Inventory Demand Forecasting for a Motorcycle Company

This capstone project is focused on optimizing and forecasting the inventory demand for spare parts of a motorcycle company named MR Motors. The objective of the project is to predict the stock demand for each spare part required for the service and maintenance of the motorcycles. The expected outcome of the project is to help the company in purchasing inventory on the basis of understanding from the demand forecasting of the spare parts. The existing inventory management system of MR Motors is a software called Auto – Ordering Vector, which places the purchase order for spare parts that are required for the service and maintenance of the motorcycles. The vector compares the current stock count with the minimum stock count and then places the purchase order based on the difference in the stock of each spare part. The minimum stock count of the spare parts is fixed, which overlooks the varying nature of the stock demand, causing problems related to constant stock outs which may result in huge losses to the company. Therefore, MR Motors need an innovative solution to precisely analyze the stock demand trends and forecast the demand for spare parts to manage the inventory proficiently. This can be achieved by using machine learning algorithms, which will learn the historical trends in the data and provides dynamic output concerning the demand, unlike the existing system. Furthermore, the integration of the machine learning model with the vector will enhance the productivity of the vector without any human intervention.