PeakPlanner: AI-Based Market Basket Insights for Demand Forecasting

Abstract

PeakPlanner is an intelligent business analytics platform designed to assist organizations in sales trend forecasting and inventory optimization through data-driven insights. This system integrates data upload, preprocessing, and predictive modeling into a seamless pipeline using a PHP-based frontend and backend coupled with Python-based forecasting algorithms. It dynamically creates user-specific databases, organizes data month-wise, and utilizes Random Forest models for accurate demand prediction. Additionally, it generates insightful reports and visual dashboards that highlight business performance and growth opportunities. The platform offers an intuitive interface for uploading CSV data, automates model training and evaluation, and provides marketing strategy recommendations based on observed trends. This paper presents the architectural design, technical implementation, and practical implications of PeakPlanner in real-time business environments, demonstrating its potential to enhance decision-making through machine learning and data visualization.

Introduction

Demand forecasting is crucial for modern inventory and supply chain management but traditional models often fall short due to complex retail data. Advances in AI and machine learning, especially Random Forest algorithms, have significantly improved forecasting accuracy and operational efficiency. Several studies demonstrate the benefits of AI-driven forecasting in reducing stockouts, optimizing inventory costs, and enhancing customer satisfaction.

Existing business forecasting tools, including ERP systems, POS analytics, business intelligence platforms, and spreadsheet methods, have limitations like high cost, complexity, or lack of AI integration, especially for small and medium enterprises.

To address these gaps, PeakPlanner was developed as a comprehensive, user-friendly web platform integrating Random Forest-based demand forecasting, dynamic database management, and interactive dashboards. Built on a WAMP server with PHP backend and Python ML scripts, it securely manages user data and provides real-time sales predictions from historical data.

The system includes modules for secure user login, data upload, visualization of sales trends, and demand prediction. Evaluations show high forecast accuracy (over 90% in stable scenarios), effective visualization, and positive user feedback on usability. However, challenges remain with fluctuating or sparse data, and scalability under large datasets needs improvement.

Conclusion

PeakPlanner marks a significant leap in AI-powered demand forecasting and business analytics by combining predictive modelling with real-time sales insights and customer behaviour trends. Its use of Random Forest algorithms, dynamic database management, and category-based strategies offers businesses a data-driven edge in inventory planning and marketing. While effective, the platform can be further enhanced by refining predictive accuracy, integrating external variables like seasonal or economic factors, and improving report generation speed. Future development will focus on real-time data processing, mobile accessibility, AI-based anomaly detection, and advanced interactive dashboards to deliver faster, smarter, and more actionable insights. These improvements will elevate PeakPlanner into a comprehensive, scalable solution for strategic decision-making and business growth.

References

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Copyright

Copyright © 2025 Hasan Phudinawala, Pratiksha Manjrekar, Maanya Mendon. 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.