Article
STORE STOCKS AND SALES ANALYSIS AND PREDICTION
The Store Stock and Sales Analysis and Prediction system is designed to monitor, analyze, and predict store inventory and sales performance using data analytics and machine-learning techniques. Effective stock management is essential for maintaining the right quantity of products, avoiding stock shortages, reducing excess inventory, and improving overall store performance. The proposed system collects historical information such as product name, category, stock quantity, sales quantity, selling price, purchase price, sales date, revenue, and store or region. The collected data is cleaned and preprocessed to handle missing values, duplicate records, and inconsistent information. Exploratory data analysis is then performed to identify sales trends, fast-moving products, slow-moving products, seasonal demand, and inventory patterns. Machine-learning and forecasting algorithms can be applied to historical sales data to predict future product demand and sales. The system can estimate the required stock levels for upcoming periods and help identify products that may require restocking. Prediction models can be evaluated using metrics such as MAE, RMSE, MAPE, and R² to measure their performance. An interactive dashboard can display important information such as total sales, current stock, revenue, top-selling products, low-stock products, sales trends, predicted demand, and inventory status. These insights enable store managers to make better decisions regarding purchasing, inventory planning, product availability, and sales strategies.
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