Forecast sharing has been widely adopted to coordinate capacity planning in supply chains. However, the effectiveness of forecast sharing can be hindered by forecast inflation caused by the forecaster's asymmetric preferences toward underforecasts or overforecasts. In this paper, we study how...

Black-box algorithms with outstanding performance have been widely used in various fields; however, the lack of interpretability leads to great difficulties in troubleshooting and model improvement, hence severely confining the practical application. In response, we propose a framework that...

Delivery time promising is critical to managing customer expectations and improving customer satisfaction. Simply over-promising or under-promising is undesirable due to their negative impacts on short-term/long-term sales. Notably, we are the first to develop a data-driven framework to predict...

In time series forecasting, decomposition-based algorithms break aggregate data into meaningful components and are therefore appreciated for their particular advantages in interpretability. Recent algorithms often combine machine learning (hereafter ML) methodology with decomposition to improve...

New products are highly valued by manufacturers and retailers due to their vital role in revenue generation. Product life cycle curves often vary by their shapes and are complicated by promotional activities that induce spiky and irregular behaviors. We collaborate with JD.com to develop a...