- Strategy
As the field of artificial intelligence (AI) advances rapidly, its application within the supply chain arena has seen a significant surge. This capstone project focuses on Artificial Intelligence/Machine Learning (AI/ML) predictive algorithms applied in demand forecasting, a pivotal process for managerial decisions such as inventory control and production planning. The research examines how adding exogenous variables into AI/ML predictive models can enhance demand forecasting within the fast-moving consumer goods (FMCG) industry, known for its quick product turnover. We explore different factors, including economic, health, environmental, and political influences, etc., and specifically target to Prophet model. By analyzing sales data from the Indian FMCG sector from 2011 to 2023, our study utilizes the Prophet model to assess how these target exogenous variables impact the accuracy of forecasts. The underlying hypothesis suggests incorporating these external variables into the forecasting models will significantly refine their predictive accuracy. We set up different configurations of AI/ML algorithms and by calibrating the configuration we develop the strategies and recommendations for businesses to better integrate exogenous variables into their Prophet model forecasting.