Enhancing Business Transaction Networks by Industry Through DNN-Based Partner Recommendations

Abstract

In today’s highly competitive global market, a company’s success often hinges on how well it can identify and build productive partnerships. This is especially true in supply chain management, where the relationship between suppliers and buyers plays a huge role in determining how efficiently a business runs and how profitable it can be. Traditionally, companies have relied on long-established networks and past relationships when selecting partners. However, this approach can be limiting, as it often overlooks new opportunities due to gaps in information or preconceived biases. To tackle this challenge, the authors of the study introduced a new model based on Deep Neural Networks (DNN). This model is designed to help businesses in South Korea identify potential partnerships across a variety of industries. By taking factors like company size, product offerings, and market position into account, the DNN model can pinpoint partnerships that would be mutually beneficial. The results were striking—there was a significant increase in the number of potential partnerships identified, showing just how effective this approach could be in widening the scope of business collaborations. Notably, the model identified numerous feasible buyer companies that had not been previously considered by suppliers, suggesting that DNN-based recommendations can challenge and reshape traditional perspectives on business compatibility. This research contributes to the growing body of AI-driven business processes and offers practical insights for companies seeking to enhance their supply chain networks through the adoption of innovative technological solutions.