Araştırma Makalesi
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Yapay Zeka Sistemlerinin Küresel Tedarik Zinciri Entegrasyonu Ve Performansına Etkileri Üzerine Bir Araştırma

Yıl 2025, Cilt: 2 Sayı: 1, 86 - 99, 30.04.2025

Öz

Bu çalışma, yapay zekâ sistemlerinin küresel tedarik zinciri entegrasyonu ve performansı üzerindeki etkilerini incelemektedir. Literatür taramasına dayalı olarak gerçekleştirilen araştırmada, yapay zekânın tedarik zincirinde karar alma süreçlerini hızlandırdığı, operasyonel verimliliği artırdığı ve sürdürülebilirlik hedeflerine katkı sağladığı ortaya konmuştur. Yapay zekâ teknolojileri, talep tahmini, stok yönetimi, rota optimizasyonu ve risk analizi gibi kritik alanlarda şirketlere maliyet tasarrufu ve rekabet avantajı sunmaktadır. Bununla birlikte, yapay zekânın etkili bir şekilde uygulanabilmesi; veri kalitesi, teknolojik altyapı, insan faktörü ve organizasyonel adaptasyon gibi unsurların bir arada ele alınmasını gerektirmektedir. Çalışmanın bulguları, yapay zekâ destekli sistemlerin yalnızca operasyonel iyileştirmeler sağlamakla kalmadığını, aynı zamanda küresel tedarik zincirlerinin şeffaflığını, esnekliğini ve dayanıklılığını artırarak stratejik bir dönüşüm sunduğunu göstermektedir. Elde edilen sonuçlar, tedarik zinciri yönetiminde teknolojik adaptasyonu teşvik eden ve sürdürülebilirlik hedeflerini önceliklendiren stratejilerin önemini vurgulamaktadır. Bu bağlamda, hem akademik literatüre hem de iş dünyasına önemli katkılar sunmayı hedeflemektedir.

Kaynakça

  • Al-Surmi, A., Bashiri, M., & Koliousis, I. (2022). AI based decision making: combining strategies to improve operational performance. International Journal of Production Research, 60(14), 4464-4486.
  • Baryannis, G., Dani, S., & Antoniou, G. (2019). Predictive analytics and artificial intelligence in supply chain management: Review and implications for the future. Computers & Industrial Engineering, 137, 106024.
  • Basnet, C. (2013). Internal supply chain integration. International Journal of Production Management and Engineering, 1(1), 1-10.
  • Chen, H., Daugherty, P. J., & Landry, T. D. (2009). Supply chain process integration: A theoretical framework. Journal of Business Logistics, 25(2), 193–218.
  • Choi, T. M., Wallace, S. W., & Wang, Y. (2018). Big Data analytics in operations management. Production and Operations Management, 27(10), 1868–1889.
  • Christopher, M. (2016). Logistics & supply chain management
  • Danach, K., El Dirani, A., & Rkein, H. (2024). Revolutionizing Supply Chain Management with AI: A Path to Efficiency and Sustainability. IEEE Access.
  • Denyer, D., & Tranfield, D. (2009). Producing a systematic review. In D. A. Buchanan & A. Bryman (Eds.), The Sage handbook of organizational research methods (pp. 671–689). Sage.
  • Dubey, R., Gunasekaran, A., Childe, S. J., Bryde, D. J., & Giannakis, M. (2019). Big data and predictive analytics and manufacturing performance: Integrating institutional theory, resource-based view and big data culture. British Journal of Management, 32(4), 1094–1112.
  • Drezner, D. W. (2005). Globalization, harmonization, and competition: The different pathways to policy convergence. Journal of European Public Policy, 12(5), 841–859.
  • Ernst, D. (2002). Global production networks and the changing geography of innovation systems. Economics of Innovation and New Technology, 11(6), 497–523.
  • Gereffi, G., & Lee, J. (2012). Why the world suddenly cares about global supply chains. Journal of Supply Chain Management, 48(3), 24–32.
  • 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.
  • Ivanov, D., Sethi, S. P., Dolgui, A., & Tsipoulanidis, A. (2018). A survey on control theory applications to operational systems, supply chain management, and logistics. Journal of Intelligent Manufacturing, 29(6), 1285–1300.
  • Jones, J. (2025). The Role of Artificial Intelligence in Enhancing Demand Forecasting and Supply Chain Decision-Making.
  • J. Scott, W. R. (2013). Institutions and organizations: Ideas, interests, and identities. Sage Publications.
  • Lee, H. L. (2002). Aligning supply chain strategies with product uncertainties. California Management Review, 44(3), 105–119.
  • Mah, P. (2022). Analysis of Artificial Intelligence and Natural Language Processing Significance as Expert Systems Support for E-Health Using Pre- Train Deep Learning Models
  • Manyika, J., & Bughin, J. (2018). The promise and challenge of the age of artificial intelligence. McKinsey Global Institute.
  • Mneimneh, F., Ghazzawi, H., & Ramakrishna, S. (2023). Review study of energy efficiency measures in favor of reducing carbon footprint of electricity and power, buildings, and transportation. Circular Economy and Sustainability, 3(1), 447-474.
  • Paramesha, M., Rane, N. L., & Rane, J. (2024). Big data analytics, artificial intelligence, machine learning, internet of things, and blockchain for enhanced business intelligence. Partners Universal Multidisciplinary Research Journal, 1(2), 110-133.
  • Power, D. (2005). Supply chain management integration and implementation: A literature review. Supply Chain Management: An International Journal, 10(4), 252–263.
  • Roscoe, S., Skipworth, H., Aktas, E., & Habib, F. (2022). Managing supply chain uncertainty: The role of digital technologies. Supply Chain Management: An International Journal, 27(4), 363–377.
  • Shabbir, J., & Anwer, T. (2018). Artificial Intelligence and its Role in Near Future. Journal of Latex Class Files, 14(8), 1–11.
  • Singh, N., & Adhikari, D. (2023). AI and IoT: A future perspective on inventory management. International Journal for Research in Applied Science and Engineering Technology, 11(11), 2753-2757.
  • Subhash, B. (2022). Causes and consequences of global supply chain disruptions: A theoretical analysis. IUP Journal of Supply Chain Management, 19(4), 7-24.

Artificial Intelligence Systems and Their Impact On Global Supply Chain Integration and Performance

Yıl 2025, Cilt: 2 Sayı: 1, 86 - 99, 30.04.2025

Öz

This study examines the impact of artificial intelligence (AI) systems on global supply chain integration and performance. Based on a literature review, the research reveals that AI accelerates decision-making processes, enhances operational efficiency, and contributes to sustainability goals in supply chain management. AI technologies offer cost savings and competitive advantages to companies in critical areas such as demand forecasting, inventory management, route optimization, and risk analysis. However, the effective implementation of AI requires the simultaneous consideration of data quality, technological infrastructure, human factors, and organizational adaptation. The findings indicate that AI-supported systems not only provide operational improvements but also offer a strategic transformation by increasing the transparency, flexibility, and resilience of global supply chains. The results emphasize the importance of strategies that promote technological adaptation in supply chain management and prioritize sustainability goals. In this context, the study aims to contribute significantly to both academic literature and the business world.

Kaynakça

  • Al-Surmi, A., Bashiri, M., & Koliousis, I. (2022). AI based decision making: combining strategies to improve operational performance. International Journal of Production Research, 60(14), 4464-4486.
  • Baryannis, G., Dani, S., & Antoniou, G. (2019). Predictive analytics and artificial intelligence in supply chain management: Review and implications for the future. Computers & Industrial Engineering, 137, 106024.
  • Basnet, C. (2013). Internal supply chain integration. International Journal of Production Management and Engineering, 1(1), 1-10.
  • Chen, H., Daugherty, P. J., & Landry, T. D. (2009). Supply chain process integration: A theoretical framework. Journal of Business Logistics, 25(2), 193–218.
  • Choi, T. M., Wallace, S. W., & Wang, Y. (2018). Big Data analytics in operations management. Production and Operations Management, 27(10), 1868–1889.
  • Christopher, M. (2016). Logistics & supply chain management
  • Danach, K., El Dirani, A., & Rkein, H. (2024). Revolutionizing Supply Chain Management with AI: A Path to Efficiency and Sustainability. IEEE Access.
  • Denyer, D., & Tranfield, D. (2009). Producing a systematic review. In D. A. Buchanan & A. Bryman (Eds.), The Sage handbook of organizational research methods (pp. 671–689). Sage.
  • Dubey, R., Gunasekaran, A., Childe, S. J., Bryde, D. J., & Giannakis, M. (2019). Big data and predictive analytics and manufacturing performance: Integrating institutional theory, resource-based view and big data culture. British Journal of Management, 32(4), 1094–1112.
  • Drezner, D. W. (2005). Globalization, harmonization, and competition: The different pathways to policy convergence. Journal of European Public Policy, 12(5), 841–859.
  • Ernst, D. (2002). Global production networks and the changing geography of innovation systems. Economics of Innovation and New Technology, 11(6), 497–523.
  • Gereffi, G., & Lee, J. (2012). Why the world suddenly cares about global supply chains. Journal of Supply Chain Management, 48(3), 24–32.
  • 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.
  • Ivanov, D., Sethi, S. P., Dolgui, A., & Tsipoulanidis, A. (2018). A survey on control theory applications to operational systems, supply chain management, and logistics. Journal of Intelligent Manufacturing, 29(6), 1285–1300.
  • Jones, J. (2025). The Role of Artificial Intelligence in Enhancing Demand Forecasting and Supply Chain Decision-Making.
  • J. Scott, W. R. (2013). Institutions and organizations: Ideas, interests, and identities. Sage Publications.
  • Lee, H. L. (2002). Aligning supply chain strategies with product uncertainties. California Management Review, 44(3), 105–119.
  • Mah, P. (2022). Analysis of Artificial Intelligence and Natural Language Processing Significance as Expert Systems Support for E-Health Using Pre- Train Deep Learning Models
  • Manyika, J., & Bughin, J. (2018). The promise and challenge of the age of artificial intelligence. McKinsey Global Institute.
  • Mneimneh, F., Ghazzawi, H., & Ramakrishna, S. (2023). Review study of energy efficiency measures in favor of reducing carbon footprint of electricity and power, buildings, and transportation. Circular Economy and Sustainability, 3(1), 447-474.
  • Paramesha, M., Rane, N. L., & Rane, J. (2024). Big data analytics, artificial intelligence, machine learning, internet of things, and blockchain for enhanced business intelligence. Partners Universal Multidisciplinary Research Journal, 1(2), 110-133.
  • Power, D. (2005). Supply chain management integration and implementation: A literature review. Supply Chain Management: An International Journal, 10(4), 252–263.
  • Roscoe, S., Skipworth, H., Aktas, E., & Habib, F. (2022). Managing supply chain uncertainty: The role of digital technologies. Supply Chain Management: An International Journal, 27(4), 363–377.
  • Shabbir, J., & Anwer, T. (2018). Artificial Intelligence and its Role in Near Future. Journal of Latex Class Files, 14(8), 1–11.
  • Singh, N., & Adhikari, D. (2023). AI and IoT: A future perspective on inventory management. International Journal for Research in Applied Science and Engineering Technology, 11(11), 2753-2757.
  • Subhash, B. (2022). Causes and consequences of global supply chain disruptions: A theoretical analysis. IUP Journal of Supply Chain Management, 19(4), 7-24.
Toplam 26 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Akıllı Hareketlilik, Lojistik, Tedarik Zinciri
Bölüm Araştırma Makaleleri
Yazarlar

Eda Nur Çelik

Yayımlanma Tarihi 30 Nisan 2025
Gönderilme Tarihi 2 Nisan 2025
Kabul Tarihi 29 Nisan 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 2 Sayı: 1

Kaynak Göster

APA Çelik, E. N. (2025). Yapay Zeka Sistemlerinin Küresel Tedarik Zinciri Entegrasyonu Ve Performansına Etkileri Üzerine Bir Araştırma. Ege Üniversitesi Ulaştırma Yönetimi Araştırmaları Dergisi, 2(1), 86-99.