A stochastic model for military air-to-ground munitions demand forecasting

摘要

Planning for military operations involves the consideration of a number of factors, including the required quantity of munitions. As these items typically require lengthy procurement lead times, stockpile levels must be established and maintained in anticipation of such operations. One of the key aspects of stockpile inventory management is demand forecasting. In this paper, we examine the problem of modeling and forecasting the munitions demand in military operations. We consider a continuous state space time series approach and we model the munitions demand as a non-homogeneous Poisson process with intensity function that depends on a continuous-time Markovian operational tempo process. Such a model is known in stochastic process as a Markov-Modulated Poisson Process (MMPP) and has been successfully applied to queuing and communication network problems. We apply the maximum likelihood estimation method to derive the MMPP model parameters. We illustrate the method with an example based upon air-to-ground munitions. This study provides military planners with a decision support method for forecasting munitions requirements in the face of abrupt changes in demand.