The primary goal of this project is to analyze the role of demand forecasting in optimizing inventory management and reducing costs. The project will focus on improving forecasting accuracy to minimize stock-outs, overstocking, and supply chain inefficiencies.
Stock-outs, resulting in lost sales and dissatisfied customers.
Overstocking, increasing storage costs and product obsolescence.
Inefficient supply chain planning, causing delays and excess holding costs.
Poor cash flow management, due to excessive inventory investments.
Analyze current forecasting methods – Understand the impact of forecasting errors on inventory levels.
Evaluate historical demand patterns – Identify trends, seasonality, and demand fluctuations.
Apply forecasting models – Use statistical and AI-based methods (Time Series, ARIMA, Machine Learning) for improved predictions.
Optimize inventory management – Recommend strategies like Just-in-Time (JIT), Economic Order Quantity (EOQ), and safety stock calculations.
Develop a demand-driven inventory framework – Propose a structured approach to enhance efficiency, reduce costs, and improve supply chain performance.
Conduct a literature review on demand forecasting techniques and inventory control methods.
Study real-world case studies of demand forecasting failures and successes.
Identify key challenges in balancing supply and demand.
Collect historical sales and inventory data from a company/industry (if available).
Identify demand patterns, seasonality, and trends using data analytics.
Evaluate the impact of forecasting errors on inventory levels and costs.
Implement different forecasting models.
Compare the accuracy of forecasting models using error metrics.
Propose various inventory optimization techniques.
Suggest technology adoption (ERP, AI, real-time tracking) for better forecasting and inventory management.
Compile findings into a detailed project report.
Develop a presentation summarizing key insights, forecasting models, and inventory strategies.
Present findings to faculty/industry mentors for feedback.
