Scientific and methodological approaches to modeling the optimal strategy for increasing the competitiveness of pharmacy chains of different sizes

Authors

DOI:

https://doi.org/10.15587/2519-4852.2021.239389

Keywords:

strategy, competitiveness, pharmacy chains, decision tree, clusters

Abstract

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

Author Biographies

Iryna Bondarieva, National University of Pharmacy

PhD, Associate Professor

Department of Pharmaceutical Management and Marketing

Volodymyr Malyi, National University of Pharmacy

Doctor of Pharmaceutical Sciences, Professor

Department of Pharmaceutical Management and Marketing

Olga Posilkina, National University of Pharmacy

Doctor of Pharmaceutical Sciences, Professor, PhD

Department of Management, Economics and Quality Assurance in Pharmacy

Zhanna Mala, National University of Pharmacy

PhD

Department of Pharmaceutical Management and Marketing

Maryna Nessonova, National University of Pharmacy

PhD, Associate Professor

Department of Educational and Information Technology

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Published

2021-08-31

How to Cite

Bondarieva, I., Malyi, V., Posilkina, O., Mala, Z., & Nessonova, M. (2021). Scientific and methodological approaches to modeling the optimal strategy for increasing the competitiveness of pharmacy chains of different sizes. ScienceRise: Pharmaceutical Science, (4(32), 59–66. https://doi.org/10.15587/2519-4852.2021.239389

Issue

No. 4(32) (2021)

Section

Pharmaceutical Science

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Copyright (c) 2021 Iryna Bondarieva, Volodymyr Malyi, Olga Posilkina, Zhanna Mala, Maryna Nessonova

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