Forecasting has always been at the forefront of decision making and planning. The
uncertainty that surrounds the future is both exciting and challenging, with individuals and …

When training or evaluating deep learning models, two essential parts are picking the
proper loss function and deciding on performance metrics. In this paper, we provide a …

Workforce analytics involves using models that integrate internal and external data to predict
future workforce and help organizations in any industry examine factors that have a …

Overnight forecasting is a crucial challenge for revenue managers because of the
uncertainty associated between demand and supply. However, there is limited research that …

The effective management of call centers is a challenging task, mainly because managers
consistently face considerable uncertainty. One important source of this uncertainty is the …

Forecasting emergency medical service call arrival rates Page 1 The Annals of Applied
Statistics 2011, Vol. 5, No. 2B, 1379–1406 DOI: 10.1214/10-AOAS442 © Institute of …

We consider the problem of staffing call centers with multiple customer classes and agent
types operating under quality-of-service (QoS) constraints and demand rate uncertainty. We …

Workload forecasting for a call center: Methodology and a case study Page 1 The Annals of
Applied Statistics 2009, Vol. 3, No. 4, 1403–1447 DOI: 10.1214/09-AOAS255 © Institute of …

We consider different statistical models for the call arrival process in telephone call centers.
We evaluate the forecasting accuracy of those models by describing results from an …

We provide an overview of the state of the art in research on operations in financial services.
We start by highlighting a number of specific operational features that differentiate financial …