Learning from Many: Partner Exposure and Team Familiarity in Fluid Teams

In services where teams come together for short collaborations, managers are often advised to strive for high team familiarity so as to improve coordination and consequently, performance. However, inducing high team familiarity by keeping team membership intact can limit workers’ opportunities to acquire useful knowledge and alternative practices from exposure to a broader set of partners. We introduce an empirical measure for prior partner exposure and estimate its impact (along with that of team familiarity) on operational performance using data from the London Ambulance Service. Our analysis focuses on ambulance transports involving new paramedic recruits, where exogenous changes in team membership enable identification of the performance effect. Specifically, we investigate the impact of prior partner exposure on time spent during patient pickup at the scene and patient handover at the hospital. We find that the effect varies with the process characteristics. For the patient pickup process, which is less standardized, greater partner exposure directly improves performance. For the more standardized patient handover process, this beneficial effect is triggered beyond a threshold of sufficient individual experience. In addition, we find some evidence that this beneficial performance impact of prior partner exposure is amplified during periods of high workload, particularly for the patient handover process. Finally, a counterfactual analysis based on our estimates shows that a team formation strategy emphasizing partner exposure outperforms one that emphasizes team familiarity by about 9.2% in our empirical context

Staffing Call Centers with Uncertain Demand Forecasts: A Chance-Constrained Optimization Approach

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 introduce a formulation of the staffing problem that requires that the QoS constraints are met with high probability with respect to the uncertainty in the demand rate. We contrast this chance-constrained formulation with the average-performance constraints that have been used so far in the literature. We then propose a two-step solution for the staffing problem under chance constraints. In the first step, we introduce a random static planning problem (RSPP) and discuss how it can be solved using two different methods. The RSPP provides us with a first-order (or fluid) approximation for the true optimal staffing levels and a staffing frontier. In the second step, we solve a finite number of staffing problems with known arrival rates--the arrival rates on the optimal staffing frontier. Hence, our formulation and solution approach has the important property that it translates the problem with uncertain demand rates to one with known arrival rates. The output of our procedure is a solution that is feasible with respect to the chance constraint and nearly optimal for large call centers.call centers, chance-constrained optimization, queueing