Service Engineering: Data-Based Science in Support of Service Management, or Empirical Adventures in Call Centers and Hospitals

Mandelbaum, Avishai

Title:

Service Engineering: Data-Based Science in Support of Service Management, or Empirical Adventures in Call Centers and Hospitals

dc.contributor.author	Mandelbaum, Avishai
dc.contributor.corporatename	Georgia Institute of Technology. School of Industrial and Systems Engineering
dc.contributor.corporatename	Ṭekhniyon, Makhon ṭekhnologi le-Yiśraʼel
dc.date.accessioned	2011-03-10T21:53:36Z
dc.date.available	2011-03-10T21:53:36Z
dc.date.issued	2011-02-24
dc.description	Dr. Avishai "Avi" Mandelbaum presented a lecture on February 24, 2011 at 4:30 pm in the ISyE Atrium on the Georgia Tech campus.	en_US
dc.description	Avi Mandelbaum has a B.Sc. in Mathematics and Computer Science and an M.A. in Statistics, both summa cum laude, from Tel-Aviv University. His Ph.D. is in Operations Research from Cornell University. After graduation, in 1983, he joined the Graduate School of Business at Stanford University. He left the U.S. in 1991 to assume a position at the Technion on the faculty of Industrial Engineering and Management (IE&M). This faculty, as well as his current interests, constitutes a convex hull of all the areas with which he has been involved. At the Technion, Dr, Mandlebaum has been teaching courses in probability, stochastic processes and service engineering. In IE&M, he served as the coordinator of IE&M graduate studies and was the associate dean for research; he is a past member of the Technion Academic Development Committee and the Prize committee of the Applied Probability Society. Since 1995, he has been the faculty advisor for IE&M outstanding students. Dr. Mandelbaum's teaching has been acknowledged by the Technion Mani Award for Excellence in Teaching, 1999; the Technion Excellence in Teaching Award, 2000 (for the course, Service Engineering); and the inaugural Meir Rosenblatt Prize for teaching, 2004. His research contributions have been acknowledged by the ORSIS Yosef Levy prize, 2001; the Mitchner Prize for Quality Sciences and Quality Management, 2003; and the inaugural Markov Lecture of the Applied Probability Society, INFORM, 2005. Dr. Mandelbaum has been conducting research in the area of stochastic processes from the perspectives of operations research, statistics, engineering and management. His work has been mainly theoretical, but with a clear practical inspiration aimed at supporting decision making in complex operations. His most recent activities have concentrated on queueing networks. These provide useful models for a wide spectrum of real-world operations, spanning services, telecommunication, computers, manufacturing and transportation. Through research and teaching, he has been attempting to specialize the theory of queues to service networks, focusing on those with high level of customer/server interaction, either face-to-face, by telephone or Internet. Outside the Technion, in Israel, Europe and the U.S.A., he has been teaching, conducting workshops and consulting with numerous organizations on how to properly design, control and manage service operations. Dr. Mandelbaum served on the editorial boards of various journals, including Management Science; Queueing Systems, Theory and Applications (QUESTA); Mathematics of Operations Research (MOR); and Manufacturing and Service Operations Management (M&SOM). Since 2007, Dr. Mandelbaum has been the academic director of the Technion's SEE Laboratory. The lab is creating and maintaining a repository of large data-bases from service operations, mostly telephone call centers and recently expanding to hospitals. The data is used to support research and teaching, at the Technion and beyond.
dc.description	Runtime: 70:33 minutes
dc.description.abstract	Synopsis: In this lecture, Dr. Mandelbaum will describe examples of complex service operations for which data-based simple models have been found useful, which he refers to as "Simple Models at the Service of Complex Realities." Examples include call centers, hospitals, banks, courts and more. He views these service systems through the mathematical lenses of Queueing Science, with a bias towards Statistics. The mathematical framework for his models is asymptotic queueing theory, where limits are taken as the number of servers increases indefinitely, in ways that maintain a sought-after (often delicate) balance between staffing level and offered-load. A specific such balance reveals an operational regime that achieves, under already moderate scale, remarkably high levels of both service quality and efficiency. This is the QED regime (Quality- & Efficiency- Driven), discovered by Erlang and substantiated mathematically by Halfin & Whitt. The data-source for the lecture is a unique data repository from call centers and hospitals. The data is maintained at the Technion's SEE Laboratory (Service Enterprise Engineering). It is unique in that it is transaction-based; it details the individual operational history of all the service transactions (e.g., calls in a call center or patients in an emergency department). One source of data, publicly available, is a network of four call centers of a U.S. bank, spanning two and a half years and covering about 1,000 agents; there are 218,047,488 telephone calls overall, out of which 41,646,142 were served by agents, while the rest were handled by answering machines. The data can be explored via SEEStat, an environment for online Exploratory Data Analysis. SEEStat is accessible here after registration.	en_US
dc.format.extent	70:33 minutes
dc.identifier.uri	http://hdl.handle.net/1853/37355
dc.language.iso	en_US	en_US
dc.publisher	Georgia Institute of Technology	en_US
dc.relation.ispartofseries	School of Industrial and Systems Engineering Distinguished Lecture Series	en_US
dc.subject	Call centers	en_US
dc.subject	Complex service operations	en_US
dc.subject	Queueing science	en_US
dc.title	Service Engineering: Data-Based Science in Support of Service Management, or Empirical Adventures in Call Centers and Hospitals	en_US
dc.type	Moving Image
dc.type.genre	Lecture
dspace.entity.type	Publication
local.contributor.corporatename	H. Milton Stewart School of Industrial and Systems Engineering
local.contributor.corporatename	College of Engineering
local.relation.ispartofseries	ISyE Distinguished Lecture Series
relation.isOrgUnitOfPublication	29ad75f0-242d-49a7-9b3d-0ac88893323c
relation.isOrgUnitOfPublication	7c022d60-21d5-497c-b552-95e489a06569
relation.isSeriesOfPublication	cebbc4cf-1efb-47ce-a1a1-d48adbd00313