When a new model of a machine is introduced, fewer units of the old model are shipped but spare parts must still be supplied to customers continuing to use the old model. However, many companies, especially those not involved in direct sales, struggle to supply parts and hold a suitable inventory because of the difficulty of determining the product lifetime and the number of working machines (NWM). Accordingly, this paper aims to use data on shipments of machines and parts to analyze NWM, order quantity of spare parts, and inventory in the supply chain. From the analysis, a method for estimating NWM and the order quantity of spare parts is proposed, and a policy for holding inventory is discussed. A classification scheme based on trends in spare part orders and an inventory policy for each classification are devised. Finally, the method is validated against actual data on small developing machines.

Earlier studies that estimated the credit cost of banks used explanatory variables and estimate models which had been already used in further earlier studies. This study explored some key explanatory variables from the mound of factors by using a multi-regression analysis and CART. Also this study assessed the suitability of a multi-regression analysis, neural network, and support vector machine as an estimate model. As a result of the study, the loan amount per customer and the deposit growth rate in the city where headquarters is located were selected as key variables. Also non-parametric method, such as neural network and support vector machine was proven suitable as a variable selection method and an estimate model on the ground that the relation between the credit cost and explanatory variables is non-linear and non-continuous.