Data-driven Service Operations Management

This dissertation concerns data driven service operations management and includes three projects. An important aim of this work is to integrate the use of rigorous and robust statistical methods into the development and analysis of service operations management problems. We develop methods that take into account demand arrival rate uncertainty and workforce operational heterogeneity. We consider the particular application of call centers, which have become a major communication channel between modern commerce and its customers. The developed tools and lessons learned have general appeal to other labor-intensive services such as healthcare. The first project concerns forecasting and scheduling with a single uncertain arrival customer stream, which can be handled by parametric stochastic programming models. Theoretical properties of parametric stochastic programming models with and without recourse actions are proved, that optimal solutions to the relaxed programs are stable under perturbations of the stochastic model parameters. We prove that the parametric stochastic programming approach meets the quality of service constraints and minimizes staffing costs in the long-run. The second project considers forecasting and staffing call centers with multiple interdependent uncertain arrival streams. We first develop general statistical models that can simultaneously forecast multiple-stream arrival rates that exhibit inter-stream dependence. The models take into account several types of inter-stream dependence. With distributional forecasts, we then implement a chance-constraint staffing algorithm to generate staffing vectors and further assess the operational effects of incorporating such inter-stream dependence, considering several system designs. Experiments using real call center data demonstrate practical applicability of our proposed approach under different staffing designs. An extensive set of simulations is performed to further investigate how the forecasting and operational benefits of the multiple-stream approach vary by the type, direction, and strength of inter-stream dependence, as well as system design. Managerial insights are discussed regarding how and when to take operational advantage of the inter-stream dependence. The third project of this dissertation studies operational heterogeneity of call center agents with regard to service efficiency and service quality. The proxies considered for agent service efficiency and service quality are agents' service times and issue resolution probabilities, respectively. Detailed analysis of agents' learning curves of service times are provided. We develop a new method to rank agents' first call resolution probabilities based on customer call-back rates. The ranking accuracy is studied and the comparison with traditional survey-driven methods is discussed.Doctor of Philosoph

Estimation of staff use efficiency: Evidence from the hospitality industry

We analyze the extent to which hospitality firms overuse staff using a production function model which considers firm heterogeneity and accounts for environmental variables in staff use. We decompose overall staff use inefficiency into transient and persistent inefficiency. To do this, we employ a state-of-the-art stochastic frontier model, which is estimated using daily data on 94 Norwegian hospitality firms from 2010 to 2014. The environmental variables, especially the annual time trend, seasonality, and days of the week are found to exert heterogeneous effects on staffing. The mean transient, persistent, and overall efficiencies of the hospitality firms are 69%, 67%, and 46%, respectively. We find that seasonality (days of the week) decreases (increases) transient inefficiency by about 4%, suggesting significant room for improvement in hospitality staff use.publishedVersio

An Integrated Approach for Shift Scheduling and Rostering Problems with Break Times for Inbound Call Centers

It may be very difficult to achieve the optimal shift schedule in call centers which have highly uncertain and peaked demand during short time periods. Overlapping shift systems are usually designed for such cases. This paper studies shift scheduling and rostering problems for in bound call centers where overlapping shift systems are used. An integer programming model that determines which shifts to be opened and how many operators to be assigned to these shifts is proposed for the shift scheduling problem. For the rostering problem both integer programming and constraint programming models are developed to determine assignments of operators to all shifts, weekly days-off, and meal and relief break times of the operators. The proposed models are tested on real data supplied by an outsource call center and optimal results are found in an acceptable computation time. An improvement of 15% in the objective function compared to the current situation is observed with the proposed model for the shift scheduling problem. The computational performances of the proposed integer and constraint programming models for the rostering problem are compared using real data observed at a call center and simulated test instances. In addition, benchmark instances are used to compare our Constraint Programming (CP) approach with the existing models. The results of the comprehensive computational study indicate that the constraint programming model runs more efficiently than the integer programming model for the rostering problem. The originality of this research can be attributed to two contributions: (a) a model for shift scheduling problem and two models for rostering problem are presented in detail and compared using real data and (b) the rostering problem is considered as a task-resource allocation and considerably shorter computation times are obtained by modeling this new problem via CP

A method for estimation of redial and reconnect probabilities in call centers

In practice, many call center forecasters use the total inbound volume to make forecasts. In reality, besides the fresh calls (initial call attempts), there are many redials (re-attempts after abandonments) and reconnects (re-attempts after answered calls) in call centers. Neglecting redials and reconnects will inevitably lead to inaccurate forecasts, which eventually leads to inaccurate staffing decisions. However, most of the call center data sets do not have customer-identity information, which makes it difficult to identify how many calls are fresh. Motivated by this, the goal of this paper is to estimate the number of fresh calls, and the redial and reconnect probabilities. To this end, we propose a model to estimate these three variables. We formulate our estimation model as a minimization problem, where the actual redial and reconnect probabilities lead to the minimum objective value. We validate our estimation results via real call center data and simulated data