AI-Based Demand Predictive Sales and Forecast Optimization

Abstract

This project introduces an AI-driven demand forecasting system aimed at enhancing supply chain operations. Leveraging machine learning algorithms such as ARIMA and Random Forest, the platform provides an intuitive web-based interface that enables users without technical expertise to produce reliable forecasts. Testing with actual retail data demonstrated gains in inventory control, customer experience, and operational performance. Its modular design ensures flexibility for application across various industries, combining ease of use with advanced technical capabilities.

Introduction

Accurate demand forecasting is essential for effective supply chain management, but many organizations struggle with complex or costly forecasting tools. This project presents a user-friendly AI-powered web application that makes demand prediction accessible to non-technical users by providing clear visual insights and forecasts.

The solution improves forecast accuracy by analyzing historical and real-time data, adapting to variable demand, enhancing collaboration across the supply chain, and reducing costs through optimized inventory management.

Key modules include:

• Data Upload & Validation to ensure clean input,

• Data Preprocessing to correct anomalies,

• A Forecasting Engine using ARIMA, Random Forest, or LSTM models with automatic tuning,

• Visualization tools for interactive data analysis,

• A web interface built with Flask for easy user interaction,

• Error Handling and Logging for smooth operation.

The platform demonstrated improved demand predictions and inventory control in retail settings, reducing stockouts and overstock, enhancing customer satisfaction, and increasing operational efficiency compared to traditional forecasting methods.

Conclusion

Our AI-based forecasting system significantly enhances the accuracy and agility of supply chain processes. Unlike conventional techniques, it dynamically adjusts to real-time data and changing market conditions. The user-friendly interface is designed for accessibility, even for those without technical expertise, while the modular architecture allows for seamless customization. Tangible results include fewer stock shortages, more efficient inventory handling, and enhanced customer satisfaction.

References

[1] Cogent Info. (2024). Forecasting Demand with AI Technology. Retrieved from www.cogentinfo.com [2] Sharma, S. (2025). Boosting Supply Chain Responsiveness with AI Forecasting Tools. Tntra Blog. [3] Adapa, S.R. (2024). Improving Supply Chain Performance via AI-Based Demand Prediction. Library Progress International, 44(3). [4] Vasudevan, R. (2024). Transforming Supply Chains Using AI Forecasting Techniques. LinkedIn Pulse. [5] Agrawal, N., et al. (2024). The Impact of Machine Learning on the Evolution of Supply Chain Management. Harvard Business Review.

Copyright

Copyright © 2025 Karthikeyan S, Saravanan P. 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.