A Fuzzy Inference System-Based Decision Support System Proposal for Subcontractor Performance Evaluation in 3PL Companies

Abstract

Third-party logistics (3PL) companies must ensure timely delivery to achieve customer satisfaction and competitive advantage. This study proposes a Fuzzy Inference System (FIS)-based decision support system to evaluate the performance of subcontractor businesses and optimize delivery processes for 3PL companies. FIS is a method capable of producing effective results in situations involving uncertainty and complexity by modeling human-like reasoning approaches. In the study, three key criteria were determined to measure the performance of subcontractor businesses: adherence to the delivery schedule, route compliance and process closure success, and operational agility. These criteria were integrated into an FIS-based model, where the performance of subcontractors was assessed using linguistic scales and fuzzy rules. The model was tested with real data, and performance scores were calculated for businesses with different performance levels. Highperforming businesses were rewarded, while penalties were proposed for low-performing ones. The results indicate that the model can effectively evaluate the performance of subcontractors in 3PL companies and assist in making decisions aimed at process improvements.