AI Summary & Key Takeaways
AI InsightThis methodology can be used in decision-making processes on logistics planning and strategic selection of warehouse locations, while helping businesses increase their competitive advantage.
📌 3 Key Takeaways
Abstract
Choosing the right warehouse location reduces costs while increasing efficiency and customer satisfaction in logistics processes. However, the choice of warehouse location usually involves a large number of uncertain factors. This study examines the fuzzy c-means method in the warehouse location selection process. Using the principles of fuzzy logic, it offers a methodology that allows the warehouse location to be evaluated with uncertainty and imprecise data. The flexibility, uncertainty, and successful applicability of fuzzy logic to real-world problems are important in decision-making processes such as warehouse location. The fuzzy C-means method is a clustering algorithm used to identify groups (clusters) in the data set. This approach makes decisions regarding warehouse location selection more accurate and supported by information. The results of the study show that the fuzzy C-means method can be used effectively in warehouse location selection and that this approach adds value to the decision processes in logistics management. This methodology can be used in decision-making processes on logistics planning and strategic selection of warehouse locations, while helping businesses increase their competitive advantage.