Prescriptive Analytics in Service Systems

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Business analytics, the use of data-driven methodologies to support decisions, plays a central role in organizations. From descriptive analytics that focuses on graphically depicting and statistically detecting problems in the existing system, through predictive analytics that aims at forecasting the system's behavior in the future, to prescriptive analytics that targets system improvement, business analytics became backbone of system and process analysis and optimization. In the proposed research, I shall focus on prescriptive analytics in service systems, such as hospitals, public transportation systems, and datacenters. I will target answering questions such as "how would the Emergency Department behave if we would add 5 nurses during last night's shift?", "when would the bus arrive to its destination had it been dispatched 5 minutes earlier?", and "how can we optimally schedule the datacenter to achieve predefined service level agreements?" Answering these questions in a data-driven fashion is the ultimate goal of evidence-based analysis and optimization in any organization and specifically, in services. In the modern era, most service systems employ information systems that monitor, control, and support their day-to-day operations. Hospitals use information systems to manage electronic health records of patients, schedule appointments, and replenish supplies. Call centers are using interactive voice response (IVR) systems to provide automated services to their customers. These systems can also collect event data that describe every activity that customers experience while contacting the system, track resource utilization, and record movements of materials and other logistic entities. In my PhD, I coined the term Queue mining, which is a set of techniques that enable automated data-driven business analytics using event data. Specifically, the data is used to construct queueing models of the underlying system, automatically, with minimal human involvement. Thus far, queue mining has been mainly focusing on predictive analytics, i.e., given the existing system (without interventions and changes), queue mining would aim at predicting its future behavior. The main emphasis has been on answering questions such as "how would my emergency department behave next Monday, with the existing resources?". Instead, I aim at developing a novel queue mining approach to answer prescriptive questions, which will involve the analysis of changes that one wishes to introduce into the underlying system. The long-term objective of the proposed research program is to develop queue mining methodologies for prescriptive business analytics in a variety of service systems that would produce robust, objective, and tunable models in minimal effort.