Penerapan Data Mining Dalam Pengendalian Persediaan Sparepart di PT. Honda IDK 1

Abstract

Spare parts are replaceable or interchangeable components of a machine that are kept in stock and used for the repair or renewal of damaged equipment or units. Honda IDK 1 is a trade industry business entity in the form of a car dealer and showroom with the Honda brand that provides workshop services. These services include routine servicing and repairs. Honda IDK 1 serves thousands of customers every month and with total annual service reaching tens of thousands. It is not uncommon for Honda IDK 1 to experience stock outs that hamper the service and repair process which adversely affects workshop performance and decreases customer satisfaction. To overcome this problem, inventory control efforts are made by looking for customer purchasing patterns. This customer purchase pattern information will be used to obtain product recommendations in demand forecasting as well as determining safety stock, and reorder points. Analysis of customer purchasing patterns to forecasting will be carried out using data mining techniques. The clustering technique produces 5 cluster sections, ranging from slow moving to fast moving. Association rules produce 15,321 combinations of association rules and 98 unique parts. The results of designing safety stock obtained the part with the most safety stock is Washer Drain 14 mm with a total of 1,129. The results of the reorder point design obtained the part with the most reorder points is Washer Drain 14 mm with a reorder point value of 1,814. From the results of this processing, the appropriate inventory model is designed.