Supplier Selection for Milk-run Logistics of an Automotive Parts Manufacturer

Abstract

      This research presents an algorithm for supplier selection for Milk-run logistics of an automotive parts manufacturer in order to minimize total operating cost per year. The motivation of this research is because these manufacturers’ customers usually force them to reduce their commodity price every year. Milk-run systems are well known for reducing operating cost, thus manufacturers are interested in applying this idea into their organizations. However, most manufacturers have a variety of their suppliers’ characteristics and it may not be worthwhile to include all of them in the milk-run system. Therefore, this research proposes 2 methods in selecting milk-run suppliers: Total Enumeration and Heuristic method. This research demonstrates the efficiency of the algorithms from the result of applying proposed algorithms in 12 problem instances with different situations. From the result, it shows that both methods are well used in real situations.  The overall cost different between total enumeration and heuristic method is 0.4% and we found that heuristic method performs better when suppliers have random location as compared to clustered locations while total enumeration gives better results when supplier locations are clustered.