Scientific and methodological approaches to modeling the optimal strategy for increasing the competitiveness of pharmacy chains of different sizes
Keywords:strategy, competitiveness, pharmacy chains, decision tree, clusters
The aim of the work is to develop scientific and methodological approaches to modelling the optimal strategy to increase the competitiveness of pharmacy chains (PC), which belong to different clusters.
Materials and methods. The algorithm for determining the optimal strategy for increasing the competitiveness of PC for different clusters using the method of constructing a decision tree and cluster analysis is proposed. To solve this problem, an expert survey of more than 400 pharmacy managers, who were part of the PC of different sizes, was previously conducted. According to the results of an expert survey using hierarchical clustering methods based on the values of 13 input variables - scores of the strengths of the competitiveness of the PC, three clusters of networks were identified, each of which proposed its own algorithm for modelling the optimal strategy of competitiveness.
Results. Using modern economic and mathematical tools, the distribution of PC depending on their size into clusters for modelling the dynamics of competitiveness is substantiated. Indicators are identified, which show a significant difference between clusters, which was taken into account in the process of modelling and selection of the optimal strategy to increase the competitiveness of PC. It is established that the biggest negative impact on the strategy of increasing the competitiveness of small networks has a slow response to changes in market conditions, the biggest positive impact – the availability of additional services in the networks; for medium PC the most important factors influencing the level of competitiveness are the location of pharmacies and competent management; for large PC – the use of modern automated management programs, the level of efficiency of the marketing complex and location features.
The algorithm of the generalized model of “decision tree” for a choice of optimum strategy of increase of competitiveness depending on the size of PC is constructed. It was found that the following factors are of the greatest importance: the size of the PC, the use of the discount card system, and the least - the speed of response to market changes and the stability of the financial condition.
Conclusions. The proposed generalized mathematical model of the “decision tree” allows a reasonable approach to choosing the optimal strategy to increase the competitiveness of PC depending on its size. The assessment of the importance of predictor variables for each cluster of PC allows determining the priority factors in the implementation of measures aimed at implementing the chosen strategy to increase competitiveness
- Mala, Z. V., Posylkina, O. V., Nessonova, M. M. (2017). Methodological approaches to the analysis and assessment of marketing competitive advantages of pharmacy networks. Social Pharmacy in Health Care, 3 (1), 41–51. doi: http://doi.org/10.24959/sphhcj.17.67
- Medina, L. A., Kremer, G. E. O., Wysk, R. A. (2013). Supporting medical device development: a standard product design process model. Journal of Engineering Design, 24 (2), 83–119. doi: http://doi.org/10.1080/09544828.2012.676635
- Ushakova, I. A., Dorokhova, L. P., Malyi, V. V., Dorokhov, A. V. (2020). Assessment of a pharmacy as a pharmaceutical service environment. Azerbaijan Pharmaceutical and Pharmacotherapy Journal, 20 (1), 24–30.
- Niziolek, L., Chiam, T. C., Yih, Y. (2012). A simulation-based study of distribution strategies for pharmaceutical supply chains. IIE Transactions on Healthcare Systems Engineering, 2 (3), 181–189. doi: http://doi.org/10.1080/19488300.2012.709583
- Settanni, E., Harrington, T. S., Srai, J. S. (2017). Pharmaceutical supply chain models: A synthesis from a systems view of operations research. Operations Research Perspectives, 4, 74–95. doi: http://doi.org/10.1016/j.orp.2017.05.002
- Harrington, T. S., Phillips, M. A., Srai, J. S. (2016). Reconfiguring global pharmaceutical value networks through targeted technology interventions. International Journal of Production Research, 55 (5), 1471–1487. doi: http://doi.org/10.1080/00207543.2016.1221541
- Mehralian, G., Zarenezhad, F., Rajabzadeh Ghatari, A. (2015). Developing a model for an agile supply chain in pharmaceutical industry. International Journal of Pharmaceutical and Healthcare Marketing, 9 (1), 74–91. doi: http://doi.org/10.1108/ijphm-09-2013-0050
- Will M. Bertrand, J., Fransoo, J. C. (2002). Operations management research methodologies using quantitative modeling. International Journal of Operations & Production Management, 22 (2), 241–264. doi: http://doi.org/10.1108/01443570210414338
- Brandenburg, M., Govindan, K., Sarkis, J., Seuring, S. (2014). Quantitative models for sustainable supply chain management: Developments and directions. European Journal of Operational Research, 233 (2), 299–312. doi: http://doi.org/10.1016/j.ejor.2013.09.032
- Narayana, S. A., Kumar Pati, R., Vrat, P. (2014). Managerial research on the pharmaceutical supply chain – A critical review and some insights for future directions. Journal of Purchasing and Supply Management, 20 (1), 18–40. doi: http://doi.org/10.1016/j.pursup.2013.09.001
- Chung, S. H., Kwon, C. (2016). Integrated supply chain management for perishable products: Dynamics and oligopolistic competition perspectives with application to pharmaceuticals. International Journal of Production Economics, 179, 117–129. doi: http://doi.org/10.1016/j.ijpe.2016.05.021
- Posilkina, O., Bondarieva, I., Malyi, V., Timanyuk, I., Mala, Z. (2021). Peculiarities of effective management of products assortment depending on different sizes of pharmacy chains. ScienceRise: Pharmaceutical Science, 2 (30), 55–63. doi: http://doi.org/10.15587/2519-4852.2021.230287
- Ahmadiani, S., Nikfar, S. (2016). Challenges of access to medicine and the responsibility of pharmaceutical companies: a legal perspective. DARU Journal of Pharmaceutical Sciences, 24, 124–130. doi: http://doi.org/10.1186/s40199-016-0151-z
- Ranyard, J. C., Fildes, R., Hu, T.-I. (2015). Reassessing the scope of OR practice: The Influences of Problem Structuring Methods and the Analytics Movement. European Journal of Operational Research, 245 (1), 1–13. doi: http://doi.org/10.1016/j.ejor.2015.01.058
- Saghiri, S., Wilding, R., Mena, C., Bourlakis, M. (2017). Toward a three-dimensional framework for omni-channel. Journal of Business Research, 77, 53–67. doi: http://doi.org/10.1016/j.jbusres.2017.03.025
- Liu, J., Gong, Y. (Yale), Zhu, J., Zhang, J. (2018). A DEA-based approach for competitive environment analysis in global operations strategies. International Journal of Production Economics, 203, 110–123. doi: http://doi.org/10.1016/j.ijpe.2018.05.029
- Zaki, M. J., Wagner, M. Jr. (2020). Data Mining and Machine Learning: Fundamental Concepts and Algorithms. Cambridge University Press. Available at: https://dataminingbook.info/book_html/
- Lyman, R. O., Longnecker, M. (2010). An Introduction to Statistical Methods and Data Analysis. Brooks/Cole: Cengage Learning, 1296.
- Masoumi, A. H., Yu, M., Nagurney, A. (2012). A supply chain generalized network oligopoly model for pharmaceuticals under brand differentiation and perishability. Transportation Research Part E: Logistics and Transportation Review, 48 (4), 762–780. doi: http://doi.org/10.1016/j.tre.2012.01.001
- Nagurney, A., Nagurney, Li, L. S. (2013). Pharmaceutical supply chain networks with outsourcing under price and quality competition. International Transactions in Operational Research, 20 (6), 859–888. doi: http://doi.org/10.1111/itor.12031
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Copyright (c) 2021 Iryna Bondarieva, Volodymyr Malyi, Olga Posilkina, Zhanna Mala, Maryna Nessonova
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