MODELING OF OPTIMAL PORTFOLIO OF CLIENTS OF CENTRALIZED PHARMACEUTICAL NETWORK

The research object identifies the risk management of a centralized pharmacy network related to marketing relationships between the network and different customer groups. The development of a competitive market leads to the need for pharmacy networks to engage the customer into a dialogue, giving him/her certain benefits, thereby reducing their own risks. The subject of the research is the modelling the optimal client portfolio of a centralized pharmacy network as one of the risk management tools. Based on the fundamentals of Markowitz portfolio theory and multicriteria optimization, this paper builds a basic model of an optimal portfolio of clients of centralized pharmacy network, which takes into account three groups of customers – loyal, casual and online orders. In contrast to the classic two-criteria model (risk minimization while maximizing income), the model has been introduced to maximize entropy, which enhances the diversification effect. Four modifications of the basic model are also considered. The first of these deepens on the analysis of the portfolio of clients of the network to the individual pharmacies belonging to this network. The following three model different marketing strategies in which one customer group is preferred. Matlab software has been developed to solve many of the Pareto-optimal client portfolios for solving multicriteria-based problem-solving models. Model verification was performed on real data provided by one of the pharmacy chains. Modelling the optimal customer portfolios of a centralized pharmacy network eliminates the deficiencies in network management and selects the optimal combination of loyal, casual, and online client group distribution. This provides the opportunity to influence these consumer groups by implementing appropriate loyalty programs, which ultimately leads to higher profits. The simulation results will be useful for automating the business processes of any trading network, managing risk, analyzing loyalty programs to improve the effectiveness of their operations