Authors:
Shane McGorty, Rayna Yang
MIT Supply Chain Management Program (SCM)
40
Summary:
Traditional demand forecasting methods often struggle to accurately predict fluctuations in omnichannel consumer behavior, posing challenges in meeting consumer demand. This research aims to develop a methodology for assessing the contribution of exogenous variables to forecasting models, with the goal of improving their accuracy. Powered by machine learning and techniques based on information theory, this study seeks to identify significant leading indicators and their optimal lead/lag effects, enabling better operational decision-making for Tempur-Sealy International.
