Prediction Model for Product Stock Procurement Using the Naive Bayes Method

Product sales circulation and product stock repurchase play a strategic role in meeting customer needs and increasing a company's profits. PT Rotaryana Engineering as a company that provides spare parts for electronic kitchen products in various brands is experiencing rapid dynamics in the sales of its various products. Therefore, a decision-making system is needed regarding spare parts stock policies that will support increased sales and customer satisfaction for the company. This research will model a product stock procurement prediction system using the Naive Bayes method. Sales data for one year will be used to categorize the availability of each product, namely the available category and the not available category. Furthermore, calculations using the Naive Bayes method produce likelihood probability values ​​for each product item which are used to predict and recommend procurement of spare parts stock. This information can be used as a basis for determining priorities for procuring stock of the most popular goods and reducing stock of less popular goods so that the circulation of the company's product stock becomes more efficient and effective.