DEVELOPMENT OF MATHEMATICAL MODELS FOR EVALUATING DEMAND PARAMETER ELASTICITY IN RETAIL NETWORKS UNDER CONSUMER-DRIVEN LOGISTICS
DOI:
https://doi.org/10.33042/2522-1809-2024-4-185-262-266Keywords:
demand elasticity, retail network, logistics, mathematical modelling, demand parameters, consumer behaviourAbstract
The retail market is characterised by its dynamic nature, requiring sophisticated models for accurately assessing demand elasticity, especially within consumer-driven logistics. This study aims to develop and validate comprehensive mathematical models to evaluate demand parameters within retail networks. The study focuses on the main factors that significantly influence demand, including the number of participants within a retail network, the average number of end consumers, and the ratio of the freight flow cost in a particular retail network compared to the average price in other networks.
The authors conducted extensive data collection across various retail networks, capturing essential parameters such as demand volume, the number of participants, consumer numbers, and pricing strategies. The analysis led to the development of regression models that provide valuable insights into demand dynamics. The results indicate that increasing network participants and the average number of end consumers positively correlates with higher demand volumes. On the other hand, a higher ratio of freight flow cost within a retail network negatively impacts demand, highlighting consumers’ sensitivity to price changes. This inverse relationship between cost and demand underlines the importance of pricing strategies in influencing consumer behaviour.
The statistical validation of the developed models demonstrated their reliability, with high correlation coefficients and low approximation errors, confirming their high predictive capabilities. These models are not only theoretically sound but also offer substantial practical applications. Retail networks can leverage these models to optimise their marketing strategies, plan their product assortments more effectively, and manage inventory more precisely. By integrating multiple factors influencing demand, this study provides a more nuanced understanding of consumer behaviour, enabling retail networks to make well-informed, data-driven decisions.
The unique contribution of this research lies in its holistic approach to demand modelling, where multiple variables are considered in conjunction rather than in isolation. This integration allows for a deeper comprehension of how different elements interact to influence consumer demand. Moreover, the models developed in this study are versatile and can be adapted to various retail settings, offering a valuable tool for academic researchers and industry practitioners.
Future research could extend these models by incorporating additional variables such as seasonality, shifts in consumer preferences, and the impact of technological advancements on retail logistics. Doing so makes it possible to continuously refine the models to maintain relevance and accuracy in an ever-changing market landscape. This ongoing evolution of demand modelling is crucial for retail networks aiming to stay competitive and responsive to consumer needs in a highly dynamic environment. The findings from this study underscore the importance of a data-driven approach in retail logistics, where precise modelling and analysis can lead to significant improvements in operational efficiency and market responsiveness.
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