Analyze current demand forecasting practices to understand how machine learning models can improve prediction accuracy and reduce planning errors in business operations.
Evaluate the role of external data factors such as weather, market trends, holidays, and economic indicators in influencing demand patterns.
Examine different machine learning techniques for forecasting demand, focusing on their effectiveness in handling large and dynamic datasets.
Assess the limitations of traditional forecasting methods compared with data-driven predictive models in modern business environments.
Investigate how data quality, feature selection, and preprocessing impact forecasting reliability and model performance.
Study traditional vs. advanced demand forecasting techniques.
Identify external factors influencing demand such as weather, inflation, and festivals.
Understand machine learning models suitable for multivariate forecasting.
Collect datasets including sales data and external variables.
Perform data integration and preprocessing.
Conduct exploratory analysis to identify relationships between variables.
Apply machine learning models like multiple regression and random forest.
Evaluate model accuracy and interpret results.
Analyze the impact of external variables on demand patterns.
Use Power BI or Excel to visualize insights.
Develop a forecasting model incorporating multiple influencing factors.
Compare results with models using only historical data.
Suggest strategies to improve forecast reliability.
Provide actionable recommendations for businesses to leverage external data in forecasting.
