Study On Demand Forecasting Of Military Supplies Based On Grey-Metabolism-Markov Chain Model

The main function of Logistics Department of the army is to supply forcesfor daily life and training purpose. The support of supplies involve purchasing,allocation, transportation and distribution. Among these items, allocation plays a veryimportant role. It has been a challenge to accurately estimate the actual need and a greatdeal of waster can therefore happen. With the rapid development of militarymodernization and introduction of new weapons and weapon systems, dramatic increaseof energy consumption and resource needs is observed. The forces are required to makethe best use of limited resources through optimizing the allocation and waste-reductionprocess. For this purpose we can determine the demand of supplies by forecasting thematerial consumption of daily life and training of military, so as to improve theaccuracy in material allocation. Currently，there are various methods of data forecasting,each with one’s own advantages and disadvantages. According to the characteristics ofthe military material consumption, the paper analyzes various prediction methods whichwere widely used in the world. Basing on grey GM (1,1) model, we use the metabolismmarkov chain model combined with the existing methods to improve the predictionprecision, through using the thought of combination forecast. The main work of thispaper includes:①The paper reviews various forecasting methods applied most widely andanalyze their advantages and disadvantages. We then employ the grey GM (1,1) model,considering that it fits the characteristics of military material consumption data. Thepaper introduces the combination forecast thought in detail and studies the practicalityand feasibility that the combination forecasting method is used on the special data ofmilitary to predict.②This paper introduces the grey GM (1,1) model in detail, which is widely usedin various fields. This paper proposed the new metabolism GM (1,1) model incombination with Equal-Dimension Grey replace GM (1,1) model andEqual-Dimension New Info Replace GM (1,1) model, in order to improve the accuracyof its predictions. At last, this paper corrected the prediction results of metabolism GM(1,1) model referencing the thought of combination forecast. Then, themetabolism-markov chain model formed. ③This paper uses two sets of real data which had its own characteristics to testthe grey-metabolism-markov chain model. Through a contrast analysis, the experimentfully proves that the model can effectively improve the accuracy of forecast data andcan be well applicable to the data according with military characteristics.