E-Ticarette Dijital Dönüşüm: Yapay Zekâ Destekli Lojistik Modellerinin Geleceği

Öz

E-ticaret sektöründe dijital dönüşüm süreci, lojistik ve tedarik zinciri yönetiminde yapay zekâ ve büyük veri analitiği kullanımını zorunlu hale getirmiştir. Bu çalışma, yapay zekâ destekli lojistik modellerinin e-ticaret sektöründeki uygulamalarını ve sağladığı avantajları literatür taraması ile incelemektedir. Yapılan araştırmada, yapay zekâ teknolojilerinin talep tahminleme, stok yönetimi, rota optimizasyonu, akıllı depo sistemleri, drone ve otonom araçlarla teslimat gibi birçok lojistik süreci optimize ettiği ve maliyetleri düşürdüğü tespit edilmiştir. Ayrıca, müşteri memnuniyetini artırmada akıllı dolaplar gibi alternatif teslimat çözümlerinin etkili olduğu belirlenmiştir. Bununla birlikte, yapay zekâ sistemlerinin yüksek yatırım maliyetleri, veri güvenliği riskleri, yasal regülasyonlar ve işgücü üzerindeki olumsuz etkileri gibi bazı zorluklar da bulunmaktadır. Sonuç olarak, e-ticaret sektöründeki işletmelerin dijital dönüşüm stratejilerini geliştirerek, yapay zekâ teknolojilerini proaktif bir şekilde benimsemesi ve bu süreçte karşılaşabilecekleri zorlukları aşmaya yönelik adımlar atması gerektiği önerilmiştir.

Anahtar Kelimeler

• E-ticaret
• yapay zekâ
• dijital dönüşüm
• lojistik yönetimi
• son mil teslimat.

Digital Transformation in E-Commerce: The Future of Artificial Intelligence-Supported Logistics Models

Öz

The digital transformation process in the e-commerce industry has made the use of artificial intelligence (AI) and big data analytics essential in logistics and supply chain management. This study examines applications and benefits of AI-supported logistics models in e-commerce through a comprehensive literature review. The findings indicate that AI technologies significantly optimize logistics processes such as demand forecasting, inventory management, route optimization, intelligent warehouse systems, and deliveries using drones and autonomous vehicles, thereby reducing operational costs. Additionally, alternative delivery solutions like smart lockers have been identified as effective in enhancing customer satisfaction. Despite these benefits, the implementation of AI-driven logistics faces challenges such as high initial investment costs, data security risks, regulatory barriers, and adverse impacts on the workforce. Consequently, it is recommended that e-commerce businesses proactively embrace AI technologies by developing robust digital transformation strategies and taking measures to address potential implementation challenges.

Anahtar Kelimeler

• E-commerce
• artificial intelligence
• digital transformation
• logistics management
• last mile delivery.

Kaynakça

1. Abrahamsson, M., Brege, S., & Norrman, A. (1998). Distribution channel reengineering – Organisational separation of distribution and sales functions in the European market. Transport Logistics, 1(4), 237–249.
2. Addo-Tenkorang, R., & Helo, P. T. (2016). Big data applications in operations/supply-chain management: A literature review. Computers & Industrial Engineering, 101, 528–543. https://doi.org/10.1016/j.cie.2016.09.023
3. Agarwal, A., & Alathur, S. (2023). Metaverse revolution and the digital transformation: Intersectional analysis of Industry 5.0. Transforming Government: People, Process and Policy, 17(4), 688–707.
4. Akter, S., Wamba, S. F., Gunasekaran, A., Dubey, R., & Childe, S. J. (2016). How to improve firm performance using big data analytics capability and business strategy alignment? International Journal of Production Economics, 182, 113–131. https://doi.org/10.1016/j.ijpe.2016.08.018
5. Alcácer, V., & Cruz-Machado, V. (2019). Scanning the industry 4.0: A literature review on technologies for manufacturing systems. Engineering Science and Technology, an International Journal, 22(3), 899–919. https://doi.org/10.1016/j.jestch.2019.01.006
6. Ardito, L., Petruzzelli, A. M., Panniello, U., & Garavelli, A. C. (2019). Towards Industry 4.0: Mapping digital technologies for supply chain management-marketing integration. Business Process Management Journal, 25(2), 323–346.
7. Bal, H. Ç., & Erkan, Ç. (2019). Industry 4.0 and competitiveness. Procedia Computer Science, 158, 625–631. https://doi.org/10.1016/j.procs.2019.09.096
8. Bernon, M., Cullen, J., & Gorst, J. (2016). Online retail returns management: Integration within an omni-channel distribution context. International Journal of Physical Distribution and Logistics Management, 46(6/7), 584–605.

9. Bharadwaj, A., El Sawy, O. A., Pavlou, P. A., & Venkatraman, N. (2013). Digital business strategy: Toward a next generation of insights. MIS Quarterly, 37(2), 471–482. https://www.jstor.org/stable/43825919
10. Boone, T., Ganeshan, R., Jain, A., & Sanders, N. R. (2019). Forecasting sales in the supply chain: Consumer analytics in the big data era. International Journal of Forecasting, 35(1), 170–180. https://doi.org/10.1016/j.ijforecast.2018.09.003
11. Brynjolfsson, E., & McAfee, A. (2017). The business of artificial intelligence. Harvard Business Review.
12. Bughin, J., Hazan, E., Ramaswamy, S., Chui, M., Allas, T., Dahlström, P., ... & Trench, M. (2018). Artificial intelligence: The next digital frontier? McKinsey Global Institute.
13. Büyüközkan, G., & Göçer, F. (2018). Digital supply chain: Literature review and a proposed framework for future research. Computers in Industry, 97, 157–177. https://doi.org/10.1016/j.compind.2018.02.010
14. Cao, L. (2014). Business model transformation in moving to a cross-channel retail strategy: A case study. International Journal of Electronic Commerce, 18(4), 69–96.
15. Cattaruzza, D., Absi, N., & Feillet, D. (2017). Vehicle routing problems with multiple trips. 4OR, 15(3), 223–259.
16. Chandna, V., & Salimath, M. S. (2018). Peer-to-peer selling in online platforms: A salient business model for virtual entrepreneurship. Journal of Business Research, 84, 162–174. https://doi.org/10.1016/j.jbusres.2017.11.019
17. Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165–1188. https://doi.org/10.2307/41703503
18. Chi, N. T. K., & Nam, V. H. (2023). The impact of drone delivery innovation on customer intention: An empirical study in Vietnam. VNU University of Economics and Business, 3(2), 102–102. https://doi.org/10.57110/vnujeb.v3i2.153
19. Choi, T. M., Wallace, S. W., & Wang, Y. (2018). Big data analytics in operations management. Production and Operations Management, 27(10), 1868–1883. https://doi.org/10.1111/poms.12838
20. Chopra, S., & Meindl, P. (2019). Supply chain management: Strategy, planning, and operation (7th ed.). Pearson Education.
21. Christopher, M. (2016). Logistics and supply chain management (5th ed.). Pearson.
22. Colla, E., & Lapoule, P. (2012). E-commerce: Exploring the critical success factors. International Journal of Retail & Distribution Management, 40(11), 842–864.
23. Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108–116.
24. Davenport, T. H., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48(1), 24–42.
25. Eğilmez, G. (2024). Sürdürülebilir akıllı lojistik. Duvar Yayınları.
26. Enriquea, D. V., Druczkoskia, J. C. M., Limaa, T. M., & Charrua-Santos, F. (2021). Advantages and difficulties of implementing Industry 4.0 technologies for labor flexibility. Procedia Computer Science, 181, 347–352.
27. Figliozzi, M. A. (2020). Autonomous delivery robots and their potential impacts on urban freight energy consumption and emissions. Transportation Research Part D: Transport and Environment, 86, 102460. https://doi.org/10.1016/j.trpro.2020.03.159
28. Fosso Wamba, S., Dubey, R., Gunasekaran, A., & Akter, S. (2021). The performance effects of big data analytics and supply chain ambidexterity: The moderating effect of environmental dynamism. International Journal of Production Economics, 222, 107498. https://doi.org/10.1016/j.ijpe.2019.09.019
29. Frazelle, E. H. (2016). World-class warehousing and material handling. McGraw-Hill Education.
30. Gantz, J., & Reinsel, D. (2012). The digital universe in 2020: Big data, bigger digital shadows, and biggest growth in the Far East. IDC.
31. Genç, E., & Tunalı, İ. (2022). Endüstri 4.0 ve lojistik 4.0 kapsamında akıllı depo sistemleri. Avrasya Sosyal ve Ekonomi Araştırmaları Dergisi, 9(4), 194–215.
32. Gevaers, R., Van de Voorde, E., & Vanelslander, T. (2014). Cost modelling and simulation of last-mile characteristics in an innovative B2C supply chain environment with implications on urban areas and cities. Procedia-Social and Behavioral Sciences, 125, 398–411. https://doi.org/10.1016/j.sbspro.2014.01.1483
33. GilPress. (2013). A very short history of big data. Forbes. https://www.forbes.com/sites/gilpress/2013/05/09/a-very-short-history-of-big-data/ (Erişim Tarihi: 5 Şubat 2025).
34. Goodchild, A., & Toy, J. (2018). Delivery by drone: An evaluation of unmanned aerial vehicle technology in reducing CO2 emissions in the delivery service industry. Transportation Research Part D: Transport and Environment, 61, 58–67. https://doi.org/10.1016/j.trd.2017.02.017
35. Gu, Y., & Dong, S. (2016). Logistics cost management from the supply chain perspective. Journal of Service Science and Management, 9, 229–232. https://doi.org/10.4236/jssm.2016.93028
36. 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. https://doi.org/10.1016/j.compind.2017.04.002
37. Hübner, A., Holzapfel, A., & Kuhn, H. (2015). Operations management in multi-channel retailing: An exploratory study. Operations Management Research, 8(3/4), 84–100.
38. Hübner, A., Kuhn, H., & Wollenburg, J. (2016). Last mile fulfilment and distribution in omni-channel grocery retailing. International Journal of Retail & Distribution Management, 44(3), 228–247. https://doi.org/10.1108/IJRDM-11-2014-0154
39. Ishfaq, R., Defee, C. C., Gibson, B. J., & Raja, U. (2016). Realignment of the physical distribution process in omni-channel fulfillment. International Journal of Physical Distribution and Logistics Management, 46(6/7), 543–561.
40. Ivanov, D., & Dolgui, A. (2020). Viability of intertwined supply networks: Extending the supply chain resilience angles towards survivability. International Journal of Production Research, 58(10), 2904–2915. https://doi.org/10.1080/00207543.2020.1750727
41. Ivanov, D., Dolgui, A., & Sokolov, B. (2019). The impact of digital technology and Industry 4.0 on supply chain risk analytics. International Journal of Production Research, 57(3), 829–846. https://doi.org/10.1080/00207543.2018.1488086
42. Joerss, M., Schröder, J., Neuhaus, F., Klink, C., & Mann, F. (2016). Parcel delivery: The future of last mile. McKinsey & Company Report, 1–32.
43. Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255–260. https://doi.org/10.1126/science.aaa8415
44. Kaisler, S., Armour, F., Espinosa, J. A., & Money, W. (2013). Big data: Issues and challenges moving forward. In 46th Hawaii International Conference on System Sciences (pp. 995–1004).
45. Kamble, S. S., Gunasekaran, A., & Sharma, R. (2020). Modeling the blockchain enabled traceability in agriculture supply chain. International Journal of Information Management, 52, 101967.
46. Kembro, J., & Norrman, A. (2019). Exploring trends, implications and challenges for logistics information systems in omni-channels: Swedish retailers’ perception. International Journal of Retail & Distribution Management, 47(4), 384–411.
47. Kostrzewski, M., Abdelatty, Y., Eliwa, A., & Nader, M. (2022). Analysis of modern vs. conventional development technologies in transportation—The case study of a last-mile delivery process. Sensors, 22(24), 9858. https://doi.org/10.3390/s22249858
48. Kshetri, N. (2018). Blockchain’s roles in meeting key supply chain management objectives. International Journal of Information Management, 39, 80–89. https://doi.org/10.1016/j.ijinfomgt.2017.12.005
49. Laudon, K. C., & Traver, C. G. (2021). E-commerce: Business, technology, society. Pearson.
50. LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., & Kruschwitz, N. (2010). Big data, analytics and the path from insights to value. MIT Sloan Management Review, 52(2), 21–32.
51. Lim, S. F. W. T., Jin, X., & Srai, J. S. (2018). Consumer-driven e-commerce and last-mile logistics. International Journal of Physical Distribution & Logistics Management, 48(3), 308–332. https://doi.org/10.1108/IJPDLM-02-2017-0081
52. Loshin, D. (2013). Big data analytics: From strategic planning to enterprise integration with tools, techniques, NoSQL, and graph. Morgan Kaufmann.
53. Mahar, S., Wright, P. D., Bretthauer, K. M., & Hill, R. P. (2014). Optimizing marketer costs and consumer benefits across ‘clicks’ and ‘bricks’. Journal of the Academy of Marketing Science, 42(6), 619–641.
54. Mangiaracina, R., Perego, A., Seghezzi, A., & Tumino, A. (2019). Innovative solutions to increase last-mile delivery efficiency. International Journal of Physical Distribution & Logistics Management, 49(9), 901–935.
55. McAfee, A., & Brynjolfsson, E. (2012). Big data: The management revolution. Harvard Business Review, 90(10), 60–68.
56. McKinsey Global Institute. (2018). Analytics comes of age. https://www.mckinsey.com/~/media/McKinsey/Business%20Functions/McKinsey%20Analytics/Our%20Insights/Analytics%20comes%20of%20age/Analytics-comes-of-age.ashx (Erişim Tarihi: 10 Şubat 2025).
57. Moroz, M., & Polkowski, Z. (2016). The last mile issue and urban logistics: Choosing parcel machines in the context of the ecological attitudes of the Y generation consumers purchasing online. Transportation Research Procedia, 16, 378–393. https://doi.org/10.1016/j.trpro.2016.11.036
58. Murray, C. C., & Chu, A. G. (2015). Optimization of drone-assisted parcel delivery. Transportation Research Part C: Emerging Technologies, 54, 86–109. https://doi.org/10.1016/j.trc.2015.03.005
59. Narkhede, G. B., Pasi, B. N., Rajhans, N., & Kulkarni, A. (2024). Industry 5.0 and sustainable manufacturing: A systematic literature review. Benchmarking: An International Journal.
60. Nguyen, T., Zhou, L., Spiegler, V., Ieromonachou, P., & Lin, Y. (2020). Big data analytics in supply chain management: A state-of-the-art literature review. Computers & Operations Research, 119, 104898. https://doi.org/10.1016/j.cor.2017.07.004
61. Obermeyer, Z., & Emanuel, E. J. (2016). Predicting the future—big data, machine learning, and clinical medicine. New England Journal of Medicine, 375(13), 1216–1219. https://doi.org/10.1056/NEJMp1606181
62. Pacchini, A. P. T., Lucato, W. C., Facchini, F., & Mummolo, G. (2019). The degree of readiness for the implementation of Industry 4.0. Computers in Industry, 113, 103125. https://doi.org/10.1016/j.compind.2019.103125
63. Ranieri, L., Digiesi, S., Silvestri, B., & Roccotelli, M. (2018). A review of last-mile logistics innovations in an externalities cost reduction vision. Sustainability, 10(3), 782. https://doi.org/10.3390/su10030782
64. Riahi, Y., Saikouk, T., Gunasekaran, A., & Badraoui, I. (2021). Artificial intelligence applications in supply chain: A descriptive bibliometric analysis and future research directions. Expert Systems with Applications, 173, 114702. https://doi.org/10.1016/j.eswa.2021.114702
65. Russom, P. (2011). Big data analytics. TDWI Best Practices Report, 4th Quarter, 1–35.
66. Sanders, N. R. (2016). How to use big data to drive your supply chain. California Management Review, 58(3), 26–48. https://doi.org/10.1525/cmr.2016.58.3.26
67. Sarkis, J. (2017). Green supply chain management: The role of logistics service providers. Transportation Research Part E: Logistics and Transportation Review, 98, 39–50. https://doi.org/10.4324/9781315233000
68. Savelsbergh, M., & Van Woensel, T. (2016). 50th anniversary invited article—City logistics: Challenges and opportunities. Transportation Science, 50(2), 579–590. https://doi.org/10.1287/trsc.2016.0675
69. Sharma, R., Kamble, S. S., & Gunasekaran, A. (2020). Big GIS analytics framework for agriculture supply chains: A literature review identifying the current trends and future perspectives. Computers and Electronics in Agriculture, 155, 103–120. https://doi.org/10.1016/j.compag.2018.10.001
70. Sternberg, H., & Andersson, M. (2014). Decentralized intelligence in freight transport—A critical review. Computers in Industry, 65(2), 306–313. https://doi.org/10.1016/j.compind.2013.11.011
71. 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. https://doi.org/10.1016/j.cie.2017.11.017
72. Tunji-Olayeni, P., Aigbavboa, C., Oke, A., & Chukwu, N. (2024). Research trends in industry 5.0 and its application in the construction industry. Technological Sustainability, 3(1), 1–23.
73. Turban, E., Outland, J., King, D., Lee, J. K., Liang, T. P., & Turban, D. C. (2018). Electronic commerce payment systems. Electronic Commerce 2018: A Managerial and Social Networks Perspective, 457–499.
74. Vakulenko, Y., Hellström, D., & Hjort, K. (2018). What's in the parcel locker? Exploring customer value in e-commerce last-mile delivery. Journal of Business Research, 88, 421–427. https://doi.org/10.1016/j.jbusres.2017.11.033
75. Verhoef, P. C., Broekhuizen, T., Bart, Y., Bhattacharya, A., Dong, J. Q., Fabian, N., & Haenlein, M. (2021). Digital transformation: A multidisciplinary reflection and research agenda. Journal of Business Research, 122, 889–901. https://doi.org/10.1016/j.jbusres.2019.09.022
76. Wang, G., Gunasekaran, A., Ngai, E. W., & Papadopoulos, T. (2016). Big data analytics in logistics and supply chain management: Certain investigations for research and applications. International Journal of Production Economics, 176, 98–110. https://doi.org/10.1016/j.ijpe.2016.03.014
77. Wang, X., & Disney, S. M. (2016). The bullwhip effect: Progress, trends and directions. European Journal of Operational Research, 250(3), 691–701. https://doi.org/10.1016/j.ejor.2015.07.022
78. Wang, X., Zhan, L., Ruan, J., & Zhang, J. (2018). How to choose “last mile” delivery modes for e-fulfillment. Mathematical Problems in Engineering, 2018, Article ID 8437649. https://doi.org/10.1155/2014/417129
79. Wang, Y., Singgih, M., Wang, J., & Rit, M. (2019a). Making sense of blockchain technology: How will it transform supply chains?. International Journal of Production Economics, 211, 221–236. https://doi.org/10.1016/j.ijpe.2019.02.002
80. Wang, Y., Han, J. H., & Beynon-Davies, P. (2019b). Understanding blockchain technology for future supply chains: A systematic literature review and research agenda. Supply Chain Management: An International Journal, 24(1), 62–84.
81. White, T. (2012). Hadoop: The definitive guide. O'Reilly Media.
82. Winkelhaus, S., & Grosse, E. H. (2020). Logistics 4.0: A systematic review towards a new logistics system. International Journal of Production Research, 58(1), 18–43.
83. Witkowski, K. (2017). Internet of Things, big data, Industry 4.0 – Innovative solutions in logistics and supply chains management. Procedia Engineering, 182, 763–769. https://doi.org/10.1016/j.proeng.2017.03.197
84. Wollenburg, J., Hübner, A., Kuhn, H., & Trautrims, A. (2018). From bricks-and-mortar to bricks-and-clicks: Logistics networks in omni-channel grocery retailing. International Journal of Physical Distribution & Logistics Management, 48(4), 415–438.
85. Zakir, J., Seymour, T., & Berg, K. (2015). Big data analytics. Issues in Information Systems, 16(2), 81–90.
86. Zhang, Y. (2019, October). The application of artificial intelligence in logistics and express delivery. In Journal of Physics: Conference Series (Vol. 1325, No. 1, p. 012085). IOP Publishing. https://doi.org/10.1088/1742-6596/1325/1/012085
87. 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. https://doi.org/10.1016/j.cie.2016.07.013
88. Zhong, R. Y., Xu, X., Klotz, E., & Newman, S. T. (2017). Intelligent manufacturing in the context of Industry 4.0: A review. Engineering, 3(5), 616–630. https://doi.org/10.1016/J.ENG.2017.05.015
89. Zhou, L., Chong, A. Y. L., & Ngai, E. W. (2015). Supply chain management in the era of the internet of things. International Journal of Production Economics, 159, 1–3. http://dx.doi.org/10.1016/j.ijpe.2014.11.014