Algorithm for selecting the winning strategies in the processes of managing the state of the system "supplier – consumer" in the presence of aggressive competitor
Keywords:system "supplier-consumer", l-level scale, strategic opportunities, optimal strategy, game price, regression equation
The issue examined in this work relates to the search for an optimal pricing strategy by an enterprise-supplier in case it faces a new competitor that offers products at a lower price. The emergence of such a problem necessitates looking for a rational way to reduce its selling price, in order to prevent losing in an aggressive competitive environment, formed by new players entering the market with proposals that are obviously better. To resolve this problem, we have developed an algorithm for selecting the winning strategies based on the estimation of strategic capabilities of a competitor under conditions of uncertainty.
It has been proposed, in order to assess the cost of a product in the system "supplier-consumer", to apply the concept of the l- level scale. It is shown that, given such a representation, it becomes possible to employ a dimensionless estimation of product pricing, regardless of its type or natural cash value. For a formalized description of relations between an enterprise- supplier and a competing company, it is proposed to use the theory of strategic games, in which a game matrix is built based on universal regression equations. A feature of the proposed solutions is that the value of winning in the game matrix is defined by solving an optimization problem based on the regression equation that describes the impact of transportation costs, profit, and a value-added tax (VAT) on the price of the game. It has been established that, given such a description, the game that is played has a saddle point with the net price of the game z=–0.5. Based on mathematical modelling, it was established that the selection of a supplier company is limited by strategies at which own profit must be close to the average or the minimally possible value.
We have constructed a predictive model for strategic opportunities of a competitor in the system "supplier-consumer", representing a universal regression equation. Based on it, an adjustment of numerical indicators for the components in product pricing can be made. It is shown that such an adjustment allows the existence of multiple alternatives, neutralizing competitor's advantages. We have substantiated constraints for the solutions derived, related to two factors: an assumption about the accuracy of determining the pricing components of a competitor, and the presence of taxation specificity in international cargo transportation.
Makarov, F. V. (1981). Puti sovershenstvovaniya planirovaniya novoy tekhniki. Voprosy sovershenstvovaniya upravleniya obshchestvennym proizvodstvom. Saratov, 180.
Murav'ev A. I. (1986). Planirovanie tekhnicheskogo razvitiya ob'edineniya. Moscow: Ekonomika, 64.
Demina, E. (1999). Formirovanie kriteriya celesoobraznosti tekhnicheskogo perevooruzheniya promyshlennogo proizvodstva. Vestnik Kharkivskoho gosudarstvennogo politekhnicheskogo universiteta. Tekhnicheskiy progress i effektivnost' proizvodstva, 95.
Oliver, K., Webber, M.; Christopher, M. (Ed.) (1982). Supply chain management: Logistics Catches up with Strategy. Logistics: The Strategy Issues. London: Chapman and Hall, 63–75.
Intrilligator, M. (2002). Matematicheskie metody optimizacii i ekonomicheskaya teoriya. Moscow: Ayris-Press, 553.
Supply Chain and Logistics Terms and Glossary (2005). Council of Supply Chain Management Professionals, 97.
Sjoerdsma, M., van Weele, A. J. (2015). Managing supplier relationships in a new product development context. Journal of Purchasing and Supply Management, 21 (3), 192–203. doi: https://doi.org/10.1016/j.pursup.2015.05.002
Zijm, H., Klumpp, M., Clausen, U., Hompel, M. (Eds.) (2016). Logistics and supply chain innovation. Springer. doi: https://doi.org/10.1007/978-3-319-22288-2
Gualandris, J., Kalchschmidt, M. (2014). Customer pressure and innovativeness: Their role in sustainable supply chain management. Journal of Purchasing and Supply Management, 20 (2), 92–103. doi: https://doi.org/10.1016/j.pursup.2014.03.001
Mentzer, J. T., DeWitt, W., Keebler, J. S., Min, S., Nix, N. W., Smith, C. D., Zacharia, Z. G. (2001). Defining Supply Chain Management. Journal of Business Logistics, 22 (2), 1–25. doi: https://doi.org/10.1002/j.2158-1592.2001.tb00001.x
Lambert, D. M. (2014). Supply Chain Management: Processes, Partnerships, Performance. Supply Chain Management Institute, 463.
Simchi-Levi, D., Kaminsky, P. (2008). Designing and managing the supply chain: concepts, strategies, and case studies. N.-Y.: McGraw-Hill Companies, 496.
Fedulova, L. (2013). Innovacionnoe razvitie: evolyuciya vzglyadov i problemy covremennogo ponimaniya. Ekonomicheskaya teoriya, 2, 28–45.
Postan, M. Ya., Malinovskiy, D. A. (2009). Model' optimal'nogo planirovaniya proizvodstva i dostavki produkcii predpriyatiya po raspredelitel'nym kanalam. Metodi ta zasobi upravlіnnya rozvitkom transportnih system, 15, 19–28.
Kurudzhi, Y., Moskvichenko, I., Postan, M. (2017). Method of finding equilibrium solutions for duopoly of supply chains taking into account the innovation activity of enterprises. Eastern-European Journal of Enterprise Technologies, 3 (4 (87)), 25–30. doi: https://doi.org/10.15587/1729-4061.2017.103989
Szyszka, G. (2004). Sieci logistyczne – nowy wymiar logistyky. Logistyki, 3, 5–7.
Bosov, A., Khalipova, N. (2017). Formation of separate optimization models for the analysis of transportation-logistics systems. Eastern-European Journal of Enterprise Technologies, 3 (3 (87)), 11–20. doi: https://doi.org/10.15587/1729-4061.2017.103220
Dorigo, M., Stutzle, T. (2004). Ant Colony Optimization. MIT Press. doi: https://doi.org/10.7551/mitpress/1290.001.0001
Lei, K., Zhu, X., Hou, J., Huang, W. (2014). Decision of Multimodal Transportation Scheme Based on Swarm Intelligence. Mathematical Problems in Engineering, 2014, 1–10. doi: https://doi.org/10.1155/2014/932832
Ramadhani, T., Hertono, G. F., Handari, B. D. (2017). An Ant Colony Optimization algorithm for solving the fixed destination multi-depot multiple traveling salesman problem with non-random parameters. AIP Conference Proceedings. doi: https://doi.org/10.1063/1.4991227
Hassan, M. R., Islam, M. M., Murase, K. (2010). A New Local Search Based Ant Colony Optimization Algorithm for Solving Combinatorial Optimization Problems. IEICE Transactions on Information and Systems, E93-D (5), 1127–1136. doi: https://doi.org/10.1587/transinf.e93.d.1127
Ashouri, M., Yousefikhoshbakht, M. (2017). A Combination of Meta-heuristic and Heuristic Algorithms for the VRP, OVRP and VRP with Simultaneous Pickup and Delivery. Brain: Broad Research in Artificial Intelligence and Neuroscience, 8 (2), 81–95.
Khalipova, N., Pasichnyk, A., Lesnikova, I., Kuzmenko, A., Kokina, M., Kutirev, V., Kushchenko, Y. (2018). Developing the method of rational trucking routing based on the modified ant algorithm. Eastern-European Journal of Enterprise Technologies, 1 (3 (91)), 68–76. doi: https://doi.org/10.15587/1729-4061.2018.123862
Konings, R., Priemus, H., Nijkamp, P. (2008) The Future of Intermodal Freight Transport. Operations, Design and Policy. Transport economics, management and policy. USA. doi: https://doi.org/10.4337/9781848441392
Hanssen, T.-E. S., Mathisen, T. A., Jørgensen, F. (2012). Generalized Transport Costs in Intermodal Freight Transport. Procedia – Social and Behavioral Sciences, 54, 189–200. doi: https://doi.org/10.1016/j.sbspro.2012.09.738
Akimova, O. V. (2014). An export and import scheme for container delivery by freight forwarding companies. Eastern-European Journal of Enterprise Technologies, 6 (3 (72)), 4–10. doi: https://doi.org/10.15587/1729-4061.2014.28862
Lysyy, A., Kotenko, V., Yakovtsev, S. (2018). Conceptual scheme for ensuring the energy efficiency principle in modern container fleet. EUREKA: Physics and Engineering, 6, 41–47. doi: https://doi.org/10.21303/2461-4262.2018.00749
Sweeney, E., Evangelista, P. (2005). 3PL definition and taxonomy. Technical Focus in Logistics Solutions. Journal of the National Institute for Transport and Logistics, 7 (2), 9–10.
Anan'ev, E. (2004). Est' u ekspeditorov zakon. Zhurnal Porty Ukrainy, 4, 15–17.
Blekuell, D., Girshik, M. A. (1958). Teoriya igr i statisticheskih resheniy. Moscow, 432.
Korshunov, Yu. M. (1980). Matematicheskie osnovy kibernetiki. Moscow: Energiya, 424.
Demin, D. (2017). Synthesis of optimal control of technological processes based on a multialternative parametric description of the final state. Eastern-European Journal of Enterprise Technologies, 3 (4 (87)), 51–63. doi: https://doi.org/10.15587/1729-4061.2017.105294
Raskin, L. G., Seraya, V. (2008). Nechetkaya matematika. Kharkiv: Parus, 352.
VIES VAT number validation. Available at: http://ec.europa.eu/taxation_customs/vies
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Copyright (c) 2018 Olena Domina, Dmitry Lunin, Olga Barabash, Olga Balynska, Yurii Paida, Liudmyla Mikhailova, Olena Niskhodovska
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