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.
