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ENTEGRASYON MİMARİSİ KULLANAN TEDARİK ZİNCİRİNİN MAKİNE ÖĞRENMESİ İLE DESTEKLENMESİ

Yıl 2026, Cilt: 29 Sayı: 1, 124 - 147, 03.03.2026

Fatih Soygazi , Haktan Akdağ , Kevser Öztürk

https://doi.org/10.17780/ksujes.1773821
https://izlik.org/JA83FW99SZ

Öz

Tedarik zincirinde dijitalleşme, çağımızın zorunluluğu haline gelmiştir. Bir tedarik zinciri takip sistemi geliştirilirken, siparişin verilmesi, mevcut stok durumunun tespiti, stok seviyelerindeki hammadde veya yarı mamül malzemelerin alım ya da tüketim süreçlerinin izlenmesi için sürekli olarak malzemelerin veri giriş ve çıkışlarını analiz eden otonom, entegre ve akıllı sistemlerin geliştirilmesine ihtiyaç vardır. Bu doğrultuda çalışma kapsamında, farklı paydaşlarda bulunan ERP (Enterprise Resource Planning - Kurumsal Kaynak Planlaması) sistemlerindeki verinin ortaklaşa kullanılabilmesi için bir mimari önerilmiş ve bu entegrasyon mimarisinden elde edilen veriden yararlanılarak hammadde miktarının tahminlenebilmesi üzerinde durulmuştur. Örnek senaryoda, yüzden fazla ekmek satış noktası olan bir fırın tarafından kullanılan un miktarının tespitine dair çalışmalar gerçekleştirilmiştir. Bu çalışmalar, makine öğrenmesi yöntemleri ile regresyon analizi üzerine yürütülmüştür. Önerilen çalışmadaki hedef, un stoğunun an itibarıyla yeterli olup olmadığının tespitidir. Bu noktada, regresyon modeli kullanımıyla eldeki un miktarından yararlanarak sonraki gün stokta olacak un miktarının belirli bir hata payı ile tespit edilmesi hedeflenmektedir. Yapılan çalışmada, geleneksel makine öğrenmesi ve derin öğrenme yöntemleri kullanılmıştır. Elde edilen sonuçlar, XAI (Explainable Artificial Intelligence - Açıklanabilir Yapay Zekâ) yöntemleri kullanılarak yorumlanmıştır. Tüm bu süreç sonucunda R2 0,90 ve MAE (Mean Absolute Error - Ortalama Mutlak Hatası) değeri 39,25 olarak en iyi sonucu LightGBM algoritması vermiştir.

Anahtar Kelimeler

Tedarik zinciri Makine öğrenmesi Entegrasyon Regresyon

Kaynakça

  • Aamer, A., Eka Yani, L., & Alan Priyatna, Im. (2020). Data analytics in the supply chain management: Review of machine learning applications in demand forecasting. Operations and Supply Chain Management: An International Journal, 14(1), 1-13. http://doi.org/10.31387/oscm0440281
  • Acar, S. G., & Yılmaz, M. (2013). Matbaa İşletmeleri İçin Bir Malzeme İhtiyaç Planlama Yazılımı Geliştirme ve Uygulanması. Bilişim Teknolojileri Dergisi, 6(1), 23-32.
  • Akdağ, H., & Kocakoç, İ. D. (2023). TOPLAM KALİTE YÖNETİMİ, KAIZEN VE ENTEGRASYON SÜREÇLERİNİN BÜTÜNLEŞİK ANALİZİ. Nazilli İktisadi ve İdari Bilimler Fakültesi Dergisi, 4(2), 119-132. https://doi.org/10.59113/niibfd.1391223
  • Aljohani, A. (2023). Predictive analytics and machine learning for real-time supply chain risk mitigation and agility. Sustainability, 15(20), 15088. https://doi.org/10.3390/su152015088
  • Bagchi, P. K., Chun Ha, B., Skjoett-Larsen, T., & Boege Soerensen, L. (2005). Supply chain integration: A European survey. The international journal of logistics management, 16(2), 275-294. https://doi.org/10.1108/09574090510634557
  • Basana, S., Suprapto, W., Andreani, F., & Tarigan, Z. (2022). The impact of supply chain practice on green hotel performance through internal, upstream, and downstream integration. Uncertain Supply Chain Management, 10(1).
  • Botta-Genoulaz, V., & Millet, P.-A. (2005). A classification for better use of ERP systems. Computers in industry, 56(6), 573-587. https://doi.org/10.1016/j.compind.2005.02.007
  • Carbonneau, R., Laframboise, K., & Vahidov, R. (2008). Application of machine learning techniques for supply chain demand forecasting. European journal of operational research, 184(3), 1140-1154. https://doi.org/10.1016/j.ejor.2006.12.004
  • Cebeci, K., & Korçak, Ö. (2020). Design of an enterprise level architecture based on microservices. Bilişim Teknolojileri Dergisi, 13(4), 357-371. https://doi.org/10.17671/gazibtd.558392
  • Chen, H.-C., Lu, M.-J., Liu, C.-C., & Tsai, C.-H. (2012). A study of the performance improvement of bill of material document sign flow system. International Journal of Academic Research in Accounting, Finance and Management Sciences, 2(1), 65-79.
  • Condon, S. (2024). GenAI: Agentic Workflows with the Supply Chain Advisor. SAP Community. Geliş tarihi gönderen https://community.sap.com/t5/technology-blogs-by-sap/genai-agentic-workflows-with-the-supply-chain-advisor/ba-p/13700531
  • Davenport, T. H., & Brooks, J. D. (2004). Enterprise systems and the supply chain. Journal of Enterprise Information Management, 17(1), 8-19. https://doi.org/10.1108/09576050410510917
  • Davis, T. (1993). Effective supply chain management. Sloan management review, 34, 35-35. Doğar, A. (2006). Tedarik Zinciri Nde Stok Yönetimi. Geliş tarihi gönderen https://polen.itu.edu.tr/bitstreams/71d3843e-d65d-470d-a63f-4362c41f9100/download
  • Droge, C., Vickery, S. K., & Jacobs, M. A. (2012). Does supply chain integration mediate the relationships between product/process strategy and service performance? An empirical study. International Journal of Production Economics, 137(2), 250-262. https://doi.org/10.1016/j.ijpe.2012.02.005
  • Elufioye, O. A., Ike, C. U., Odeyemi, O., Usman, F. O., & Mhlongo, N. Z. (2024). Ai-Driven predictive analytics in agricultural supply chains: A review: assessing the benefits and challenges of ai in forecasting demand and optimizing supply in agriculture. Computer Science & IT Research Journal, 5(2), 473-497.
  • Frohlich, M. T., & Westbrook, R. (2001). Arcs of integration: An international study of supply chain strategies. Journal of operations management, 19(2), 185-200. https://doi.org/10.1016/S0272-6963(00)00055-3
  • Ghobakhloo, M. (2020). Industry 4.0, digitization, and opportunities for sustainability. Journal of cleaner production, 252, 119869. https://doi.org/10.1016/j.jclepro.2019.119869
  • Groenewald, C. A., Garg, A., & Yerasuri, S. S. (2024). Smart supply chain management optimization and risk mitigation with artificial intelligence. Naturalista campano, 28(1), 261-270.
  • Hazen, B. T., Boone, C. A., Ezell, J. D., & Jones-Farmer, L. A. (2014). Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications. International Journal of Production Economics, 154, 72-80. https://doi.org/10.1016/j.ijpe.2014.04.018
  • Islam, S., Amin, S. H., & Wardley, L. J. (2024). A supplier selection & order allocation planning framework by integrating deep learning, principal component analysis, and optimization techniques. Expert Systems with Applications, 235, 121121. https://doi.org/10.1016/j.eswa.2023.121121
  • Karakoç, N., Eren, T., & Özcan, E. (2020). Sürdürülebilir Tedarik Zinciri Yönetimi için Endüstri 4.0’daki Zorluklarin Değerlendirilmesi. Endüstri Mühendisliği, 31(2), 215-233. https://doi.org/10.46465/endustrimuhendisligi.694613
  • Khanuja, A., & Jain, R. K. (2019). Supply chain integration: A review of enablers, dimensions and performance. Benchmarking: An International Journal, 27(1), 264-301. https://doi.org/10.1108/bij-07-2018-0217
  • Koçoğlu, İ., İmamoğlu, S. Z., İnce, H., & Keskin, H. (2011). The effect of supply chain integration on information sharing: Enhancing the supply chain performance. Procedia-social and behavioral sciences, 24, 1630-1649. https://doi.org/10.1016/j.sbspro.2011.09.016
  • Kunduru, A. R., & Kandepu, R. (2023). Data archival methodology in enterprise resource planning applications (Oracle ERP, Peoplesoft). Journal of Advances in Mathematics and Computer Science, 38(9), 115-127.
  • Lamer, A., Fruchart, M., Paris, N., Popoff, B., Payen, A., Balcaen, T., … Doutreligne, M. (2022). Standardized description of the feature extraction process to transform raw data into meaningful information for enhancing data reuse: Consensus study. JMIR Medical Informatics, 10(10), e38936. https://doi.org/10.2196/38936
  • Lee, H. L., Padmanabhan, V., & Whang, S. (1997). The Bullwhip Effect in Supply Chains. Sloan Management Review, 38(3), 93-102.
  • Mentzer, J. T., DeWitt, W., Keebler, J. S., Min, S., Nix, N. W., Smith, C. D., & Zacharia, Z. G. (2001). DEFINING SUPPLY CHAIN MANAGEMENT. Journal of Business Logistics, 22(2), 1-25. https://doi.org/10.1002/j.2158-1592.2001.tb00001.x
  • Min, S., Zacharia, Z. G., & Smith, C. D. (2019). Defining Supply Chain Management: In the Past, Present, and Future. Journal of Business Logistics, 40(1), 44-55. https://doi.org/10.1111/jbl.12201
  • Mohamed, A. E. (2024). Inventory Management. Geliş tarihi gönderen https://www.intechopen.com/online-first/88430
  • Ni, D., Xiao, Z., & Lim, M. K. (2020). A systematic review of the research trends of machine learning in supply chain management. International Journal of Machine Learning and Cybernetics, 11(7), 1463-1482. https://doi.org/10.1007/s13042-019-01050-0
  • Oyewole, A. T., Okoye, C. C., Ofodile, O. C., & Ejairu, E. (2024). Reviewing predictive analytics in supply chain management: Applications and benefits. World Journal of Advanced Research and Reviews, 21(3), 568-574. https://doi.org/10.30574/wjarr.2024.21.3.0673
  • Özdemir, A. İ. (2004). Tedarik zinciri yönetiminin gelişimi, süreçleri ve yararları. Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, (23). Geliş tarihi gönderen https://dergipark.org.tr/en/pub/erciyesiibd/issue/5880/77809
  • Power, D. (2005). Supply chain management integration and implementation: A literature review. Supply chain management: an International journal, 10(4), 252-263. https://doi.org/10.1108/13598540510612721
  • Prajapati, M. (2024). We are integrating Artificial Intelligence and Data Analytics for Supply Chain Optimization in the Pharmaceutical Industry. J. Electrical Systems, 20(3s), 682-690.
  • Rai, R., Tiwari, M. K., Ivanov, D., & Dolgui, A. (2021). Machine learning in manufacturing and industry 4.0 applications. International Journal of Production Research, 59(16), 4773-4778. https://doi.org/10.1080/00207543.2021.1956675
  • Rodríguez, E. Y. A., Rodríguez, E. C. A., D, A. F., Silva, A., Rizol, P. M. S. R., D, R., … Marins, F. A. S. (2024). Analysis of machine learning integration into supply chain management. International Journal of Logistics Systems and Management, 47(3), 327-355. https://doi.org/10.1504/ijlsm.2024.136856
  • Sayer, S., & Ülker, A. (2014). ÜRÜN YAŞAM DÖNGÜSÜ YÖNETİMİ. Engineer & the Machinery Magazine, (657). Geliş tarihi gönderen https://search.ebscohost.com/login.aspx? direct=true&profile=ehost&scope=site&authtype=crawler&jrnl=13003402&AN=101072025&h=I8GAYfXMY%2Fpb%2BIEFz6G7i%2FyDBNwisNyBtWQOXBzCNPlMNTx0BETJaBTGEpQvzt4z%2BoQrb7DC2xJNT7bpLYpGLA%3D%3D&crl=c
  • Tiwari, S. (2021). Supply chain integration and Industry 4.0: A systematic literature review. Benchmarking: An International Journal, 28(3), 990-1030. https://doi.org/10.1108/BIJ-08-2020-0428
  • Toorajipour, R., Sohrabpour, V., Nazarpour, A., Oghazi, P., & Fischl, M. (2021). Artificial intelligence in supply chain management: A systematic literature review. Journal of Business Research, 122, 502-517. https://doi.org/10.1016/j.jbusres.2020.09.009
  • Usuga Cadavid, J. P., Lamouri, S., Grabot, B., Pellerin, R., & Fortin, A. (2020). Machine learning applied in production planning and control: A state-of-the-art in the era of industry 4.0. Journal of Intelligent Manufacturing, 31(6), 1531-1558. https://doi.org/10.1007/s10845-019-01531-7
  • Xu, Q., He, F., & Qiu, R. G. (2005). Heterogeneous information integration for supply chain systems. 2005 IEEE International Conference on Systems, Man and Cybernetics, 1, 97-102. IEEE. Geliş tarihi gönderen https://ieeexplore.ieee.org/abstract/document/1571128/?casa_token=RzXzXZchGqgAAAAA:og5MkaX1r-sN6a3PR_GuUPHn_v7qcwtL8JRTrFppYdT4oCOjjJKtDEB8U9hD28T0SjkKp2I https://doi.org/10.1109/ICSMC.2005.1571128
  • Yu, W., Jacobs, M. A., Salisbury, W. D., & Enns, H. (2013). The effects of supply chain integration on customer satisfaction and financial performance: An organizational learning perspective. International Journal of Production Economics, 146(1), 346-358. https://doi.org/10.1016/j.ijpe.2013.07.023

ENHANCING SUPPLY CHAIN WITH INTEGRATION ARCHITECTURE AND MACHINE LEARNING

Yıl 2026, Cilt: 29 Sayı: 1, 124 - 147, 03.03.2026

Fatih Soygazi , Haktan Akdağ , Kevser Öztürk

https://doi.org/10.17780/ksujes.1773821
https://izlik.org/JA83FW99SZ

Öz

Digitalization has become a necessity of our era within the supply chain. When developing a supply chain tracking system, there is a need to create an autonomous system to analyze the data inputs and outputs of materials that require continuous monitoring, such as order placement, determination of current stock status, and procurement or consumption processes of raw materials or semi-finished goods at stock levels. In this context, the focus has been on the possibility of using ERP systems among different stakeholders for shared data usage and forecasting based on this data. In the use case scenario, studies have been conducted on determining the amount of flour used by a bakery with over a hundred bread sales points. These studies were conducted using machine learning method is regression. The goal of proposed study was to determine whether the flour stock is sufficient at the moment. Here, the aim is to predict the amount of flour that will be in stock the next day with a certain margin of error, using the available flour quantity with the use of regression models. In this study, traditional machine learning and deep learning methods were used. The results obtained were interpreted using XAI (Explainable Artificial Intelligence) methods. As a result of this entire process, the LightGBM algorithm yielded the best result with an R2 value of 0.90 and a MAE (Mean Absolute Error) value of 39.25.

Anahtar Kelimeler

Supply chain Machine learning integration regression

Kaynakça

  • Aamer, A., Eka Yani, L., & Alan Priyatna, Im. (2020). Data analytics in the supply chain management: Review of machine learning applications in demand forecasting. Operations and Supply Chain Management: An International Journal, 14(1), 1-13. http://doi.org/10.31387/oscm0440281
  • Acar, S. G., & Yılmaz, M. (2013). Matbaa İşletmeleri İçin Bir Malzeme İhtiyaç Planlama Yazılımı Geliştirme ve Uygulanması. Bilişim Teknolojileri Dergisi, 6(1), 23-32.
  • Akdağ, H., & Kocakoç, İ. D. (2023). TOPLAM KALİTE YÖNETİMİ, KAIZEN VE ENTEGRASYON SÜREÇLERİNİN BÜTÜNLEŞİK ANALİZİ. Nazilli İktisadi ve İdari Bilimler Fakültesi Dergisi, 4(2), 119-132. https://doi.org/10.59113/niibfd.1391223
  • Aljohani, A. (2023). Predictive analytics and machine learning for real-time supply chain risk mitigation and agility. Sustainability, 15(20), 15088. https://doi.org/10.3390/su152015088
  • Bagchi, P. K., Chun Ha, B., Skjoett-Larsen, T., & Boege Soerensen, L. (2005). Supply chain integration: A European survey. The international journal of logistics management, 16(2), 275-294. https://doi.org/10.1108/09574090510634557
  • Basana, S., Suprapto, W., Andreani, F., & Tarigan, Z. (2022). The impact of supply chain practice on green hotel performance through internal, upstream, and downstream integration. Uncertain Supply Chain Management, 10(1).
  • Botta-Genoulaz, V., & Millet, P.-A. (2005). A classification for better use of ERP systems. Computers in industry, 56(6), 573-587. https://doi.org/10.1016/j.compind.2005.02.007
  • Carbonneau, R., Laframboise, K., & Vahidov, R. (2008). Application of machine learning techniques for supply chain demand forecasting. European journal of operational research, 184(3), 1140-1154. https://doi.org/10.1016/j.ejor.2006.12.004
  • Cebeci, K., & Korçak, Ö. (2020). Design of an enterprise level architecture based on microservices. Bilişim Teknolojileri Dergisi, 13(4), 357-371. https://doi.org/10.17671/gazibtd.558392
  • Chen, H.-C., Lu, M.-J., Liu, C.-C., & Tsai, C.-H. (2012). A study of the performance improvement of bill of material document sign flow system. International Journal of Academic Research in Accounting, Finance and Management Sciences, 2(1), 65-79.
  • Condon, S. (2024). GenAI: Agentic Workflows with the Supply Chain Advisor. SAP Community. Geliş tarihi gönderen https://community.sap.com/t5/technology-blogs-by-sap/genai-agentic-workflows-with-the-supply-chain-advisor/ba-p/13700531
  • Davenport, T. H., & Brooks, J. D. (2004). Enterprise systems and the supply chain. Journal of Enterprise Information Management, 17(1), 8-19. https://doi.org/10.1108/09576050410510917
  • Davis, T. (1993). Effective supply chain management. Sloan management review, 34, 35-35. Doğar, A. (2006). Tedarik Zinciri Nde Stok Yönetimi. Geliş tarihi gönderen https://polen.itu.edu.tr/bitstreams/71d3843e-d65d-470d-a63f-4362c41f9100/download
  • Droge, C., Vickery, S. K., & Jacobs, M. A. (2012). Does supply chain integration mediate the relationships between product/process strategy and service performance? An empirical study. International Journal of Production Economics, 137(2), 250-262. https://doi.org/10.1016/j.ijpe.2012.02.005
  • Elufioye, O. A., Ike, C. U., Odeyemi, O., Usman, F. O., & Mhlongo, N. Z. (2024). Ai-Driven predictive analytics in agricultural supply chains: A review: assessing the benefits and challenges of ai in forecasting demand and optimizing supply in agriculture. Computer Science & IT Research Journal, 5(2), 473-497.
  • Frohlich, M. T., & Westbrook, R. (2001). Arcs of integration: An international study of supply chain strategies. Journal of operations management, 19(2), 185-200. https://doi.org/10.1016/S0272-6963(00)00055-3
  • Ghobakhloo, M. (2020). Industry 4.0, digitization, and opportunities for sustainability. Journal of cleaner production, 252, 119869. https://doi.org/10.1016/j.jclepro.2019.119869
  • Groenewald, C. A., Garg, A., & Yerasuri, S. S. (2024). Smart supply chain management optimization and risk mitigation with artificial intelligence. Naturalista campano, 28(1), 261-270.
  • Hazen, B. T., Boone, C. A., Ezell, J. D., & Jones-Farmer, L. A. (2014). Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications. International Journal of Production Economics, 154, 72-80. https://doi.org/10.1016/j.ijpe.2014.04.018
  • Islam, S., Amin, S. H., & Wardley, L. J. (2024). A supplier selection & order allocation planning framework by integrating deep learning, principal component analysis, and optimization techniques. Expert Systems with Applications, 235, 121121. https://doi.org/10.1016/j.eswa.2023.121121
  • Karakoç, N., Eren, T., & Özcan, E. (2020). Sürdürülebilir Tedarik Zinciri Yönetimi için Endüstri 4.0’daki Zorluklarin Değerlendirilmesi. Endüstri Mühendisliği, 31(2), 215-233. https://doi.org/10.46465/endustrimuhendisligi.694613
  • Khanuja, A., & Jain, R. K. (2019). Supply chain integration: A review of enablers, dimensions and performance. Benchmarking: An International Journal, 27(1), 264-301. https://doi.org/10.1108/bij-07-2018-0217
  • Koçoğlu, İ., İmamoğlu, S. Z., İnce, H., & Keskin, H. (2011). The effect of supply chain integration on information sharing: Enhancing the supply chain performance. Procedia-social and behavioral sciences, 24, 1630-1649. https://doi.org/10.1016/j.sbspro.2011.09.016
  • Kunduru, A. R., & Kandepu, R. (2023). Data archival methodology in enterprise resource planning applications (Oracle ERP, Peoplesoft). Journal of Advances in Mathematics and Computer Science, 38(9), 115-127.
  • Lamer, A., Fruchart, M., Paris, N., Popoff, B., Payen, A., Balcaen, T., … Doutreligne, M. (2022). Standardized description of the feature extraction process to transform raw data into meaningful information for enhancing data reuse: Consensus study. JMIR Medical Informatics, 10(10), e38936. https://doi.org/10.2196/38936
  • Lee, H. L., Padmanabhan, V., & Whang, S. (1997). The Bullwhip Effect in Supply Chains. Sloan Management Review, 38(3), 93-102.
  • Mentzer, J. T., DeWitt, W., Keebler, J. S., Min, S., Nix, N. W., Smith, C. D., & Zacharia, Z. G. (2001). DEFINING SUPPLY CHAIN MANAGEMENT. Journal of Business Logistics, 22(2), 1-25. https://doi.org/10.1002/j.2158-1592.2001.tb00001.x
  • Min, S., Zacharia, Z. G., & Smith, C. D. (2019). Defining Supply Chain Management: In the Past, Present, and Future. Journal of Business Logistics, 40(1), 44-55. https://doi.org/10.1111/jbl.12201
  • Mohamed, A. E. (2024). Inventory Management. Geliş tarihi gönderen https://www.intechopen.com/online-first/88430
  • Ni, D., Xiao, Z., & Lim, M. K. (2020). A systematic review of the research trends of machine learning in supply chain management. International Journal of Machine Learning and Cybernetics, 11(7), 1463-1482. https://doi.org/10.1007/s13042-019-01050-0
  • Oyewole, A. T., Okoye, C. C., Ofodile, O. C., & Ejairu, E. (2024). Reviewing predictive analytics in supply chain management: Applications and benefits. World Journal of Advanced Research and Reviews, 21(3), 568-574. https://doi.org/10.30574/wjarr.2024.21.3.0673
  • Özdemir, A. İ. (2004). Tedarik zinciri yönetiminin gelişimi, süreçleri ve yararları. Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, (23). Geliş tarihi gönderen https://dergipark.org.tr/en/pub/erciyesiibd/issue/5880/77809
  • Power, D. (2005). Supply chain management integration and implementation: A literature review. Supply chain management: an International journal, 10(4), 252-263. https://doi.org/10.1108/13598540510612721
  • Prajapati, M. (2024). We are integrating Artificial Intelligence and Data Analytics for Supply Chain Optimization in the Pharmaceutical Industry. J. Electrical Systems, 20(3s), 682-690.
  • Rai, R., Tiwari, M. K., Ivanov, D., & Dolgui, A. (2021). Machine learning in manufacturing and industry 4.0 applications. International Journal of Production Research, 59(16), 4773-4778. https://doi.org/10.1080/00207543.2021.1956675
  • Rodríguez, E. Y. A., Rodríguez, E. C. A., D, A. F., Silva, A., Rizol, P. M. S. R., D, R., … Marins, F. A. S. (2024). Analysis of machine learning integration into supply chain management. International Journal of Logistics Systems and Management, 47(3), 327-355. https://doi.org/10.1504/ijlsm.2024.136856
  • Sayer, S., & Ülker, A. (2014). ÜRÜN YAŞAM DÖNGÜSÜ YÖNETİMİ. Engineer & the Machinery Magazine, (657). Geliş tarihi gönderen https://search.ebscohost.com/login.aspx? direct=true&profile=ehost&scope=site&authtype=crawler&jrnl=13003402&AN=101072025&h=I8GAYfXMY%2Fpb%2BIEFz6G7i%2FyDBNwisNyBtWQOXBzCNPlMNTx0BETJaBTGEpQvzt4z%2BoQrb7DC2xJNT7bpLYpGLA%3D%3D&crl=c
  • Tiwari, S. (2021). Supply chain integration and Industry 4.0: A systematic literature review. Benchmarking: An International Journal, 28(3), 990-1030. https://doi.org/10.1108/BIJ-08-2020-0428
  • Toorajipour, R., Sohrabpour, V., Nazarpour, A., Oghazi, P., & Fischl, M. (2021). Artificial intelligence in supply chain management: A systematic literature review. Journal of Business Research, 122, 502-517. https://doi.org/10.1016/j.jbusres.2020.09.009
  • Usuga Cadavid, J. P., Lamouri, S., Grabot, B., Pellerin, R., & Fortin, A. (2020). Machine learning applied in production planning and control: A state-of-the-art in the era of industry 4.0. Journal of Intelligent Manufacturing, 31(6), 1531-1558. https://doi.org/10.1007/s10845-019-01531-7
  • Xu, Q., He, F., & Qiu, R. G. (2005). Heterogeneous information integration for supply chain systems. 2005 IEEE International Conference on Systems, Man and Cybernetics, 1, 97-102. IEEE. Geliş tarihi gönderen https://ieeexplore.ieee.org/abstract/document/1571128/?casa_token=RzXzXZchGqgAAAAA:og5MkaX1r-sN6a3PR_GuUPHn_v7qcwtL8JRTrFppYdT4oCOjjJKtDEB8U9hD28T0SjkKp2I https://doi.org/10.1109/ICSMC.2005.1571128
  • Yu, W., Jacobs, M. A., Salisbury, W. D., & Enns, H. (2013). The effects of supply chain integration on customer satisfaction and financial performance: An organizational learning perspective. International Journal of Production Economics, 146(1), 346-358. https://doi.org/10.1016/j.ijpe.2013.07.023
Toplam 42 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular İş Süreçleri Yönetimi, Karar Desteği ve Grup Destek Sistemleri, Makine Öğrenme (Diğer), Yapay Zeka (Diğer), Yazılım Mimarisi
Bölüm Araştırma Makalesi
Yazarlar

Fatih Soygazi 0000-0001-8426-2283

Haktan Akdağ 0009-0003-2151-7228

Kevser Öztürk 0009-0006-4888-066X

Gönderilme Tarihi 29 Ağustos 2025
Kabul Tarihi 11 Aralık 2025
Yayımlanma Tarihi 3 Mart 2026
DOI https://doi.org/10.17780/ksujes.1773821
IZ https://izlik.org/JA83FW99SZ
Yayımlandığı Sayı Yıl 2026 Cilt: 29 Sayı: 1

Kaynak Göster

APA Soygazi, F., Akdağ, H., & Öztürk, K. (2026). ENTEGRASYON MİMARİSİ KULLANAN TEDARİK ZİNCİRİNİN MAKİNE ÖĞRENMESİ İLE DESTEKLENMESİ. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 29(1), 124-147. https://doi.org/10.17780/ksujes.1773821