Forecasting the future demand of spare parts based on vehicle population in a market region

The challenge of low inventories and high service level has led companies to focus more on predicting the future. Especially in after sales market, the ability to have right products available when a customer requires and at the same time avoid binding capital to inventories. The main challenge is faced with the slow moving products which are demanded irregular. The purpose of this research is to combine two different data sources, in a field of spare part business, to examine whether the sources give a better demand prediction for products with irregular demand than one source alone. The research focuses on an example company in a field of mining and tunneling vehicle manufacturing. The examined data sources are comprised of the past sales data and the maintenance plan of a vehicle. The combination of these two data sources is not extensively studied as a part of a demand forecasting. To achieve the objectives of the project, the research is conducted by using design science research methodology. The main objective is to test and develop a forecast model, by using the existing forecasting methods, to support the forecasting of the irregularly sold products.