Authors : Yogesh Thakur; Arikatla Sai Sumedha; Karthik Goalla; Praveen Kumar Burra; Bharani Kumar Depuru

Volume/Issue : Volume 10 - 2025, Issue 6 - June

Google Scholar : https://tinyurl.com/3dbyfscj

DOI : https://doi.org/10.38124/ijisrt/25jun757

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : This research presents a comprehensive data-driven approach to apparel sales forecasting designed to address the critical inventory management challenges faced by multi-outlet retail businesses. The client operates numerous business outlets across the country, frequently encountering inventory inefficiencies including overstock situations and stockouts that impact profitability and customer satisfaction. Historical sales data was analyzed to categorize materials into fast-moving, medium-moving, and slow-moving segments, providing strategic inventory classification essential for targeted management approaches. Extensive exploratory data analysis (EDA) was conducted at the outlet level to identify performance patterns, revealing which locations demonstrated maximum and minimum sales volumes along with the underlying causal factors. This outlet-specific intelligence provided crucial context for subsequent modeling efforts. Comprehensive data preprocessing techniques were applied to the one-year historical dataset provided by the client, ensuring data quality and model readiness. Following the CRISP-ML(Q) methodology, multiple forecasting approaches were evaluated. Initial time series analysis included seasonal decomposition, stationarity testing, and ACF/PACF plots to inform traditional ARIMA and SARIMA models. However, when these models failed to achieve sufficient accuracy, the research pivoted to advanced machine learning techniques capable of capturing nonlinear relationships in the data. Random Forest and XGBoost models were developed and rigorously tested, with Random Forest ultimately selected as the superior performer. The model was fine- tuned through hyperparameter optimization using RandomSearchCV to maximize prediction accuracy. To operationalize the solution, a Streamlit-based web application was developed, enabling business users to generate weekly sales forecasts by selecting specific materials and desired date ranges. The system displays predicted sales figures alongside confidence intervals to guide inventory planning decisions. Additionally, the application features comprehensive activity logs that track daily sales performance, identify trends, and highlight the best and worst-performing outlets, providing management with actionable business intelligence for ground-level operational decisions. This forecasting system empowers the client with data-driven inventory management capabilities, reducing both excess inventory costs and lost sales opportunities while providing unprecedented visibility into operational performance across their retail network.

Keywords : Apparel Sales Forecasting, Inventory Optimization, Machine Learning, Random Forest, Time Series Analysis, Business Intelligence, Streamlit Application, Predictive Modeling, Multi-outlet Retail, Data-Driven Decision Making.

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