Optimizing Saree Retail Inventory Through Predictive Demand Forecasting

The Indian saree industry, characterized by its rich cultural significance and diverse demand patterns, encounters challenges in inventory management stemming from fluctuating consumer preferences, seasonal trends, and various external factors. Online saree retailers like Vannaval Pvt Ltd require effective inventory management to ensure customer satisfaction while minimizing losses from overstocking or stock-outs. This research aims to develop a predictive analytics report to forecast future saree demand by utilizing historical sales data and considering external elements such as seasonality, cultural events, and fashion trends. The goal is to provide accurate predictions that enable retailers to proactively manage inventory levels, Thereby reducing stock-outs and preventing overstocking. The study employs a quantitative, Predictive analytics-based approach. It involves analyzing historical sales data and external factors using techniques like regression analysis, time series analysis, and exploratory data analysis. Data was also collected via a questionnaire administered to 200 respondents, selected using stratified random sampling from a larger dataset. Tools such as Excel, SPSS, and Power BI were utilized for data processing and analysis. The analysis revealed that the largest respondent age group is “41-50> (24.0%). Saree sales peak in summer (36.5%) and during Q1 (Jan-Mar) and Q2 (Apr-Jun) (35.5% each). Cotton sarees are in highest demand (22.5%), and Designer/embroidered styles are most preferred (23.5%). A significant majority (80.0%) confirmed that fashion trends impact demand, and 41.0% of consumers are deterred by high prices.