Abstract:
In the competitive realm of e-commerce, optimizing pricing strategies within supply chain man agement is essential for maximizing revenue while remaining competitive. This study provides a
thorough analysis of pricing strategies using real-world data from various sources, with a focus on the
Indian e-commerce landscape. We develop and apply mathematical optimization models to enhance
pricing decisions by incorporating supply chain performance metrics, transaction data, merchant
category classifications, and payment service provider (PSP) performance. The study demonstrates
how both dummy values and actual data can be utilized to formulate and solve these models, offering
insights into optimization under diverse market conditions.
To understand the impact of variable changes on optimal pricing strategies, sensitivity analysis is
employed. This approach helps identify how fluctuations in parameters affect pricing decisions and
overall supply chain performance. The research emphasizes the importance of data-driven methods
in refining pricing strategies within e-commerce. By applying mathematical models and analyzing
real-world data, we provide actionable recommendations for improving decision-making processes
in supply chain management. Our findings suggest that leveraging optimization techniques and
incorporating performance metrics can significantly enhance revenue and competitive positioning.
This study not only underscores the practical benefits of data-driven optimization but also serves
as a valuable resource for businesses seeking to advance their pricing strategies in the dynamic e commerce environment. Through these insights, companies can achieve better alignment between
pricing strategies and supply chain performance, leading to improved financial outcomes and opera tional efficiency.
