The financial services industry covers banks, insurance companies and investment managers, as well as transaction or message processing companies. To streamline billions of daily transactions, lean principles and operational excellence programs have found theirway to financial service firms....

We investigate near-shoring a small part of the global production to local \emph{SpeedFactories} that serve only the variable demand. The short lead time of the responsive SpeedFactory reduces the risk of making large volumes in advance, yet it does not involve a complete re-shoring of demand....

We study an inventory model with volume flexibility. A firm may replenish its inventory by using its base capacity at regular sourcing cost, after which additional supply is available at a premium. The structure of the optimal replenishment policy is known to be a modified base-stock policy, yet...

The integration of digital technologies is changing the way organizations operate and deliver value. Digitizing operations may replace manual work through increased automation, but it may also enable smarter execution of workflows thereby augmenting human work. Evaluating the level and reach of...

Can there be side effects of improved forecast accuracy? In this study of the Belgian food retailer Colruyt Group, we show how adding explanatory variables (such as promotions, weather forecasts, national events, etc.) increases forecast accuracy compared to methods using only historical sales...

By digitizing operations, companies may replace manual work with increased automation, but they may also augment human work through smarter execution. The authors present a conceptual framework that distinguishes the different levels of digitization, automation, and intelligence. This framework...

We investigate how volume flexibility, defined by a sourcing cost premium beyond a base capacity, at a local responsive supplier impacts the decision to reshore supply. The buyer also has access to a remote supplier that is cheaper with no restrictions on volume flexibility. We show that with...

We propose a new policy architecture to apply deep reinforcement learning (DRL) with a continuous action representation. This contrasts most current DRL implementations that make use of a discrete action space. The latter are not scalable to large problems. To obtain feasible discrete actions,...