Abstract
The paper focuses on the stochastic modelling of the quasi-periodic intermittent demand patterns, which arise in the inventory management of the “slow moving items” such as service parts or high-priced capital goods. It is proposed a new stochastic model, which describes the demand patterns with essentially non-exponential distribution of the inter-arrival times. The model is based on generalized beta-binomial distribution and the Bayesian inference using the historical data array describing the demand repeatability within the time periods. For this model, there were derived explicit expressions for the forecast distributions, its moments and relevant Bayesian risk. The efficiency of the proposed approach is confirmed by computer simulation and an application example.
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