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Inventory management method based on flow simulations


V.A. Bugrimov – Senior Lecturer, Department «Land Vehicles», Moscow Polytechnic University
V.Yu. Stroganov – Dr. Sc. (Eng.), Professor, Department «Information Processing and Control Systems», Bauman Moscow State Technical University
V.M. Chernenky – Dr. Sc. (Eng.), Professor, Head of Department «Information Processing and Control Systems», Bauman Moscow State Technical University

In this paper, a description of the simulation model and the results of modeling a single-product inventory management system of a service center is given. So, for the classical model of inventory management, there are various generalizations and extensions. This may be a deficit, which leads to the need to calculate the loss of profits. It is possible to simulate a given time interval for the delivery of parts after the order. There is still a lot of digressions. In addition, quite often the deterministic model does not give adequate results, which leads to the need to introduce stochastic parametrization. All this makes the simulation model more adequate. And it can have a sufficiently large number of parameters.
The paper describes a model that has the following set of parameters: the minimum amount of reserves for the formation of an order for the supply of spare parts; Maximum volume of stocks, random time of delivery of spare parts with random distribution; The planning horizon of the inventory management system; The intensity of requests for a particular part; The price of holding one position per unit of time; Cost of delivery of the lot; Penalty for missing parts and others.
The algorithm for modeling the inventory management system contains the steps: generation of a model range of volumes of requests for spare parts; Making an order planning decision; Monitoring the number of balances in the warehouse and others.
When solving the problem of quantitative parametrization of the model, a preliminary statistical analysis was carried out for a different nomenclature of details, which, based on the implementation of nonparametric estimation procedures, demonstrated the adequacy of using a Poisson flow, although there are small but statistically significant autocorrelation functions for the flow of spare parts orders. In this regard, the use of autoregressive models of the first and second order is proposed for flow modeling.
Based on the series of experiments performed for various combinations of parameters, nonlinear regression models were obtained that showed the effect of parameters on the choice of optimal values of the minimum stock and the maximum batch volume. The most attention is paid to the analysis of the influence of the intensity of the consumption of spare parts on these values of the controlled parameters. Within the framework of the analysis and the revealed dependencies, an adaptive algorithm is proposed for forming the batch volume, depending on the predicted values of the intensity of requests for spare parts.

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