Demand forecasting model of service parts with different failure rate

Manufacturers are required to provide repair service for their users after sales. Therefore manufacturers usually hold inventory of service parts for many years to keep their repair service level satisfactory. On the other hand, they have to decide the production volume of service parts before the assurance period expires because they cannot keep the production facility for each product for many years. Thus, demand forecasting of service parts close to the end of the assurance period is becoming more important. An accurate demand forecasting enables manufacturers to keep appropriate inventory level of service parts for the remaining period. In this paper, we propose a new demand forecasting model. Our model is developed on the basis of the three factors: the failure rate of a part, the replacement rate of a failed part and the declining rate of the population of products in use. The failure rate is derived from mixing the Weibull distribution. The replacement rate and declining rate are modeled as exponential function. We examined the forecasting performance of the proposed model, using actual shipment records of service parts at a home appliance manufacturer. The results show that the proposed model outperforms the conventional model based on renewal theory.