Optimization of Motorcycle Stock and Distribution Using the Apriori Method

Abstract

Stock management and distribution in motorcycle sales companies often face challenges regarding discrepancies between inventory levels and customer demand. Such conditions can lead to overstocking of certain products or stockouts of high-demand items, resulting in increased holding costs, distribution delays, and diminished customer service quality. Therefore, a method capable of analyzing consumer purchasing patterns based on sales transaction data is required to formulate more effective stock management and distribution strategies. This study aims to optimize stock and distribution strategies by applying the Apriori method to identify relationships between products frequently purchased together. The study benefits the company by enhancing inventory management efficiency, minimizing the risk of overstocking or stockouts, and supporting more accurate, data-driven decision-making. The Apriori method was implemented through stages including transaction data collection, preprocessing, generating frequent itemsets based on minimum support values, calculating confidence values, and formulating association rules to identify product interrelationship patterns. The results indicate that the highest-selling products requiring priority in stock management are engine oil, brake pads, and automatic transmission (matic) gear oil. These three products exhibited the highest frequency of occurrence in transactions, making them top priorities for stocking to prevent inventory shortages and ensure smooth distribution. The application of the Apriori method proved capable of generating valuable insights into purchasing patterns, thereby improving stock management effectiveness, accelerating distribution processes, and enhancing customer service quality.