Vijay Mehrotra

Bio: Vijay Mehrotra is an academic researcher from University of San Francisco. The author has contributed to research in topics: Call management & Call control. The author has an hindex of 14, co-authored 24 publications receiving 1323 citations. Previous affiliations of Vijay Mehrotra include San Francisco State University.

The Modern Call Center: A Multi-Disciplinary Perspective on Operations Management Research

Abstract: Call centers are an increasingly important part of today's business world, employing millions of agents across the globe and serving as a primary customer-facing channel for firms in many different industries. Call centers have been a fertile area for operations management researchers in several domains, including forecasting, capacity planning, queueing, and personnel scheduling. In addition, as telecommunications and information technology have advanced over the past several years, the operational challenges faced by call center managers have become more complicated. Issues associated with human resources management, sales, and marketing have also become increasingly relevant to call center operations and associated academic research. In this paper, we provide a survey of the recent literature on call center operations management. Along with traditional research areas, we pay special attention to new management challenges that have been caused by emerging technologies, to behavioral issues associated with both call center agents and customers, and to the interface between call center operations and sales and marketing. We identify a handful of broad themes for future investigation while also pointing out several very specific research opportunities.

Call center simulation modeling: Methods, challenges, and opportunities

Abstract: Using stochastic models to plan call center operations, schedule call center staff efficiently, and analyze projected performance is not a new phenomenon, dating back to Erlang's work in the early twentieth century. However, several factors have recently conspired to increase demand for call center simulation analysis.• Increasing complexity in call traffic, coupled with the almost ubiquitous use of Skill-Based Routing.• Rapid change in operations due to increased merger and acquisition activity, business volatility, outsourcing options, and multiple customer channels (inbound phone, outbound phone, email, web, chat) to support.• Cheaper, faster desktop computing, combined with specialized call center simulation applications that are now commercially available.In this tutorial, we will provide an overview of call center simulation models, highlighting typical inputs and data sources, modeling challenges, and key model outputs. In the process, we will also present an interesting "real-world" example of effective use of call center simulation.

Intelligent Procedures for Intra‐Day Updating of Call Center Agent Schedules

Abstract: For nearly all call centers, agent schedules are typically created several days or weeks before the time that agents report to work. After schedules are created, call center resource managers receive additional information that can affect forecasted workload and resource availability. In particular, there is significant evidence, both among practitioners and in the research literature, suggesting that actual call arrival volumes early in a scheduling period (typically an individual day or week) can provide valuable information about the call arrival pattern later in the same scheduling period. In this paper, we develop a flexible and powerful heuristic framework for managers to make intra-day resource adjustment decisions that take into account updated call forecasts, updated agent requirements, existing agent schedules, agents' schedule flexibility, and associated incremental labor costs. We demonstrate the value of this methodology in managing the trade-off between labor costs and service levels to best meet variable rates of demand for service, using data from an actual call center.

Forecast errors in service systems

Abstract: We investigate the presence and impact of forecast errors in the arrival rate of customers to a service system. Analysis of a large dataset shows that forecast errors can be large relative to the fluctuations naturally expected in a Poisson process. We show that ignoring forecast errors typically leads to overestimates of performance and that forecast errors of the magnitude seen in our dataset can have a practically significant impact on predictions of long-run performance. We also define short-run performance as the random percentage of calls received in a particular period that are answered in a timely fashion. We prove a central limit theorem that yields a normal-mixture approximation for its distribution for Markovian queues and we sketch an argument that shows that a normal-mixture approximation should be valid in great generality. Our results provide motivation for studying staffing strategies that are more flexible than the fixed-level staffing rules traditionally studied in the literature.

A Call Center Uses Simulation to Drive Strategic Change

Abstract: A large, customer-focused software company relied on simulation modeling of its call center operations in launching a new fee-based technical-support program. Prior to launching this rapid program, call center managers were concerned about the difficulty of meeting a proposed guarantee to paying customers that they would wait less than one minute on hold. Managers also wanted to know how the new program would affect the service provided to their existing base of regular, nonpaying customers. We quickly developed an animated simulation model that addressed these concerns and gave the managers a good understanding for the impact on system performance of changes in the number of customers purchasing the rapid program and in the number of agents. The one-minute guarantee would be fairly easy to achieve, even if the percentage of callers in the rapid program became quite high. Managers also gained confidence that, with appropriate staffing levels, they could successfully implement the new program, which they soon did.

Convergence of probability measures

Abstract: The author's preface gives an outline: "This book is about weakconvergence methods in metric spaces, with applications sufficient to show their power and utility. The Introduction motivates the definitions and indicates how the theory will yield solutions to problems arising outside it. Chapter 1 sets out the basic general theorems, which are then specialized in Chapter 2 to the space C[0, l ] of continuous functions on the unit interval and in Chapter 3 to the space D [0, 1 ] of functions with discontinuities of the first kind. The results of the first three chapters are used in Chapter 4 to derive a variety of limit theorems for dependent sequences of random variables. " The book develops and expands on Donsker's 1951 and 1952 papers on the invariance principle and empirical distributions. The basic random variables remain real-valued although, of course, measures on C[0, l ] and D[0, l ] are vitally used. Within this framework, there are various possibilities for a different and apparently better treatment of the material. More of the general theory of weak convergence of probabilities on separable metric spaces would be useful. Metrizability of the convergence is not brought up until late in the Appendix. The close relation of the Prokhorov metric and a metric for convergence in probability is (hence) not mentioned (see V. Strassen, Ann. Math. Statist. 36 (1965), 423-439; the reviewer, ibid. 39 (1968), 1563-1572). This relation would illuminate and organize such results as Theorems 4.1, 4.2 and 4.4 which give isolated, ad hoc connections between weak convergence of measures and nearness in probability. In the middle of p. 16, it should be noted that C*(S) consists of signed measures which need only be finitely additive if 5 is not compact. On p. 239, where the author twice speaks of separable subsets having nonmeasurable cardinal, he means "discrete" rather than "separable." Theorem 1.4 is Ulam's theorem that a Borel probability on a complete separable metric space is tight. Theorem 1 of Appendix 3 weakens completeness to topological completeness. After mentioning that probabilities on the rationals are tight, the author says it is an

The Modern Call Center: A Multi-Disciplinary Perspective on Operations Management Research

Abstract: Call centers are an increasingly important part of today's business world, employing millions of agents across the globe and serving as a primary customer-facing channel for firms in many different industries. Call centers have been a fertile area for operations management researchers in several domains, including forecasting, capacity planning, queueing, and personnel scheduling. In addition, as telecommunications and information technology have advanced over the past several years, the operational challenges faced by call center managers have become more complicated. Issues associated with human resources management, sales, and marketing have also become increasingly relevant to call center operations and associated academic research. In this paper, we provide a survey of the recent literature on call center operations management. Along with traditional research areas, we pay special attention to new management challenges that have been caused by emerging technologies, to behavioral issues associated with both call center agents and customers, and to the interface between call center operations and sales and marketing. We identify a handful of broad themes for future investigation while also pointing out several very specific research opportunities.

Hospital Readmissions Reduction Program

Abstract: Hospital readmission measures have been touted not only as a quality measure but also as a means to bend the healthcare cost curve. The Affordable Care Act (ACA) established the Hospital Readmission Reduction Program (HRRP) in 2012. Under this program, hospitals are financially penalized if they have higher-than-expected risk-standardized 30-day readmission rates for acute myocardial infarction, heart failure, and pneumonia. The HRRP has garnered significant attention from the medical community, both positive and negative. Here, we describe the reasons the HRRP was implemented, the penalties levied, the impact it has had on transitional care and readmissions, the pros and cons of the policy, and its future. Hospital readmissions are associated with unfavorable patient outcomes and high financial costs.1,2 Causes of readmissions are multifactorial, and rates vary substantially by institution.3,4 Historically, nearly 20% of all Medicare discharges had a readmission within 30 days.1 The Medicare Payment Advisory Commission has estimated that 12% of readmissions are potentially avoidable. Preventing even 10% of these readmissions could save Medicare $1 billion.5 Therefore, reducing hospital readmissions has been made a national priority. In 2008, the Medicare Payment Advisory Commission recommended to Congress that the Centers for Medicare & Medicaid Services (CMS) begin confidentially reporting readmission rates and resource use to hospitals and physicians.6 In 2009, CMS began publicly reporting hospital-level readmission rates, which were added to the Hospital Compare Web site.7 Before 2012, hospitals had little direct financial incentive to reduce readmissions. For Medicare beneficiaries with inpatient stays, hospitals receive payment with the inpatient prospective payment system (IPPS). This payment, based on a diagnosis-related group (DRG), covers the inpatient stay and any outpatient diagnostic and admission-related outpatient nondiagnostic services provided by the institution on the date of the patient’s admission or within 3 days immediately …

New “Smart Parking” System Based on Resource Allocation and Reservations

Abstract: We propose a novel “smart parking” system for an urban environment. The system assigns and reserves an optimal parking space based on the driver's cost function that combines proximity to destination and parking cost. Our approach solves a mixed-integer linear programming (MILP) problem at each decision point defined in a time-driven sequence. The solution of each MILP is an optimal allocation based on current state information and is updated at the next decision point with a guarantee that there is no resource reservation conflict and that no driver is ever assigned a resource with a cost function higher than this driver's current cost function value. Based on simulation results, compared with uncontrolled parking processes or state-of-the-art guidance-based systems, our system reduces the average time to find a parking space and the parking cost, whereas the overall parking capacity is more efficiently utilized. We also describe full implementation in a garage to test this system, where a new light system scheme is proposed to guarantee user reservations.