AI-Driven Demand Forecasting System for Accurate Inventory and Production Planning

Problem: Traditional demand forecasting methods often fail due to market fluctuations, leading to overstocking or stockouts.

Outcome: Develop an AI-based model that improves demand forecasting accuracy for better inventory and production planning.

Week 1-2: Industry Research & Data Collection

Study demand forecasting techniques.

Collect historical sales and demand data.

Week 3-4: AI Model Selection & Data Preprocessing

Select suitable AI/ML models (LSTM, XGBoost, etc.).

Clean and preprocess data for training.

Week 5-6: Model Training & Testing

Train an AI model using past demand data.

Test performance with different datasets.

Week 7-8: Model Integration & Dashboard Development

Develop a dashboard for real-time forecasting.

Integrate with ERP or inventory management software.

Week 9-10: Validation & Optimization

Compare AI forecasts with actual demand trends.

Optimize the model based on business needs.

Week 11-12: Report & Final Presentation

Document findings, model performance, and business impact.

Present to operations and supply chain teams.