1.PHARMACEUTICAL E-COMMERCE SALES FORECASTING USING GM(1,1) AND SVR REVIEW

The pharmaceutical e -commerce industry has rapidly expanded due to digitalization and increased Online healthcare purchases are preferred by consumers. In this industry, demand planning, inventory control, and customer happiness all depend on accurate sales forecasting. The Grey Model (GM(1,1)) and Support Vector Regr ession (SVR) are combined in this project's hybrid predictive model to forecast pharmaceutical e- commerce sales effectively. The GM(1,1) model is employed to identify short -term patterns in limited datasets, while SVR refines predictions by capturing non-linear relationships in sales behavior. The hybrid model leverages the strengths of both statistical and machine learning approaches, offering improved accuracy and adaptability compared to individual models. Experimental simulations using monthly sales data demonstrate that the GM(1,1) - SVR model significantly reduces forecasting errors. This approach helps businesses in optimizing stock control , minimizing wastage of medicines, and improving supply chain decisions. The proposed model can serve as a reliable decision -support tool for pharmaceutical e-commerce companies aiming to enhance operational efficiency and responsiveness to market dynamics .