Improving linear programming model for assessing product sales taking into account social responsibility of consumers

Abstract

The object of this study is the process of analyzing the volume of product sales in the retail market, where producers and consumers with different levels of social responsibility operate. Advancements are aimed at solving the problem of insufficient consideration of the social responsibility of economic entities in modern economic and mathematical models. In the work, the improvement of the linear programming model, intended for the evaluation of sales of alternative variants of goods with different social utility to consumers with different social responsibility, by taking into account some real conditions of making purchases, was carried out. As a result of the study, a modification of the model was proposed that makes it possible to take into account the behavior of buyers who choose a product randomly. Also, with the help of additional restrictions, the model takes into account the fact that consumers with different social responsibility make purchases in parallel in time. In addition, the approach to identifying a set of consumer groups in the aspect of social responsibility using the method of questionnaires of the ranking type and building a decision tree of consumers is described. The proposed modification of the model was tested on the example of evaluating the sales volumes of environmentally-friendly and non-environmentally-friendly varieties of goods in the office paper market. It is shown that not taking into account the added restrictions in the model could significantly affect the estimates of sales volumes. The proposed improvements to the model make it possible to obtain more realistic estimates of the volume of sales of goods, taking into account the social responsibility of consumers, as well as quantitative and qualitative characteristics of competitors' offers. This contributes to better decision-making support for manufacturing socially useful products, in particular, environmentally-friendly ones.