Abstract: In part inventory management, in order to accurately forecast future number of orders of parts for which order has, for example, fallen to one unit per month or fewer, low-order-rate parts whose order rates to have fallen below the predetermined level are extracted, a parameter indicating a characteristic of orders is determined and classification into multiple categories is conducted. Then, using the parameter, an order occurrence probability distribution is calculated for each category. Monte Carlo simulation is carried out based on the calculated order occurrence probability distributions to determine occurrence rate probability distributions of number of orders during a predetermined period, and the future number of orders of the low-order-rate parts are forecast based on the calculated occurrence rate probability distributions of number of orders during the predetermined period.

Abstract: In part inventory management, in order to accurately forecast future number of orders of parts for which order has, for example, fallen to one unit per month or fewer, low-order-rate parts whose order rates to have fallen below the predetermined level are extracted, a parameter indicating a characteristic of orders is determined and classification into multiple categories is conducted. Then, using the parameter, an order occurrence probability distribution is calculated for each category. Monte Carlo simulation is carried out based on the calculated order occurrence probability distributions to determine occurrence rate probability distributions of number of orders during a predetermined period, and the future number of orders of the low-order-rate parts are forecast based on the calculated occurrence rate probability distributions of number of orders during the predetermined period.