Analyze machine learning-based demand forecasting methods to understand their role in improving retail inventory planning and stock availability.
Evaluate historical sales patterns, seasonal trends, and consumer buying behavior influencing retail demand across product categories.
Examine the effectiveness of machine learning models in predicting future demand more accurately than traditional forecasting approaches.
Assess the limitations of conventional inventory planning methods compared with predictive analytics-driven retail systems.
Investigate the influence of external factors such as promotions, holidays, pricing changes, and market trends on retail demand fluctuations.
Study the concept of demand forecasting and its importance in retail and supply chain management.
Identify challenges in traditional forecasting methods such as seasonality and demand variability.
Understand the basics of machine learning and predictive analytics.
Collect historical sales data and relevant external variables (season, promotions, holidays).
Perform data cleaning, preprocessing, and feature selection.
Apply machine learning models such as linear regression, decision trees, and time-series forecasting.
Compare the performance of different models using accuracy metrics like MAE and RMSE.
Analyze demand patterns and identify trends or anomalies.
Use tools like Python, Excel, or Power BI for analysis and visualization.
Develop dashboards to track demand forecasts and inventory levels.
Evaluate the impact of accurate forecasting on inventory costs and service levels.
Suggest strategies for inventory optimization using forecasting insights.
Identify limitations such as data quality and model assumptions.
Provide actionable recommendations to improve forecasting systems in retail businesses.
