Slavica Radosavljević – Academy of Technical and Art Applied Studies Belgrade, Department School of Information and Communication Technologies, Zdravka Čelara 16, 11000 Belgrade, Serbia
Biljana Grgurović – Academy of Technical and Art Applied Studies Belgrade, Department School of Information and Communication Technologies, Zdravka Čelara 16, 11000 Belgrade, Serbia
Jelena Milutinović – Academy of Technical and Art Applied Studies Belgrade, Department School of Information and Communication Technologies, Zdravka Čelara 16, 11000 Belgrade, Serbia
Stevan Veličković – Academy of Technical and Art Applied Studies Belgrade, Department School of Information and Communication Technologies, Zdravka Čelara 16, 11000 Belgrade, Serbia
Keywords:
Logistics;
Modern business process;
Technologies;
Sustainability;
Predictive analytics
Abstract: The evolution of logistics within modern business processes is a pivotal factor in achieving success and attaining a competitive edge. Leveraging available technologies and data opens up significant opportunities, primarily centered around more precise and efficient forecasting, planning, and execution of logistics operations. Adopting a proactive approach in the logistics process involves foreseeing customer needs, identifying avenues for enhancement, and clearly formulating solutions before customers articulate their requirements. This proactive stance is crucial for cultivating enduring customer relationships, amplifying sales, and elevating the overall customer experience.
Various facets of this transformation seamlessly intertwine and mutually reinforce one another, underscored by the potential advantages they offer businesses, including heightened visibility, cost savings, and operational efficiency. This paper investigates pivotal aspects, notably process automation, cutting-edge solutions, autonomous technologies, sustainability initiatives, collaborative efforts, and interconnection among diverse companies. Additionally, we explore the significance of predictive analytics coupled with adaptive strategies, emphasizing the imperative of adapting to emerging trends in logistics-based systems.

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8th International Scientific Conference – EMAN 2024 – Economics and Management: How to Cope With Disrupted Times, Rome, Italy, March 21, 2024, CONFERENCE PROCEEDINGS, published by: Association of Economists and Managers of the Balkans, Belgrade, Serbia; ISBN 978-86-80194-83-7, ISSN 2683-4510, DOI: https://doi.org/10.31410/EMAN.2024
Creative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution-Non-Commercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission.
REFERENCES
Adrodegari, F., Pashou, T., & Saccani, N. (2017). Business model innovation: Process and tools for service transformation of industrial firms, Procedia CIRP, 64, pp. 103–108. doi:10.1016/j.procir.2017.03.056.
Altana. (n.d.). https://altana.ai
Aytekin, A., Korucuk, S., Bedirhanoğlu, Ş. B., & Simic, V. (2024). Selecting the ideal sustainable green strategy for logistics companies using a T-spherical fuzzy-based methodology. Engineering Applications of Artificial Intelligence, 127, 107347. https://doi.org/10.1016/j. engappai.2023.107347
Bae, H.-S. (2024). The effects of trust and communication on flexibility and customer relationships between port logistics firms and shippers, The Asian Journal of Shipping and Logistics [Preprint]. doi:10.1016/j.ajsl.2024.02.004.
Baruffaldi, G., Accorsi, R., & Manzini, R. (2019). Warehouse management system customization and information availability in 3pl companies: A decision-support tool. Ind. Manag. Data Syst. 2019; 119: 251-273. 10.1108/IMDS-01-2018-0033
Beans.ai. (n.d.). https://www.beans.ai/
Bughin, J., Seong, J., Manyika, J., Chui, M., & Joshi, R. (2018). Notes from the AI frontier: Modeling the impact of AI on the world economy. McKinsey Global Institute 4.
Cuzzocrea, A., Leung, C. K., Deng, D., Mai, J. J., Jiang, F., & Fadda, E. (2020). A combined deep-learning and transfer-learning approach for supporting social influence prediction. Procedia Computer Science, 177, 170–177.
Ding, Y., Chen, K., Wei, X., & Yang, Y. (2022). A novel cost-management system for container terminals using a time-driven activity-based costing approach. Ocean & Coastal Management, 217, Article 106011.
DroppX. (n.d.). https://thehub.io/startups/droppx
Kulkarni, O., Dahan, M., & Montreuil, B. (2022). Resilient hyperconnected parcel delivery network design under disruption risks. International Journal of Production Economics.
Leyerer, M., Sonneberg, M.-O., Heumann, M., & Breitner, M. (2019). Decision support for sustainable and resilience-oriented urban parcel delivery. EURO Journal on Decision Processes, 7(3-4), 267-300. https://doi.org/10.1007/s40070-019-00105-5
Martinez, V., Bastl, M., Kingston, J., & Evans, E. (2010). Challenges in transforming manufacturing organisations into product-service providers. Journal of Manufacturing Technology Management 2010; 21(4): 449–469.
Melton, J. (2022). Global parcel volume to grow at 8.5% CAGR through 2027. September 26 Digital Commerce, 360.
Neely, A. (2008). Exploring the financial consequences of the servitization of manufacturing. Operations Management Research, 1(2), 103-118.
Oliva, R., & Kallenberg, R. (2003). Managing the transition from products to services. International journal of service industry management, 14(2), 160-172
Palkina, E. (2022). Transformation of business models of logistics and transportation companies in Digital Economy, Transportation Research Procedia, 63, pp. 2130–2137. doi:10.1016/j. trpro.2022.06.239.
Paulraj, A., Lado, A. A., & Chen, I. J. (2007). Inter‐organizational communication as a relational competency: Antecedents and performance outcomes in collaborative buyer-supplier relationships, Journal of Operations Management, 26(1), pp. 45–64. doi:10.1016/j. jom.2007.04.001.
Peneder, M. (2022). Digitization and the evolution of money as a social technology of account. J. Evol. Econ., 32, 175-203. 10.1007/s00191-021-00729-4
Pereira Marcilio Nogueira, G., Jos´e de Assis Rangel, J., Rossi Croce, P., & Almeida Peixoto, T. (2022). The environmental impact of fast delivery B2C e-commerce in outbound logistics operations: A simulation approach. Cleaner Logistics and Supply Chain, 5, Article 100070. https://doi.org/10.1016/j.clscn.2022.100070
Praveen, U., Farnaz, G., & Hatim, G. (2019). Inventory management and cost reduction of supply chain processes using AI based time-series forecasting and ANN modeling. Procedia Manufacturing, 38, 256–263.
Qin, J., Liu, Y., & Grosvenor, R. (2016). A Categorical Framework of Manufacturing for Industry 4.0 and Beyond. Procedia CIRP, 52, 173-178. https://doi.org/10.1016/j.procir.2016.08.005
Rosario, A. T., & Dias, J. C. (2023). How has data-driven marketing evolved: Challenges and opportunities with emerging technologies. International Journal of Information Management Data Insights, 3(2), Article 100203.
Rosen, R., von Wichert, G., Lo, G., & Bettenhausen, K. D. (2015). About The Importance of Autonomy and Digital Twins for the Future of Manufacturing. IFAC-PapersOnLine, 48(3), 567-572. https://doi.org/10.1016/j.ifacol.2015.06.141
Spadafora, J., Rodriguez, M., Sawhney, G., & Verckens, A. (2022). Pitney Bowes parcel shipping index. https://www.pitneybowes.com/us/shipping-index.html
Teng, J. T. C., Grover, V., & Fiedler, K. D. (1994). Business process reengineering: Charting a strategic path for the information age, California Management Review, 36(3), pp. 9–31. doi:10.2307/41165753.
UPS. (n.d.). https://www.ups.com/
Verma, S., Sharma, R., Deb, S., & Maitra, D. (2021). Artificial intelligence in marketing: Systematic review and future research direction. International Journal of Information Management Data Insights, 1(1)
Walmart. (n.d.). https://www.walmart.com/
Wang, X., Han, R., & Zheng, M. H. (2023). Competitive strategy and stock market liquidity: A natural language processing approach. Information Technology and Management.
Yaiprasert, C., & Hidayanto, A. N. (2024). AI-Powered Ensemble Machine Learning to optimize cost strategies in logistics business, International Journal of Information Management Data Insights, 4(1), p. 100209. doi:10.1016/j.jjimei.2023.100209.
Yaqub, M. Z., & Alsabban, A. (2023). Industry-4.0-enabled digital transformation: Prospects, Instruments, challenges, and implications for business strategies, Sustainability, 15(11), p. 8553. doi:10.3390/su15118553.
