Digitalization in Logistics Operations and Industry 4.0: Understanding the Linkages with Buzzwords

  • Metehan Feridun SorkunEmail author
Part of the Contributions to Management Science book series (MANAGEMENT SC.)


The new industrial revolution, Industry 4.0, requires digital transformation in all business operations including those of logistics. The digitalization in logistics operations, such as transportation, warehousing, inventory planning, sourcing, and return can provide firms high levels of flexibility and efficiency that are key to competitiveness in the era of Industry 4.0. In this regard, many buzzwords (technologies) are discussed in the discourses of Industry 4.0, emphasizing their key importance for the successful digitalization of logistics operations. However, the lack of clear understanding on these buzzwords and their interrelations is a barrier to firms’ determination of a clear road map for the digitalization process. For this reason, this study aims to initially introduce the Industry 4.0 enabling technologies (buzzwords), expected to be widely used in logistics operations in the immediate future, and then reveals the linkages between these technologies. To this end, this study applies the fuzzy-total interpretative structure modelling on the Industry 4.0 enabling technologies, which are big data analytics, internet of things, artificial intelligence, cloud technology, 3D printing, augmented reality, 5G connection, and autonomous vehicles. The results show that most Industry 4.0 enabling technologies are interdependent, but to different degrees. These results provide guidance on which technologies firms should primarily focus on to achieve digital transformation in logistics operations.


  1. Aktepe, Ç., & Yaşar Saatçıoğlu, Ö. (2017). Cloud computing adoption in logistics firms in Turkey: An exploratory study. Ordu University Journal of Social Science Research, 7(1), 9–20.Google Scholar
  2. Andersson, J., & Jonsson, P. (2018). Big data in spare parts supply chains: The potential of using product-in-use data in aftermarket demand planning. International Journal of Physical Distribution and Logistics Management, 48(5), 524–544.CrossRefGoogle Scholar
  3. Arunachalam, D., Kumar, N., & Kawalek, J. P. (2018). Understanding big data analytics capabilities in supply chain management: Unravelling the issues, challenges and implications for practice. Transportation Research Part E: Logistics and Transportation Review, 114, 416–436.CrossRefGoogle Scholar
  4. Bechtsis, D., Tsolakis, N., Vlachos, D., & Srai, J. S. (2018). Intelligent autonomous vehicles in digital supply chains: A framework for integrating innovations towards sustainable value networks. Journal of Cleaner Production, 181, 60–71.CrossRefGoogle Scholar
  5. Ben-Daya, M., Hassini, E., & Bahroun, Z. (2017). Internet of things and supply chain management: A literature review. International Journal of Production Research, 57(15–16), 4719–4742.Google Scholar
  6. Bowcott, O. (2017, December 14). Laws for safe use of driverless cars to be ready by 2021. Retrieved January 05, 2019, from
  7. Brinch, M., Stentoft, J., Jensen, J. K., & Rajkumar, C. (2018). Practitioners understanding of big data and its applications in supply chain management. International Journal of Logistics Management, 29(2), 555–574.CrossRefGoogle Scholar
  8. Brynjolfsson, E., Rock, D., & Syverson, C. (2018). Artificial intelligence and the modern productivity paradox: A clash of expectations and statistics. In The economics of artificial intelligence: An agenda. Chicago: University of Chicago Press.Google Scholar
  9. Büyüközkan, G., & Göçer, F. (2018). Digital supply chain: Literature review and a proposed framework for future research. Computers in Industry, 97, 157–177.CrossRefGoogle Scholar
  10. Chase, C. W. (2016). Next generation demand management: People, process, analytics, and technology. New York: Wiley.CrossRefGoogle Scholar
  11. Chen, D. Q., Preston, D. S., & Swink, M. (2015). How the use of big data analytics affects value creation in supply chain management. Journal of Management Information Systems, 32(4), 4–39.CrossRefGoogle Scholar
  12. Chou, T. Y., & Liang, G. S. (2001). Application of a fuzzy multi-criteria decision-making model for shipping company performance evaluation. Maritime Policy & Management, 28(4), 375–392.CrossRefGoogle Scholar
  13. Cirulis, A., & Ginters, E. (2013). Augmented reality in logistics. Procedia Computer Science, 26, 14–20.CrossRefGoogle Scholar
  14. Daniluk, D., & Holtkamp, B. (2015). Logistics mall—A cloud platform for logistics. In Cloud Computing for Logistics (pp. 13–27). Cham: Springer.Google Scholar
  15. Delfmann, W., Ten Hompel, M., Kersten, W., Schmidt, T., & Stölzle, W. (2018). Logistics as a science: Central research questions in the era of the fourth industrial revolution. Logistics Research, 11(9), 1–13.Google Scholar
  16. DHL. (2014). Self-driving vehicles in logistics: A DHL perspective on implications and use cases for the logistics industry. Troisdorf: DHL GSI.Google Scholar
  17. DHL. (2016). 3D printing and the future of supply chains: A DHL perspective on the state of 3D printing and implications for logistics. Troisdorf: DHL GSI.Google Scholar
  18. Douaioui, K., Fri, M., & Mabroukki, C. (2018, April). The interaction between industry 4.0 and smart logistics: Concepts and perspectives. In 2018 international colloquium on logistics and supply chain management (LOGISTIQUA) (pp. 128–132). IEEE.Google Scholar
  19. Ericsson. (2016). Digitalizing port operations with 5G. Retrieved January 04, 2019,
  20. Erol, S., Schumacher, A., & Sihn, W. (2016). Strategic guidance towards Industry 4.0–a three-stage process model. International Conference on Competitive Manufacturing, 9(1), 495–501.Google Scholar
  21. Fan, T., Tao, F., Deng, S., & Li, S. (2015). Impact of RFID technology on supply chain decisions with inventory inaccuracies. International Journal of Production Economics, 159, 117–125.CrossRefGoogle Scholar
  22. Flämig, H. (2016). Autonomous vehicles and autonomous driving in freight transport. In M. Maurer, J. Gerdes, B. Lenz, & H. Winner (Eds.), Autonomous driving. Berlin, Heidelberg: Springer.Google Scholar
  23. Georgakopoulos, D., Jayaraman, P. P., Fazia, M., Villari, M., & Ranjan, R. (2016). Internet of things and edge cloud computing roadmap for manufacturing. IEEE Cloud Computing, 3(4), 66–73.CrossRefGoogle Scholar
  24. Gesing, B., Peterson, S. J., & Michelsen, D. (2018). Artificial intelligence in logistics. A collaborative report by DHL and IBM on implications and use cases for the logistics industry. Troisdorf: DHL GSI.Google Scholar
  25. Glockner, H., Jannek, K., Johannes, M., & Theis, B. (2014). Augmented reality in logistics: Changing the way we see logistics – A DHL perspective. Troisdorf: DHL GSI.Google Scholar
  26. Gomez, M., Grand, S., & Gatziu Grivas, S. (2015). Digitalisation in logistics and the role of cloud computing: How cloud computing will change the game. Logistics Innovation Technologie.
  27. Govindan, K., Cheng, T. C. E., Mishra, N., & Shukla, N. (2018). Big data analytics and application for logistics and supply chain management. Transportation Research Part E: Logistics and Transportation Review, 114, 343–349.CrossRefGoogle Scholar
  28. Govindan, K., Palaniappan, M., Zhu, Q., & Kannan, D. (2012). Analysis of third party reverse logistics provider using interpretive structural modeling. International Journal of Production Economics, 140(1), 204–211.CrossRefGoogle Scholar
  29. Gravili, G., Benvenuto, M., Avram, A., & Viola, C. (2018). The influence of the digital divide on big data generation within supply chain management. International Journal of Logistics Management, 29(2), 592–628.CrossRefGoogle Scholar
  30. Gunasekaran, A., Papadopoulos, T., Dubey, R., Wamba, S. F., Childe, S. J., Hazen, B., et al. (2017). Big data and predictive analytics for supply chain and organizational performance. Journal of Business Research, 70, 308–317.CrossRefGoogle Scholar
  31. Gupta, S., Kumar, S., Singh, S. K., Foropon, C., & Chandra, C. (2018). Role of cloud ERP on the performance of an organization: Contingent resource based view perspective. International Journal of Logistics Management, 29(2), 659–675.CrossRefGoogle Scholar
  32. Hannan, M. A., Akhtar, M., Begum, R. A., Basri, H., Hussain, A., & Scavino, E. (2018). Capacitated vehicle-routing problem model for scheduled solid waste collection and route optimization using PSO algorithm. Waste Management, 71, 31–41.CrossRefGoogle Scholar
  33. Hoehle, H., Aloysius, J. A., Chan, F., & Venkatesh, V. (2018). Customers’ tolerance for validation in omnichannel retail stores: Enabling logistics and supply chain analytics. International Journal of Logistics Management, 29(2), 704–722.CrossRefGoogle Scholar
  34. Hofmann, E. (2017). Big data and supply chain decisions: The impact of volume, variety and velocity properties on the bullwhip effect. International Journal of Production Research, 55(17), 5108–5126.CrossRefGoogle Scholar
  35. Hofmann, E., & Rüsch, M. (2017). Industry 4.0 and the current status as well as future prospects on logistics. Computers in Industry, 89, 23–34.CrossRefGoogle Scholar
  36. Hopkins, J., & Hawking, P. (2018). Big data analytics and IoT in logistics: A case study. International Journal of Logistics Management, 29(2), 575–591.CrossRefGoogle Scholar
  37. Jain, R., Singh, A. R., Yadav, H. C., & Mishra, P. K. (2014). Using data mining synergies for evaluating criteria at pre-qualification stage of supplier selection. Journal of Intelligent Manufacturing, 25(1), 165–175.CrossRefGoogle Scholar
  38. James, J. Q., & Lam, A. Y. (2018). Autonomous vehicle logistic system: Joint routing and charging strategy. IEEE Transactions on Intelligent Transportation Systems, 19(7), 2175–2187.CrossRefGoogle Scholar
  39. Jeschke, S., Brecher, C., Meisen, T., Özdemir, D., & Eschert, T. (2017). Industrial internet of things and cyber manufacturing systems. In Industrial internet of things (pp. 3–19). Cham: Springer.CrossRefGoogle Scholar
  40. Jung, J. U., & Kim, H. S. (2014). Deployment of cloud computing in logistics industry. Journal of digital convergence, 12(2), 163–171.CrossRefGoogle Scholar
  41. Khatwani, G., Singh, S. P., Trivedi, A., & Chauhan, A. (2015). Fuzzy-TISM: A fuzzy extension of TISM for group decision making. Global Journal of Flexible Systems Management, 16(1), 97–112.CrossRefGoogle Scholar
  42. Lamba, K., & Singh, S. P. (2018). Modeling big data enablers for operations and supply chain management. International Journal of Logistics Management, 29(2), 629–658.CrossRefGoogle Scholar
  43. Li, G., Hou, Y., & Wu, A. (2017). Fourth industrial revolution: Technological drivers, impacts and coping methods. Chinese Geographical Science, 27(4), 626–637.CrossRefGoogle Scholar
  44. Li, X., Wang, Y., & Chen, X. (2012). Cold chain logistics system based on cloud computing. Concurrency and Computation: Practice and Experience, 24(17), 2138–2150.CrossRefGoogle Scholar
  45. Lu, Y. (2017). Industry 4.0: A survey on technologies, applications and open research issues. Journal of Industrial Information Integration, 6, 1–10.CrossRefGoogle Scholar
  46. Merlino, M., & Sproģe, I. (2017). The augmented supply chain. Procedia Engineering, 178, 308–318.CrossRefGoogle Scholar
  47. Mishra, N., & Singh, A. (2018). Use of twitter data for waste minimisation in beef supply chain. Annals of Operations Research, 270(1–2), 337–359.CrossRefGoogle Scholar
  48. Mohr, S., & Khan, O. (2015). 3D printing and its disruptive impacts on supply chains of the future. Technology Innovation Management Review, 5(11), 20–25.CrossRefGoogle Scholar
  49. Morrar, R., Arman, H., & Mousa, S. (2017). The fourth industrial revolution (industry 4.0): A social innovation perspective. Technology Innovation Management Review, 7(11), 12–20.CrossRefGoogle Scholar
  50. Nauck, D. D. (2003). Fuzzy data analysis with NEFCLASS. International Journal of Approximate Reasoning, 32(2–3), 103–130.CrossRefGoogle Scholar
  51. Nguyen, T., Zhou, L., Spiegler, V., Ieromonachou, P., & Lin, Y. (2018). Big data analytics in supply chain management: A state-of-the-art literature review. Computers & Operations Research, 98, 254–264.CrossRefGoogle Scholar
  52. Olson, D. L. (2015). A review of supply chain data mining publications. Journal of Supply Chain Management Science.
  53. Opricovic, S., & Tzeng, G. H. (2003). Defuzzification within a multicriteria decision model. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 11(05), 635–652.CrossRefGoogle Scholar
  54. Paelke, V. (2014, September). Augmented reality in the smart factory: Supporting workers in an industry 4.0. Environment. In 2014 IEEE emerging technology and factory automation (ETFA) (pp. 1–4). IEEE.Google Scholar
  55. Pan, Y. (2016). Heading toward artificial intelligence 2.0. Engineering, 2(4), 409–413.CrossRefGoogle Scholar
  56. Parada, R., Melià-Seguí, J., & Pous, R. (2018). Anomaly detection using RFID-based information management in an IoT context. Journal of Organizational and End User Computing (JOEUC), 30(3), 1–23.CrossRefGoogle Scholar
  57. Piccarozzi, M., Aquilani, B., & Gatti, C. (2018). Industry 4.0 in management studies: A systematic literature review. Sustainability, 10(10), 3821.CrossRefGoogle Scholar
  58. Que, S., Chen, J., Chen, B., & Jiang, H. (2016). The application of 5G Technology in logistics information acquisition. In DEStech Transactions on Computer Science and Engineering, International Conference on Electronic Information Technology and Intellectualization (ICEITI 2016) (pp. 512–517). ISBN: 978-1-60595-364-9. CrossRefGoogle Scholar
  59. Ramanathan, R., Philpott, E., Duan, Y., & Cao, G. (2017). Adoption of business analytics and impact on performance: A qualitative study in retail. Production Planning & Control, 28(11–12), 985–998.CrossRefGoogle Scholar
  60. Rao, S. K., & Prasad, R. (2018). Impact of 5G technologies on industry 4.0. Wireless Personal Communications, 100(1), 145–159.CrossRefGoogle Scholar
  61. Rogers, H., Baricz, N., & Pawar, K. S. (2016). 3D printing services: Classification, supply chain implications and research agenda. International Journal of Physical Distribution & Logistics Management, 46(10), 886–907.CrossRefGoogle Scholar
  62. Rüßmann, M., Lorenz, M., Gerbert, P., Waldner, M., Justus, J., Engel, P., et al. (2015). Industry 4.0: The future of productivity and growth in manufacturing industries. Boston Consulting Group.
  63. Sanders, N. R. (2016). How to use big data to drive your supply chain. California Management Review, 58(3), 26–48.CrossRefGoogle Scholar
  64. Sasson, A., & Johnson, J. C. (2016). The 3D printing order: Variability, supercenters and supply chain reconfigurations. International Journal of Physical Distribution & Logistics Management, 46(1), 82–94.CrossRefGoogle Scholar
  65. Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V. (2017). Critical analysis of big data challenges and analytical methods. Journal of Business Research, 70, 263–286.CrossRefGoogle Scholar
  66. Sorkun, M. F. (2018a). Improving the effectiveness of solid waste treatment plants via integrated system approach: A case study on Manisa. Celal Bayar Üniversitesi Sosyal Bilimler Dergisi, 16(4), 239–268.Google Scholar
  67. Sorkun, M. F. (2018b). The hierarchy of motivations turning manufacturers’ attention to reverse logistics. Ege Akademik Bakış Dergisi, 18(2), 243–259.Google Scholar
  68. Sorkun, M. F., & Onay, M. (2016). Ürün modülerliğinin ters lojistik süreçleri üzerinden tedarik zinciri stratejilerine etkisi. Sosyal Ve Beşeri Bilimler Dergisi, 8(2), 41–57.Google Scholar
  69. Srinivasan, R., & Swink, M. (2018). An investigation of visibility and flexibility as complements to supply chain analytics: An organizational information processing theory perspective. Production and Operations Management, 27(10), 1849–1867.CrossRefGoogle Scholar
  70. Tadejko, P. (2015). Application of internet of things in logistics – Current challenges. Economics and Management, 7(4), 54–64.Google Scholar
  71. Tan, K. H., Zhan, Y., Ji, G., Ye, F., & Chang, C. (2015). Harvesting big data to enhance supply chain innovation capabilities: An analytic infrastructure based on deduction graph. International Journal of Production Economics, 165, 223–233.CrossRefGoogle Scholar
  72. Thornton, P. H., & Ocasio, W. (2008). Institutional logics. In R. Greenwood, C. Oliver, R. Suddaby, & K. Sahlin (Eds.), The Sage handbook of organizational institutionalism (pp. 99–129). London: Sage.CrossRefGoogle Scholar
  73. Tiwari, S., Wee, H. M., & Daryanto, Y. (2018). Big data analytics in supply chain management between 2010 and 2016: Insights to industries. Computers & Industrial Engineering, 115, 319–330.CrossRefGoogle Scholar
  74. Togard, A. (2017, August 18). The impact of 5G: How will 5G affect supply chain & logistics? Retrieved 04 January, 2019, from
  75. Uden, L., & He, W. (2017). How the internet of things can help knowledge management: A case study from the automotive domain. Journal of Knowledge Management, 21(1), 57–70.CrossRefGoogle Scholar
  76. Vanderroost, M., Ragaert, P., Verwaeren, J., De Meulenaer, B., De Baets, B., & Devlieghere, F. (2017). The digitization of a food package’s life cycle: Existing and emerging computer systems in the logistics and post-logistics phase. Computers in Industry, 87, 15–30.CrossRefGoogle Scholar
  77. Wang, G., Gunasekaran, A., & Ngai, E. W. (2018). Distribution network design with big data: Model and analysis. Annals of Operations Research, 270(1–2), 539–551.CrossRefGoogle Scholar
  78. Wang, S., Wan, J., Zhang, D., Li, D., & Zhang, C. (2016). Towards smart factory for industry 4.0: A self-organized multi-agent system with big data based feedback and coordination. Computer Networks, 101, 158–168.CrossRefGoogle Scholar
  79. Warfield, J. N. (1974). Toward interpretation of complex structural models. IEEE Transactions on Systems, Man, and Cybernetics, 5, 405–417.CrossRefGoogle Scholar
  80. Xu, L. D., & Duan, L. (2018). Big data for cyber physical systems in industry 4.0: A survey. Enterprise Information Systems, 13(2), 148–169.CrossRefGoogle Scholar
  81. Yu, W., Chavez, R., Jacobs, M. A., & Feng, M. (2018). Data-driven supply chain capabilities and performance: A resource-based view. Transportation Research Part E: Logistics and Transportation Review, 114, 371–385.CrossRefGoogle Scholar
  82. Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353.CrossRefGoogle Scholar
  83. Zhong, R. Y., Newman, S. T., Huang, G. Q., & Lan, S. (2016). Big data for supply chain management in the service and manufacturing sectors: Challenges, opportunities, and future perspectives. Computers & Industrial Engineering, 101, 572–591.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Business AdministrationIzmir University of EconomicsBalçovaTurkey

Personalised recommendations