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With the ongoing transformation of business to the era of Industry 5.0, related IT technologies and solutions dedicated to logistics are undergoing transformation. This article presents a workflow that will help managers choose the correct strategy and sequence for implementing solutions that help companies embark on the path to Industry 5.0. The practical implications of the results include a clear roadmap for companies implementing Industry 5.0. The proposed IT hierarchy and LSCM prioritization provide decision-makers with actionable insights for planning investments, selecting technologies, and improving resilience and efficiency. This study supports strategic decisions across sectors by highlighting the most impactful digital tools that can drive transformation, sustainability, and competitive advantage in real business environments. In the scientific context, this article presents a template combining IT technologies used in companies and logistics and supply chain management (LSCM) planes in relation to the Industry 5.0 concept. Then, using the hybrid DEMATELPROMETHEE II methodology, the characteristic parameters of these spheres are assessed and hierarchized. The results presented in this article indicate that among the areas characterizing Industry 5.0, resilience solutions should be implemented first. Among IT technologies, solutions from the area of artificial intelligence (AI) and machine learning (ML) should play the leading role and, in terms of LSCM areas, the most important solutions should cover the sphere of demand planning and forecasting.
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Tom
Strony
22--33
Opis fizyczny
Bibliogr. 46 poz., tab.
Twórcy
autor
- Maritime University of Szczecin, Faculty of Engineering and Economics of Transport 11 Pobożnego St., 70-506 Szczecin, Poland
Bibliografia
- 1. Ahmed, T., Karmaker, C.L., Nasir, S.B., Moktadir, M.A. & Paul, S.K. (2023) Modeling the artificial intelligence-based imperatives of Industry 5.0 towards resilient supply chains: A post-COVID-19 pandemic perspective. Computers & Industrial Engineering 177, 109055, doi: 10.1016/j.cie.2023.109055.
- 2. Ali, I., Nguyen, K. & Oh, I. (2025) Systematic literature review on Industry 5.0: current status and future research directions with insights for the Asia Pacific countries. Asia Pacific Business Review, pp. 1–28, doi: 10.1080/13602381. 2025.2452877.
- 3. Antonaci, F.G., Olivetti, E.C. & Marcolin, F. (2024) Workplace well-being in Industry 5.0: A worker-centered systematic review. Sensors 24, 5473, doi: 10.3390/ s24175473.
- 4. Barreto, L., Amaral, A. & Pereira, T. (2017) Industry 4.0 implications in logistics: an overview. Procedia Manufacturing 13, pp. 1245‒1252, doi: 10.1016/j.promfg.2017.09.045.
- 5. Behzadian, M., Kazemzadeh, R.B. & Albadvi, A. (2010) PROMETHEE: A comprehensive literature review on methodologies and applications. European Journal of Operational Research 200 (1), pp. 198‒215, doi: 10.1016/j. ejor.2009.01.021.
- 6. Brückner, A., Wölke, M., Hein-Pensel, F., Schero, E., Winkler, H. & Jabs, I. (2025) Assessing Industry 5.0 readiness ‒ Prototype of a holistic digital index to evaluate sustainability, resilience and human-centered factors. International Journal of Information Management Data Insights 5 (1), 100329, doi: 10.1016/j.jjimei.2025.100329.
- 7. Brusset, X., Ivanov, D. & Jebali, A. (2023) A dynamic approach to supply chain reconfiguration and ripple effect analysis in an epidemic. International Journal of Production Economics 263, 108935, doi.org/10.1016/j. ijpe.2023.108935.
- 8. Cheung, K.F, Bell, M.G.H. & Bhattacharjya, J. (2021) Cybersecurity in logistics and supply chain management: An overview and future research directions. Transportation Research Part E: Logistics and Transportation Review 146, 102217, doi: 10.1016/j.tre.2020.102217.
- 9. Chrifi-Alaoui, C., Bouhaddou, I., Chaouni Benabdellah, A. & Zekhnini, K. (2025) Industry 5.0 for sustainable supply chains: A fuzzy AHP approach for evaluating the adoption barriers. Procedia Computer Science 253, pp. 2645– 2654, doi: 10.1016/j.procs.2025.01.324.
- 10. Dacre, N., Yan, J., Frei, R., Al-Mhdawi, M.K.S. & Dong, H. (2024) Advancing sustainable manufacturing: A systematic exploration of Industry 5.0 supply chains for sustainability, human-centricity, and resilience. Production Planning & Control, pp. 1–30, doi: 10.1080/09537287.2024.2380361.
- 11. Darzi, M.A. (2025) Evaluating e-waste mitigation strategies based on Industry 5.0 enablers: An integrated scenario-based BWM and F-VIKOR approach. Journal of Environmental Management 373, 123999, doi: 10.1016/j. jenvman.2024.123999.
- 12. Dyczkowska, J.A., Chamier-Gliszczyński, N. & Olkiewicz, M. (2024) Evaluation of IT systems in logistics. Procedia Computer Science 246, pp. 4297‒4306, doi: 10.1016/j. procs.2024.09.279.
- 13. Ferraro, C., Demsar, V., Sands, S., Restrepo, M. & Campbell, C. (2024) The paradoxes of generative AI-enabled customer service: A guide for managers. Business Horizons 67 (5), pp. 549‒559, doi: 10.1016/j.bushor.2024.04.013.
- 14. Fournier, É., Jeoffrion, C. & Hmedan, B. (2024) Human-cobot collaboration’s impact on success, time completion, errors, workload, gestures and acceptability during an assembly task. Applied Ergonomics 119, 104306, doi: 10.1016/j.apergo.2024.104306.
- 15. Ghobakhloo, M., Iranmanesh, M., Fathi, M., Rejeb, A., Foroughi, B. & Nikbin, D. (2024) Beyond Industry 4.0: A systematic review of Industry 5.0 technologies and implications for social, environmental and economic sustainability. Asia-Pacific Journal of Business Administration (aheadof-print), doi: 10.1108/apjba-08-2023-0384.
- 16. Ghosh, S., Sarkar, S.K. & Roy, P. (2025). Application of automation and artificial intelligence (AI) in green transportation system, pp. 21-42. In: Khang, A. (eds) Driving Green Transportation System Through Artificial Intelligence and Automation. Lecture Notes in Intelligent Transportation and Infrastructure. Springer, Cham. doi: 10.1007/978-3- 031-72617-0_2.
- 17. Gladysz, B., Van Erp, T., Carvalho, N.G.P., Gerolamo, M.C., Gonçalves, R. & Rytter, N.G.M. (2024) Industry 5.0: A new strategy framework for sustainability management and beyond. Journal of Cleaner Production 461, 142271, doi: 10.1016/j.jclepro.2024.142271.
- 18. Idrissi, Z.K., Lachgar, M. & Hrimech, H. (2024) Blockchain, IoT and AI in logistics and transportation: A systematic review. Transport Economics and Management 2, pp. 275‒285, doi: 10.1016/j.team.2024.09.002.
- 19. Javaid, M., Haleem, A., Singh, R.P., Rab, S. & Suman, R. (2022) Significant applications of Cobots in the field of manufacturing. Cognitive Robotics 2, pp. 222‒233, doi: 10.1016/j.cogr.2022.10.001.
- 20. Kayvanfar, V., Elomri, A., Kerbache, L., Vandchali, H.R. & El Omri, A. (2024) A review of decision support systems in the internet of things and supply chain and logistics using web content mining. Supply Chain Analytics 6, 100063, doi: 10.1016/j.sca.2024.100063.
- 21. Koca, G., Egilmez, O. & Akcakaya, O. (2021) Evaluation of the smart city: Applying the dematel technique. Telematics and Informatics 62, 101625, doi doi.org/10.1016/j. tele.2021.101625.
- 22. Kunicina, N., Beliaev, V., Grants, R., Caiko, J., Amanova, R., Brüzgiené, R. & Mansurova, M. (2024) Decision-making system for electric vehicle management by integrating smart technologies and local characteristics. Applied Sciences 14, 11150, doi: 10.3390/app142311150.
- 23. Lemardelé, C., Estrada, M. & Pagès, L. (2021) Potentialities of drones and ground autonomous delivery devices for last-mile logistics. Transportation Research Part E: Logistics and Transportation Review 149, 102325, doi: 10.1016/j. tre.2021.102325.
- 24. Li, Y., Li, X. & Gao, L. (2025) Real-time scheduling for production-logistics collaborative environment using multi-agent deep reinforcement learning. Advanced Engineering Informatics 65 (Part B), 103216, doi: 10.1016/j. aei.2025.103216.
- 25. Liu, Y., Pan, S. & Ballot, E. (2024) Unveiling the potential of digital twins in logistics and supply chain management: Services, capabilities, and research opportunities. Digital Engineering 3, 100025, doi: 10.1016/j.dte.2024.100025.
- 26. Momena, A.F., Gazi, K.H., Rahaman, M., Sobczak, A., Salahshour, S., Mondal, S.P. & Ghosh, A. (2024) Ranking and challenges of supply chain companies using mcdm methodology. Logistics 8 (3), 87, doi: 10.3390/ logistics8030087.
- 27. Murtaza, A.A., Saher, A. & Zafar, M.H. (2024) Paradigm shift for predictive maintenance and condition monitoring from Industry 4.0 to Industry 5.0: A systematic review, challenges and case study. Results in Engineering 24, 102935, doi: 10.1016/j.rineng.2024.102935.
- 28. Müssigmann, B., Gracht, H. & Hartmann, E. (2020) Blockchain technology in logistics and supply chain management ‒ A bibliometric literature review from 2016 to January 2020. IEEE Transactions on Engineering Management 67 (4), pp. 988‒1007, doi: 10.1109/TEM.2020.2980733.
- 29. Nayeri, S., Sazvar, Z. & Heydari, J. (2023) Towards a responsive supply chain based on the Industry 5.0 dimensions: A novel decision-making method. Expert Systems with Applications 213 (Part C), 119267, doi: 10.1016/j. eswa.2022.119267.
- 30. Nezianya, M.C., Adebayo, A.O. & Ezeliora, P. (2024) A critical review of machine learning applications in supply chain risk management. World Journal of Advanced Research and Reviews 23 (3), pp. 1554–1567, doi: 10.30574/ wjarr.2024.23.3.2760.
- 31. Park, K.-T., Lee, J.-Y., Park, M.-W., Park, Y.H., Lee, J.-Y. & Choi, Y.-H. (2024) Models and P4R asset description for digital twin-based advanced planning and scheduling using cyber-physical integration for resilient production operation. Journal of Manufacturing Systems 77, pp. 127‒153, doi: 10.1016/j.jmsy.2024.08.030.
- 32. Rajak, B.K., Chatterjee, S., Upadhyay, A. & Rani, M.V. (2024) Contextual difference in the drivers of internet-of-things-adoption in road, rail, and maritime freight transport. IEEE Transactions on Engineering Management 71, pp. 11125‒11137, doi: 10.1109/TEM.2024.3413353.
- 33. Rao, S.K. & Prasad, R. (2018) Impact of 5G technologies on Industry 4.0. Wireless Personal Communications 100, pp. 145–159, doi: 10.1007/s11277-018-5615-7.
- 34. Rejeb, A., Keogh, J.G. & Wamba, S.F. (2021) The potentials of augmented reality in supply chain management: a stateof-the-art review. Management Review Q 71, pp. 819–856, doi: 10.1007/s11301-020-00201-w.
- 35. Romero, D. & Stahre, J. (2021) Towards the resilient operator 5.0: The future of work in smart resilient manufacturing systems. Procedia CIRP 104, pp. 1089–1094, doi: 10.1016/j.procir.2021.11.183.
- 36. Saad, S.M., Bahadori, R. & Bhovar, C. (2024) Industry 4.0 and lean manufacturing – a systematic review of the state-of-the-art literature and key recommendations for future research. International Journal of Lean Six Sigma 15 (5), pp. 997-1024, doi: 10.1108/IJLSS-02-2022-0021.
- 37. Sah, B.P. & Shaikh, S. (2024) AI-driven IoT and blockchain integration in Industry 5.0: A systematic review of supply chain transformation. Innovatech Engineering Journal 1 (1), pp. 99–116, doi: dx.doi.org/10.2139/ssrn.5076952.
- 38. Saraswat, J.K. & Choudhari, S. (2025) Integrating big data and cloud computing into the existing system and performance impact: A case study in manufacturing. Technological Forecasting and Social Change 210, 123883, doi: 10.1016/j.techfore.2024.123883.
- 39. Shafique, M.N., Yeo, S.F. & Tan, C.L. (2024) Roles of top management support and compatibility in big data predictive analytics for supply chain collaboration and supply chain performance. Technological Forecasting and Social Change 199, 123074, doi: 10.1016/j.techfore.2023.123074.
- 40. Shamsuzzoha, A. & Pelkonen, S. (2025) A robotic process automation model for order-handling optimization in supply chain management. Supply Chain Analytics 9, 100102, doi: 10.1016/j.sca.2025.100102.
- 41. Sheikh, R.A., Ahmed, I. & Faqihi, A.Y.A. (2024) Global perspectives on navigating Industry 5.0 knowledge: Achieving resilience, sustainability, and human-centric innovation in manufacturing. Journal of the Knowledge Economy, doi: 10.1007/s13132-024-02498-4.
- 42. Sornprom, N. (2024) Role of cloud computing & artificial intelligence in the logistics & supply chain industry. Transactions on Engineering and Computing Sciences, 12 (6), pp. 1–13, doi: 10.14738/tecs.126.17867.
- 43. Wang, X., Zhu, X. & Anwar, M.K. (2024) Evaluating the role of AI and empirical models for predicting regional economic growth and transportation dynamics: An application of advanced AI approaches. International Journal of Transportation Science and Technology, doi: 10.1016/j. ijtst.2024.08.007.
- 44. Yadav, A., Garg, R.K. & Sachdeva, A. (2024) Artificial intelligence applications for information management in sustainable supply chain management: A systematic review and future research agenda. International Journal of Information Management Data Insights 4 (2), 100292, doi: 10.1016/j. jjimei.2024.100292.
- 45. Yang, J., Liu, Y. & Morgan, P.L. (2024) Human–machine interaction towards Industry 5.0: Human-centric smart manufacturing. Digital Engineering 2, 100013, doi: 10.1016/j. dte.2024.100013.
- 46. Zaidi, S.A.H., Khan, S.A. & Chaabane, A. (2024) Unlocking the potential of digital twins in supply chains: A systematic review. Supply Chain Analytics 7, 100075, doi: 10.1016/j.sca.2024.100075.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki i promocja sportu (2025).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-c43d1ecf-d0f2-41aa-8247-ef28c6ce456e
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