A data mining approach for bill of materials for motor revision

Supply chain management is a core business process and is today considered the focus of competitive analysis. Business enterprises are data overloaded and, hence, using data mining techniques to transform the vast amount of data into meaningful information can be extremely beneficial. We will present a data mining approach for inventory forecasting and planning a Bill Of Materials in a highly competitive environment such as an Italian car racing team. By exploiting clustering algorithms and by using statistical techniques to identify the optimal number of clusters this work presents a method to optimally cluster a multi-year dataset containing the products used in car revision after each rally competition during a three-year period. The Bill Of Materials was used as input for the Material Requirements Planning.