AI-Driven Smart Retail Shop Management and Sales Forecasting System

Abstract

Reliable sales forecast is essential for effective retail planning and inventory control. This study evaluates and compare machine learning models for retail sales prediction. Using [dataset name and source], the study applies [models you actually implement] to determine a suitable forecasting method. The data are prepared through [your preprocessing methods], while model performance is assessed using [metrics, such as MAE, RMSE, and R²]. Results indicate that [insert your measured findings and best-performing model]. Based on these findings, the study examines how well the evaluated models support retail sales forecasting and data-driven decision-making.

Introduction

The paper presents an AI-Driven Smart Retail Inventory Replenishment System designed to improve inventory management in retail businesses by identifying products that need restocking, reducing stockouts, and maintaining adequate product availability.

Traditional inventory management often relies on manual stock tracking and fixed reorder points, which may not respond effectively to changing customer demand. The proposed system uses historical sales data, current inventory levels, product demand, and reorder thresholds to support more accurate and timely replenishment decisions.

The main objectives of the study are to:

• Monitor product stock quantities and availability.

• Identify products that require immediate replenishment.

• Reduce stockouts and excess inventory.

• Analyze historical sales and product demand.

• Generate restocking alerts and recommendations.

• Improve inventory control and retail operational efficiency.

The proposed system follows several stages:

1. Data Collection: Collects product details, historical sales, current stock quantities, and supplier lead-time information.

2. Data Preprocessing: Handles missing values, duplicate records, inconsistent product identifiers, and incorrect data formats.

3. Inventory Analysis: Evaluates stock levels and product sales movement to identify low-stock and adequately stocked products.

4. Reorder Level Identification: Establishes reorder thresholds and generates alerts when stock reaches or falls below the specified level.

5. Replenishment Decision-Making: Recommends restocking quantities based on inventory requirements and predefined stock targets.

6. Performance Evaluation: Assesses the system using stockout frequency, inventory turnover, replenishment accuracy, and urgent restocking requirements.

The implementation of the AI-Driven Smart Retail Shop combines sales analysis, product identification, sales forecasting, inventory assessment, correlation analysis, and Power BI visualization.

The system includes three major analytical features:

• Sales Trend and Top-Selling Products Analysis: Displays recent sales trends and identifies the best-selling products to support inventory planning.

• Sales Forecasting and Inventory Breakdown: Predicts sales for the next seven days and displays stock distribution to help retailers plan future replenishment.

• Correlation Matrix Analysis: Examines relationships between numerical variables to identify patterns and support better data analysis and predictive modelling.

The literature review highlights the importance of safety-stock management, data-driven retail operations, inventory performance monitoring, and product availability. The research gap focuses on developing a practical replenishment approach that combines stock levels, historical product movement, and reorder thresholds.

Conclusion

Retail businesses depend on effective inventory management to ensure product availability, minimize stock shortages, and sustain efficient operations. This paper presents an intelligent inventory replenishment method that evaluates existing stock quantities, past product movement, and established reorder thresholds to determine when replenishment is needed. Through prompt alerts and restocking recommendations, the approach is intended to improve inventory oversight and lower the likelihood of stockouts. Its effectiveness can be evaluated using inventory measures such as stockout frequency, inventory turnover, and replenishment accuracy. Future improvements may include supplier lead-time information, safety-stock calculations, and changing demand trends. Overall, the proposed method offers a basis for data-driven inventory planning and better decision-making in retail settings.

References

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Copyright

Copyright © 2026 Poorvi S., Mr. DineshKumar M., Ananya G., M S Pranathi Patavardhan, Rachana SN. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.