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An Analysis and Evaluation of Artificial Intelligence Applications in Logistics and Procurement

Yıl 2025, Cilt: 10 Sayı: 2, 533 - 564, 28.12.2025
https://doi.org/10.30927/ijpf.1525662

Öz

The accelerating evolution of artificial intelligence (AI) technologies is precipitating transformative shifts within logistics and procurement, redefining operational paradigms across both public and private domains. The adoption of AI in finance, supply chain management, and procurement processes has gained remarkable traction, owing to its capacity to deliver heightened efficiency, enhanced accuracy, and improved cost-effectiveness. Yet, despite this momentum, integrative resources that systematically delineate the conceptual foundations, strategic advantages, and practical illustrations of AI within logistics and procurement remain scarce, though notable progress continues to emerge. This paper seeks to advance both scholarly discourse and professional practice by critically evaluating the implications of AI-driven process automation in logistics and procurement. Drawing upon industry-leading platforms such as Symphony and GEP SMART, complemented by sectoral reports and contemporary academic literature, the study develops comprehensive process maps that elucidate applied dimensions of AI integration. Beyond analytical insights, the paper proposes actionable and forward-looking recommendations specifically tailored to the Public Procurement Authority (Kamu İhale Kurumu, KİK) of the Republic of Türkiye, thereby offering a roadmap for embedding AI within procurement governance frameworks.

Kaynakça

  • Akben, İ., & İncenacar, T. (2018). Artificial intelligence in the supply chain management. II. Uluslararası Sosyal Bilimler Kongresi, 184–199. https://t.ly/yfvY
  • Allal-Chérif, O., Simón-Moya, V., & Cuenca Ballester, A. C. (2021). Intelligent purchasing: How artificial intelligence can target the purchasing function. Journal of Business Research, 124, 69–76. https://doi.org/10.1016/j.jbusres.2020.11.050
  • Alparslan, G., & Lule, S. (2022). What is financial inclusion? How can artificial intelligence be used? https://ai.org.tr/2022/08/26/finansal-kapsayicilik-nedir-yapay-zeka-nasil-kullanilabilir/
  • Al-Saba, T., & El-Amin, I. (1999). Artificial neural networks as applied to long-term demand forecasting. Artificial Intelligence in Engineering, 13, 189–197.
  • Aylak, B. L., Oral, O., & Yazıcı, K. (2021). Yapay zeka ve makine öğrenmesi tekniklerinin lojistik sektöründe kullanımı. El-Cezeri Journal of Science and Engineering, 8(1), 74–93. https://doi.org/10.31202/ecjse.776314
  • Baily, P., Farmer, D., & Jessop, D. (2005). Purchasing principles and management. Pearson Education Limited.
  • Becze, J. (2020). Volvo VISE (Bachelor’s thesis, Umeå University). DiVA. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-171730
  • Bhattacharya, A. (2018). Artificial intelligence (AI) & its impact on procurement. Zycus. https://www.zycus.com/blog/procurement-technology/artificial-intelligence-ai-its-impact-on-procurement.html
  • Bowersox, D. J., Calantone, R. J., & Rodrigues, A. M. (2011). Estimation of global logistics expenditures using neural networks. Journal of Business Logistics, 24(2), 1–14. https://doi.org/10.1002/j.2158-1592.2003.tb00044.x
  • Camacho, A., Bienvenu, M., & McIlraith, S. A. (2021). Towards a unified view of AI planning and reactive synthesis. Proceedings of the International Conference on Automated Planning and Scheduling, 29(1), 58–67. https://ojs.aaai.org/index.php/ICAPS/article/view/3460
  • Chopra, S. (2019). AI in supply & procurement. In Amity International Conference on Artificial Intelligence (AICAI) (pp. 308–316). IEEE. https://doi.org/10.1109/AICAI.2019.8701357
  • Cui, R., Li, M., & Zhang, S. (2020). AI and procurement. Manufacturing & Service Operations Management. SSRN. http://dx.doi.org/10.2139/ssrn.3570967
  • Cui, R., Li, M., & Zhang, S. (2021). AI and procurement. Manufacturing & Service Operations Management. Advance online publication. https://doi.org/10.1287/msom.2021.0989
  • Cuzzolin, F., Morelli, A., Cîrstea, B., & Sahakian, B. (2020). Knowing me, knowing you: Theory of mind in AI. Psychological Medicine, 50(7), 1057–1061. https://doi.org/10.1017/S0033291720000835
  • Das, D. (2020). Artificial intelligence and the future of procurement. Dragon Sourcing. https://www.dragonsourcing.com/artificial-intelligence-and-the-future-of-procurement/
  • Efendigil, T., Onut, S., & Kahraman, C. (2008). A decision support system for demand forecasting with artificial neural networks and neuro-fuzzy models: A comparative analysis. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2008.08.058
  • Fjelland, R. (2020). Why general artificial intelligence will not be realized. Humanities and Social Sciences Communications, 7, Article 10. https://doi.org/10.1057/s41599-020-0494-4
  • Humphreys, P., Huang, G., & McIvor, R. (2002). An expert system for evaluating the make or buy decision. Computers & Industrial Engineering, 42(2–4), 567–585.
  • ILSCompany. (2021). 7 successful strategies to improve your supply chain. https://ilscompany.tempurl.host/7-successful-strategies-to-improve-your-supply-chain/ Jeong, B., Jung, H., & Park, N. (2002). A computerized forecasting system using genetic algorithm in supply chain management. The Journal of Systems and Software, 60(3), 223–237.
  • Karlsson, F. (2020). The opportunities of applying artificial intelligence in strategic sourcing (Master’s thesis, KTH Royal Institute of Technology). http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-281306
  • Kruppenbacher, T. A. (1984). The application of artificial intelligence to contract management (Descriptive Note). U.S. Army Construction Engineering Research Laboratory. https://apps.dtic.mil/sti/citations/ADA146681
  • Lamberton, C., Brigo, D., & Hoy, D. (2017). Impact of robotics, RPA and AI on the insurance industry: Challenges and opportunities. Journal of Financial Perspectives, 4(1). https://ssrn.com/abstract=3079495
  • Lee, D., Huang, H.-Y., Lee, W.-S., & Liu, Y. (2020). Artificial intelligence implementation framework development for building energy saving. International Journal of Energy Research. https://doi.org/10.1002/er.5839
  • Lee, R. S. T. (2020). AI and self-consciousness. In Artificial intelligence in daily life (pp. [insert page range]). Springer. https://doi.org/10.1007/978-981-15-7695-9_13
  • Mackey, T. K., & Cuomo, R. E. (2020). An interdisciplinary review of digital technologies to facilitate anti-corruption, transparency and accountability in medicines procurement. Global Health Action, 13(sup1). https://doi.org/10.1080/16549716.2019.1695241
  • Min, H. (2010). Artificial intelligence in supply chain management: Theory and applications. International Journal of Logistics Research and Applications, 13(1), 13–39. https://doi.org/10.1080/13675560902736537
  • Nadimpalli, M. (2017). Artificial intelligence: Risks and benefits. International Journal of Innovative Research in Science, Engineering and Technology, 6(6).
  • Paul, S., & Azeem, A. (2011). An artificial neural network model for optimization of finished goods inventory. International Journal of Industrial Engineering Computations, 2(2), 431–438.
  • Port of Rotterdam. (2020). The vast potential of AI – Port and maritime working group sets to work. https://www.portofrotterdam.com/en/news-and-press-releases/vast-potential-ai-port-and-maritime-working-group-sets-work
  • Simfoni. (2021). AI in procurement. https://simfoni.com/ai-in-procurement/
  • Stanfill, C., & Waltz, D. (1986). Toward memory-based reasoning. Communications of the ACM, 29(12), 1213–1228. https://doi.org/10.1145/7902.7906
  • Sustrova, T. (2016). A suitable artificial intelligence model for inventory level optimization. Trends Economics and Management, 25, 48–55. https://doi.org/10.13164/trends.2016.25.48
  • Tavana, M., Fallahpour, A., Caprio, D. D., & Santos-Arteaga, F. J. (2016). A hybrid intelligent fuzzy predictive model with simulation for supplier evaluation and selection. Expert Systems with Applications, 61, 129–144.
  • Tbtech. (2021). Amazon’s AI logistics warehouses. https://tbtech.co/featured-news/amazons-ai-logistics-warehouses/
  • Triana, M. (2012). Demand forecast for short life cycle products (Master’s thesis, Pontificia Universidad Javeriana). http://vitela.javerianacali.edu.co/handle/11522/3472
  • UNIMAR. (2021). Lojistiğin kaderini değiştiren teknoloji: Yapay zeka. https://globelinkunimar.com/lojistigin-kaderini-degistiren-teknoloji-yapay-zeka/
  • Wiecek, P. (2016). Intelligent approach to inventory control in logistics under uncertainty conditions. Transportation Research Procedia, 18, 164–171.

An Analysis and Evaluation of Artificial Intelligence Applications in Logistics and Procurement

Yıl 2025, Cilt: 10 Sayı: 2, 533 - 564, 28.12.2025
https://doi.org/10.30927/ijpf.1525662

Öz

The accelerating evolution of artificial intelligence (AI) technologies is precipitating transformative shifts within logistics and procurement, redefining operational paradigms across both public and private domains. The adoption of AI in finance, supply chain management, and procurement processes has gained remarkable traction, owing to its capacity to deliver heightened efficiency, enhanced accuracy, and improved cost-effectiveness. Yet, despite this momentum, integrative resources that systematically delineate the conceptual foundations, strategic advantages, and practical illustrations of AI within logistics and procurement remain scarce, though notable progress continues to emerge. This paper seeks to advance both scholarly discourse and professional practice by critically evaluating the implications of AI-driven process automation in logistics and procurement. Drawing upon industry-leading platforms such as Symphony and GEP SMART, complemented by sectoral reports and contemporary academic literature, the study develops comprehensive process maps that elucidate applied dimensions of AI integration. Beyond analytical insights, the paper proposes actionable and forward-looking recommendations specifically tailored to the Public Procurement Authority (Kamu İhale Kurumu, KİK) of the Republic of Türkiye, thereby offering a roadmap for embedding AI within procurement governance frameworks.

Kaynakça

  • Akben, İ., & İncenacar, T. (2018). Artificial intelligence in the supply chain management. II. Uluslararası Sosyal Bilimler Kongresi, 184–199. https://t.ly/yfvY
  • Allal-Chérif, O., Simón-Moya, V., & Cuenca Ballester, A. C. (2021). Intelligent purchasing: How artificial intelligence can target the purchasing function. Journal of Business Research, 124, 69–76. https://doi.org/10.1016/j.jbusres.2020.11.050
  • Alparslan, G., & Lule, S. (2022). What is financial inclusion? How can artificial intelligence be used? https://ai.org.tr/2022/08/26/finansal-kapsayicilik-nedir-yapay-zeka-nasil-kullanilabilir/
  • Al-Saba, T., & El-Amin, I. (1999). Artificial neural networks as applied to long-term demand forecasting. Artificial Intelligence in Engineering, 13, 189–197.
  • Aylak, B. L., Oral, O., & Yazıcı, K. (2021). Yapay zeka ve makine öğrenmesi tekniklerinin lojistik sektöründe kullanımı. El-Cezeri Journal of Science and Engineering, 8(1), 74–93. https://doi.org/10.31202/ecjse.776314
  • Baily, P., Farmer, D., & Jessop, D. (2005). Purchasing principles and management. Pearson Education Limited.
  • Becze, J. (2020). Volvo VISE (Bachelor’s thesis, Umeå University). DiVA. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-171730
  • Bhattacharya, A. (2018). Artificial intelligence (AI) & its impact on procurement. Zycus. https://www.zycus.com/blog/procurement-technology/artificial-intelligence-ai-its-impact-on-procurement.html
  • Bowersox, D. J., Calantone, R. J., & Rodrigues, A. M. (2011). Estimation of global logistics expenditures using neural networks. Journal of Business Logistics, 24(2), 1–14. https://doi.org/10.1002/j.2158-1592.2003.tb00044.x
  • Camacho, A., Bienvenu, M., & McIlraith, S. A. (2021). Towards a unified view of AI planning and reactive synthesis. Proceedings of the International Conference on Automated Planning and Scheduling, 29(1), 58–67. https://ojs.aaai.org/index.php/ICAPS/article/view/3460
  • Chopra, S. (2019). AI in supply & procurement. In Amity International Conference on Artificial Intelligence (AICAI) (pp. 308–316). IEEE. https://doi.org/10.1109/AICAI.2019.8701357
  • Cui, R., Li, M., & Zhang, S. (2020). AI and procurement. Manufacturing & Service Operations Management. SSRN. http://dx.doi.org/10.2139/ssrn.3570967
  • Cui, R., Li, M., & Zhang, S. (2021). AI and procurement. Manufacturing & Service Operations Management. Advance online publication. https://doi.org/10.1287/msom.2021.0989
  • Cuzzolin, F., Morelli, A., Cîrstea, B., & Sahakian, B. (2020). Knowing me, knowing you: Theory of mind in AI. Psychological Medicine, 50(7), 1057–1061. https://doi.org/10.1017/S0033291720000835
  • Das, D. (2020). Artificial intelligence and the future of procurement. Dragon Sourcing. https://www.dragonsourcing.com/artificial-intelligence-and-the-future-of-procurement/
  • Efendigil, T., Onut, S., & Kahraman, C. (2008). A decision support system for demand forecasting with artificial neural networks and neuro-fuzzy models: A comparative analysis. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2008.08.058
  • Fjelland, R. (2020). Why general artificial intelligence will not be realized. Humanities and Social Sciences Communications, 7, Article 10. https://doi.org/10.1057/s41599-020-0494-4
  • Humphreys, P., Huang, G., & McIvor, R. (2002). An expert system for evaluating the make or buy decision. Computers & Industrial Engineering, 42(2–4), 567–585.
  • ILSCompany. (2021). 7 successful strategies to improve your supply chain. https://ilscompany.tempurl.host/7-successful-strategies-to-improve-your-supply-chain/ Jeong, B., Jung, H., & Park, N. (2002). A computerized forecasting system using genetic algorithm in supply chain management. The Journal of Systems and Software, 60(3), 223–237.
  • Karlsson, F. (2020). The opportunities of applying artificial intelligence in strategic sourcing (Master’s thesis, KTH Royal Institute of Technology). http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-281306
  • Kruppenbacher, T. A. (1984). The application of artificial intelligence to contract management (Descriptive Note). U.S. Army Construction Engineering Research Laboratory. https://apps.dtic.mil/sti/citations/ADA146681
  • Lamberton, C., Brigo, D., & Hoy, D. (2017). Impact of robotics, RPA and AI on the insurance industry: Challenges and opportunities. Journal of Financial Perspectives, 4(1). https://ssrn.com/abstract=3079495
  • Lee, D., Huang, H.-Y., Lee, W.-S., & Liu, Y. (2020). Artificial intelligence implementation framework development for building energy saving. International Journal of Energy Research. https://doi.org/10.1002/er.5839
  • Lee, R. S. T. (2020). AI and self-consciousness. In Artificial intelligence in daily life (pp. [insert page range]). Springer. https://doi.org/10.1007/978-981-15-7695-9_13
  • Mackey, T. K., & Cuomo, R. E. (2020). An interdisciplinary review of digital technologies to facilitate anti-corruption, transparency and accountability in medicines procurement. Global Health Action, 13(sup1). https://doi.org/10.1080/16549716.2019.1695241
  • Min, H. (2010). Artificial intelligence in supply chain management: Theory and applications. International Journal of Logistics Research and Applications, 13(1), 13–39. https://doi.org/10.1080/13675560902736537
  • Nadimpalli, M. (2017). Artificial intelligence: Risks and benefits. International Journal of Innovative Research in Science, Engineering and Technology, 6(6).
  • Paul, S., & Azeem, A. (2011). An artificial neural network model for optimization of finished goods inventory. International Journal of Industrial Engineering Computations, 2(2), 431–438.
  • Port of Rotterdam. (2020). The vast potential of AI – Port and maritime working group sets to work. https://www.portofrotterdam.com/en/news-and-press-releases/vast-potential-ai-port-and-maritime-working-group-sets-work
  • Simfoni. (2021). AI in procurement. https://simfoni.com/ai-in-procurement/
  • Stanfill, C., & Waltz, D. (1986). Toward memory-based reasoning. Communications of the ACM, 29(12), 1213–1228. https://doi.org/10.1145/7902.7906
  • Sustrova, T. (2016). A suitable artificial intelligence model for inventory level optimization. Trends Economics and Management, 25, 48–55. https://doi.org/10.13164/trends.2016.25.48
  • Tavana, M., Fallahpour, A., Caprio, D. D., & Santos-Arteaga, F. J. (2016). A hybrid intelligent fuzzy predictive model with simulation for supplier evaluation and selection. Expert Systems with Applications, 61, 129–144.
  • Tbtech. (2021). Amazon’s AI logistics warehouses. https://tbtech.co/featured-news/amazons-ai-logistics-warehouses/
  • Triana, M. (2012). Demand forecast for short life cycle products (Master’s thesis, Pontificia Universidad Javeriana). http://vitela.javerianacali.edu.co/handle/11522/3472
  • UNIMAR. (2021). Lojistiğin kaderini değiştiren teknoloji: Yapay zeka. https://globelinkunimar.com/lojistigin-kaderini-degistiren-teknoloji-yapay-zeka/
  • Wiecek, P. (2016). Intelligent approach to inventory control in logistics under uncertainty conditions. Transportation Research Procedia, 18, 164–171.
Toplam 37 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Kamu Ekonomisi, Bütçe ve Mali Planlama
Bölüm Derleme
Yazarlar

Ahmet Efe 0000-0002-2691-7517

Gönderilme Tarihi 31 Temmuz 2024
Kabul Tarihi 8 Ekim 2025
Yayımlanma Tarihi 28 Aralık 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 10 Sayı: 2

Kaynak Göster

APA Efe, A. (2025). An Analysis and Evaluation of Artificial Intelligence Applications in Logistics and Procurement. International Journal of Public Finance, 10(2), 533-564. https://doi.org/10.30927/ijpf.1525662

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