Üretken Yapay Zekânın Satın Alma Süreçlerinde Karar Destek Aracı Olarak Kullanılması: ChatGPT, CoPilot ve Gemini Araçlarının Karşılaştırılması

Öz

İşletmelerde satın alma taleplerinin doğru önceliklendirilmesi, iş sürekliliği ve kaynak yönetimi açısından kritik öneme sahiptir. Gün içinde farklı departmanlarca oluşturulan talepler genellikle satın alma birimi tarafından sübjektif olarak sıralanmakta, bu da bazı acil taleplerin geri planda kalmasına yol açabilmektedir. Sürecin insan kontrolünde yürütülmesi hem zaman kaybına hem de hatalı önceliklendirmelere neden olmaktadır. Bu çalışmada, bir üretim işletmesinin ERP sistemine API aracılığıyla entegre edilen üç üretken yapay zekâ aracı (ChatGPT-4.5, Microsoft CoPilot ve Google Gemini) kullanılarak 100 satın alma talebi önce “Acil”, “Normal” ve “Acil Değil”, ardından “Acil” ve “Normal” biçiminde sınıflandırılmıştır. Yapay zekâ modellerinin sonuçları, satın alma personelinin sınıflandırmalarıyla karşılaştırılarak doğruluk, Cohen’s Kappa, precision, recall ve F1-score metrikleri üzerinden değerlendirilmiştir. Ayrıca üretken yapay zeka araçlarının doğru yanıt verme performansları Pearson Ki-kare testiyle incelenmiş; sonuçlar, araçlar arasında anlamlı bir karşılıklı bağımlılık olduğunu ve özellikle Copilot ile Gemini’nin hem üçlü hem de ikili sınıflandırmalarda yüksek düzeyde uyum sergilediğini göstermiştir. Bulgular, her üç modelin ikili sınıflandırmada başarılı performans gösterdiğini; özellikle CoPilot’un diğer modellere göre daha yüksek doğruluk sağladığını ortaya koymuştur. Çalışma, üretken yapay zekâ araçlarının satın alma süreçlerinde ön sınıflandırma, hız ve iş gücü tasarrufu açısından etkili bir karar destek aracı olarak kullanılabileceğini göstermektedir.

Anahtar Kelimeler

• Üretken Yapay Zeka
• Satın Alma Süreci
• Karar Destek Sistemi
• Kurumsal Kaynak Planlama (KKP)

Using Generative Artificial Intelligence as a Decision Support Tool in Purchasing Processes: Comparison of ChatGPT, CoPilot, and Gemini Tools

Abstract

Accurate prioritization of purchase requests in enterprises is critical for ensuring business continuity and effective resource management. Throughout the day, requests generated by different departments are usually ranked subjectively by the purchasing unit, which may cause some urgent requests to be deprioritized. Managing the process under human control leads to time loss and inaccurate prioritization. This study integrated three generative artificial intelligence tools—ChatGPT-4.5, Microsoft CoPilot, and Google Gemini—into a manufacturing company’s ERP system via an API. A total of 100 purchase requests were classified first into three categories (“Urgent,” “Normal,” and “Not Urgent”) and then into two categories (“Urgent” and “Normal”). The results produced by the AI models were compared with the classifications made by the purchasing staff and evaluated using accuracy, Cohen’s Kappa, precision, recall, and F1-score metrics. In addition, the correct response performance of generative artificial intelligence tools was analyzed using the Pearson Chi-square test; the results revealed a significant interdependence among the tools, with Copilot and Gemini showing an exceptionally high consistency across both triple and binary classifications. The findings revealed that all three models performed well in the binary classification, with CoPilot achieving higher accuracy than the others. The study demonstrates that generative AI tools can be practical decision-support systems in purchasing processes, offering significant advantages in preliminary classification, efficiency, and time savings.

Keywords

• Generative Artificial Intelligence
• Purchasing Processes
• Decision Support System
• Enterprise Resource Planning (ERP)

Kaynakça

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