PhD in Operations and Information Systems
Students in Operations and Information Systems (OIS) train to become scholars in Operations Management (OM) and Management Information Systems (MIS). All students receive training in the fundamentals of OM and MIS and in-depth training in at least one of these two disciplines. OM and MIS intersect in various ways. The fields share a focus on understanding and improving business processes. OM and MIS researchers often reside in the same university departments and belong to the same professional societies. The goal of the PhD in OIS is to train the student to perform and publish first-class research in preparation for a career at a research-intensive university.
Maryam Zakeri Hosseinabadi
"From the time I was in the master's program, my plan was to pursue my studies in a high-quality PhD program; therefore, I applied to the Alberta School of Business."
OM is concerned with organizing the production of goods and services by private firms and public sector entities. OM research addresses questions about how best to design, plan and execute the tasks needed to produce a product or service. OM research has traditionally emphasized mathematical modeling — in particular optimization, stochastic, and simulation models — but OM research also employs experiments, surveys and analysis of secondary data.
MIS is concerned with the collection, storage and use of information within organizations. MIS research addresses questions about how technology and information shape and facilitate the work that is done in organizations. MIS research uses methodologies from a variety of disciplines, including but not limited to: the social sciences, mathematics and computer science.
OM and MIS are closely related to the emerging areas of business analytics and big data.
The following Business Analytics (ABA) faculty members support the OIS specialization:
- Param Chhabra (New product development, crowdfunding, innovation, entrepreneurship, empirical research in operations management)
- Ivor Cribben (Time series analysis/econometrics, high-dimensional data, data analytics, networks, computational statistics, neuroeconomics)
- Armann Ingolfsson (Health care and service operations, queueing theory, behavioral queueing, computational probability)
- Yonghua Ji (Economics of information systems, dynamic optimization of software development, information systems performance analysis and optimization)
- Bora Kolfal (Flexibility in service operations and supply chains, queueing systems, supply chain management, healthcare applications of OR, impact of risk on IT)
- Ilbin Lee (Sequential decision-making, data analytics, health applications, and wildfire operations)
- Mohua Podder (Dynamic variable selections, supervised classification models)
- Borzou Rostami (Data-driven decision making, machine learning applied to real-time decision making, large-scale optimization, supply chain and retails analytics, transportation and logistics)
- Saied Samiedaluie (Health care operations, approximate dynamic programming)
- Mohamad Soltani (Healthcare operations, service operations, empirical studies in operations management)
Graduates of the OIS specialization (and its predecessor, Management Science) have published articles in top OM and MIS journals and have pursued academic careers at prestigious universities throughout the world.
Specialization Course Requirements
We prefer that students have a Master's degree in a relevant field and experience in his or her field of study. Students may enter the program without these prerequisites; however additional courses may be required.
Each student must complete four of the following courses:
This course provides a general introduction to the major research fields of operations management (OM). The focus will be on reading and evaluating current papers from prominent OM journals. The theory of science and the review process will be briefly discussed. Students are expected to have as mathematical background the equivalent of upper-level undergraduate or first-year graduate courses in optimization and probability or stochastic modeling. This course may be appropriate for some graduate students in engineering or computing science.
Prerequisite: A graduate or undergraduate course in operations management. Open to all doctoral students or with the written permission of the instructor. Approval of the Associate Dean, PhD Program is also required for non-PhD students.
This course will provide an in-depth introduction to a particular methodology or a particular setting that is relevant to research in operations management. The topic may vary from year to year. Possible topics include optimization modeling and formulation, stochastic modeling and optimization, behavioural research in operations management, and health care operations management. The required background for students will vary depending on the topic. This course may be appropriate for some graduate students in engineering or computing science.
Prerequisite: Written permission of the instructor. Approval of the Associate Dean, PhD Program is also required for non-PhD students.
Prerequisite: Written permission of the instructor. Approval of the Associate Dean, PhD Program is also required for non-PhD students.
This course provides a general introduction to the major research fields of management information systems (MIS). As an introductory seminar, coverage will include current and historical topics appearing in top information systems journals. Discussions will revolve around the reference disciplines and theories used in the MIS literature.
Co-requisite: MGTSC 705. Prerequisite: A graduate or undergraduate course in management information systems or equivalent. Open to all doctoral students or with the written permission of the instructor. Approval of the Associate Dean, PhD Program is also required for non-PhD students.
This course will provide an in-depth introduction to a particular methodology or a particular setting that is relevant to research in management information systems. The topic may vary from year to year. Possible topics include applications of optimal control theory in management information systems and operations management, collaborative communication systems, and quantitative models for management information systems. The required background for students will vary depending on the topic. This course may be appropriate for some graduate students in engineering or computing science.
Prerequisite: Written permission of the instructor. Approval of the Associate Dean, PhD Program is also required for non-PhD students.
Prerequisite: Written permission of the instructor. Approval of the Associate Dean, PhD Program is also required for non-PhD students.
An overview of multivariate data analysis normally taken by students in the first year of the Business PhD program. The course is designed to bring students to the point where they are comfortable with commonly used data analysis techniques available in most statistical software packages. Students are expected to complete exercises in data analysis and in solving proofs of the major results. Topics will include univariate analysis, bivariate analysis, multiple linear regression, and analysis of variance. It is expected that students have as background at least (a) one semester of calculus; (b) one semester of linear algebra, and (c) two semesters introduction to probability, probability distributions and statistical inference.
Prerequisite: Registration in Business PhD Program or written permission of instructor. Approval of the Associate Dean, PhD Program is also required for non-PhD students.
This course is organized into two parts. Part I covers univariate and multivariate time domain models of stationary and nonstationary time series. Topics covered include univariate time series models, unit root tests, time series regression modeling, systems of regression equations, vector autoregressive models for multivariate time series and cointegration. In Part II the course introduces the issues and opportunities that arise with panel data and the main statistical techniques used for its analysis. Topics covered include fixed-effects models, random-effects models, dynamic models and limited dependent variable models. Throughout the course, the emphasis will be on how to use S-plus and Stata to estimate panel data and time series models. There is relatively less emphasis on statistical theory. Evaluation in the course is based on home work assignments and a term project.
Prerequisite: MGTSC 705 or equivalent.
Research Methods Courses
Students must complete four graduate-level courses in research methods applicable to that student's program of study. This may include courses from the list above, if not used to satisfy the specialization's requirement.
Cognate Discipline
Students must complete a Cognate Discipline consisting of three courses. These courses can be offered within the School of Business or in other faculties or schools within the University of Alberta. The purpose of these courses is to support the dissertation research. Elective courses are selected in consultation with the student's supervisor. The Cognate Discipline could be in one of the other PhD specializations offered in the School of Business.