ivana Lucica 5, 10000 Zagreb, Croatia)

  • Goran Đukić

    (Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb, Ivana Lucica 5, 10000 Zagreb, Croatia)

  • Hrvoje Cajner

    (Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb, Ivana Lucica 5, 10000 Zagreb, Croatia)

Abstract

With the introduction of Society 5.0 for the sustainable future, special caution is given to humans’ role within the general system. Similarly, Industry 5.0 as a concept has been presented, followed by Logistics 5.0 in its theoretical framework. The transition towards the new concept of Logistics 5.0 requires an accurate and optimal strategic plan definition for which, in this paper, an implementation model based on decision support systems will be developed. The output data from this model are the priority of Logistics 5.0 elements (from five groups—green warehousing, green transport, green packaging, infrastructure and organization, and human resources) for the optimal implementation, based on three goals (initial investment, return of investment time, implementation and exploitation complexity) that companies aim to achieve in the future. The model is based on the analytic hierarchy process, and data were collected from an expert group and analyzed with several statistical methods. The result is a model that provides an optimal strategy for the implementation of elements of Logistics 5.0. The implementation priority list of elements is very beneficial for the management of many companies from various types of industries.

Suggested Citation

  • Maja Trstenjak & Tihomir Opetuk & Goran Đukić & Hrvoje Cajner, 2022. "Logistics 5.0 Implementation Model Based on Decision Support Systems," Sustainability, MDPI, vol. 14(11), pages 1-19, May.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:11:p:6514-:d:824808
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    References listed on IDEAS

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    1. Ageron, Blandine & Gunasekaran, Angappa & Spalanzani, Alain, 2012. "Sustainable supply management: An empirical study," International Journal of Production Economics, Elsevier, vol. 140(1), pages 168-182.
    2. Amitabh Bhargava & Deepshikha Bhargava & P. Naveen Kumar & Guna Sekhar Sajja & Samrat Ray, 2022. "Industrial IoT and AI implementation in vehicular logistics and supply chain management for vehicle mediated transportation systems," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(1), pages 673-680, March.
    3. Burroughs, Benjamin & Burroughs, W. Jeffrey, 2020. "Digital logistics: Enchantment in distribution channels," Technology in Society, Elsevier, vol. 62(C).
    4. Sgarbossa, F. & Grosse, E. H. & Neumann, W. P. & Battini, D. & Glock, C. H., 2020. "Human factors in production and logistics systems of the future," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 120615, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
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    Cited by:

    1. Adam Panek, 2024. "Wykorzystanie sztucznej inteligencji w procesach HR przedsiębiorstw logistycznych," Nowoczesne Systemy Zarządzania. Modern Management Systems, Military University of Technology, Faculty of Security, Logistics and Management, Institute of Organization and Management, issue 3, pages 29-40.

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