AI-Driven Demand Forecasting and Dynamic Pricing System for Indian Retailers

Objective: To design, build, and deploy a machine-learning engine that predicts retail inventory demand and optimizes real-time pricing to maximize profitability and reduce wastage for Indian retail SMEs.

Retailers in India, especially small and medium enterprises (SMEs), face significant challenges in accurately predicting product demand and dynamically adjusting prices in response to fluctuating market conditions, leading to overstocking, wastage, or missed revenue opportunities. This problem affects both traditional kirana stores and organized retail chains, resulting in financial losses and inefficient inventory management.

The proposed solution is an AI-driven demand forecasting and dynamic pricing engine integrated with a user-friendly dashboard. The system will leverage historical sales data, seasonal trends, local events, and external factors (such as weather or festivals) to predict inventory demand using advanced machine learning models (e.g., LSTM, Prophet) and recommend optimal pricing using reinforcement learning and real-time analytics.

Key deliverables include: (1) a scalable backend with trained forecasting and pricing models, (2) a responsive web app/dashboard for inventory managers, (3) integration with sample POS data (using mock or real datasets like IndiaRetailSales or Kaggle's Indian Retail Dataset), (4) real-time simulation and visualization of forecasts and dynamic price recommendations, and (5) a cost-benefit analysis tailored to Indian SME contexts. The working model will be demonstrated live to the exam panel with test-case scenarios and a deployable codebase.

This system empowers Indian retailers with data-driven decision-making, reduces wastage, increases revenue, and provides a scalable template for digital transformation in the retail sector. Its modular design supports integration with existing POS systems, making it suitable for rapid adoption across various urban and rural settings.