Modeling of the supply system of industrial enterprises at a given level of reliability

High-tech production requires highly reliable systems to provide material resources. The system parameters ensuring the prescribed reliability can be determined by mathematical modeling taking into account the random nature of the production process. In this paper, a method for estimating the reliability of the system is presented. The developed technique is based on the Monte Carlo method and makes it possible to build the area of feasible parameters of the system. Unlike deterministic methods, the Monte Carlo method enables high accuracy solutions at low computational costs. The paper proposed a model of the enterprise supply system consisting of the following elements: a material supply model taking into account incoming requests, a decision-making model for ordering materials from suppliers, and a model of materials resupply by suppliers. The main attention is paid to the following two aspects: modeling of the flow of material requests from industry and construction of the area of feasible parameters of an inventory management strategy to ensure a given reliability level of logistics support. This model will be useful for industries where the production cycle cannot be interrupted because of the lack of materials since it could cause significant financial losses. In particular, it is important for energy companies, defense industry and health care.

References

[1] Ivanov D., Sokolov B. Adaptive Supply ChainManagement. London, Springer, 2010. 269 p.

[2] Sterligova A.N. Upravlenie zapasami v tsepiakh postavok [Inventory management in supply chains]. Moscow, INFRA-M publ., 2008. 428 p.

[3] Stock James R., Lambert Douglas M. Strategic Logistics Management. McGraw-Hill Education, 2001. 872 p.

[4] Donald J. Bowersox. Logistical Management. McGraw-Hill Education (India) Pvt Limited, 2000. 730 p.

[5] Brodetskii G.L. Upravlenie zapasami. Polnyi kurs MBA [Inventory Management. MBA Full Course]. Moscow, Eksmo publ., 2008. 352 p.

[6] Mahnam M., Yadollahpour M.R., Famil-Dardashti V., Hejazi S.R. Supply chain modeling in uncertain environment with bi-objective approach. Computers & Industrial Engineering, 2009, vol. 56, issue 4, pp. 1535—1544.

[7] Wiendahl H.-P., Von Cieminski G., Begemann C. A Systematic Approach for Ensuring the Logistic Process Reliability of Supply Chains. CIRP Annals-Manufacturing Technology, 2003, vol. 52, issue 1, pp. 375—380.

[8] Merschmann, U., Thonemann, U.W. Supply chain flexibility, uncertainty and firm performance: An empirical analysis of German manufacturing firms. International Journal of Production Economics, 2011, vol. 130, issue 1, pp. 43—53.

[9] Kee-Hung Lai, T. C. Edwin Cheng. Just-In-Time Logistics. Gower Publishing Ltd., 2009. 190 p.

[10] Brodetskii G.L., Gusev D.A., Elin E.A. Upravlenie riskami v logistike [Risk management in logistics]. Moscow, Akademiia publ., 2010. 192 p.

[11] Awudu I., Zhang J. Uncertainties and sustainability concepts in biofuel supply chain management: A review. Renewable and Sustainable Energy Reviews, 2012, vol. 16, issue 2, pp. 1359—1368.

[12] Awudu I., Zhang J. Stochastic production planning for a biofuel supply chain under demand and price uncertainties. Applied Energy, 2013, vol. 103, pp. 189—196.

[13] Liao H.-C., Chang H.-H. The optimal approach for parameter settings based on adjustable contracting capacity for the hospital supply chain logistics system. Expert Systems with Applications, 2011, vol. 38, issue 5, pp. 4790—4797.

[14] Mohammadi Bidhandi H., Mohd Yusuff R. Integrated supply chain planning under uncertainty using an improved stochastic approach. Applied Mathematical Modelling, 2011, vol. 35, issue 6, pp. 2618—2630.