PhD in Operations and Information Systems

Students in the Operations and Information Systems (OIS) major train to become scholars in Operations Management (OM) and Management Information Systems (MIS).  All students receive training in the fundamentals of both 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.

OM is concerned with how to organize 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, and 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.

The following faculty members support the OIS major:

  • Ofer Arazy (MIS: Knowledge management and computer supported cooperative work) 
  • Reidar Hagtvedt (OM: Health care operations) 
  • Armann Ingolfsson (OM: Health care and service operations; developing methodology to analyze congested systems) 
  • Yonghua Ji (MIS: Economics of information systems; dynamic optimization of software development; information systems performance analysis and optimization) 
  • Bora Kolfal (OM: Flexibility in service operations and supply chains; queueing systems; supply chain management; healthcare applications of OR; impact of risk on IT) 
  • Ray Patterson (MIS: Meta-heuristic decision techniques; design science; service science; network design; security) 
  • Michele Samorani (OM: Business analytics, data mining, appointment scheduling)

Graduates of the OIS major (and its predecessor, the major in Management Science) have published articles in top OM and MIS journals and have pursued academic careers at prestigious universities throughout the world.

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.

1. Major Area Course Requirements

Each student must complete four of the following courses:

OM 701. Introductions to Operations Management Research 
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.

OM 702. Advanced Research Topics in Operations Management.
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.

OM 710. Individual Research

MIS 701. Introduction to Management Information Systems Research
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.

MIS 702. Advanced Research Topics in Management Information Systems
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.

MIS 710. Individual Research                                                                                                                       

MGTSC 705. Multivariate Data Analysis I                                                                                                    

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.

MGTSC 706. Multivariate Data Analysis II                                                                                                  
A continuation of the overview of multivariate data analysis begun in MGTSC 705. Topics include categorical data analysis, multivariate linear regression, discriminant analysis, canonical correlation, multivariate analysis of variance, principal component analysis, factor analysis, cluster analysis and logistic regression. Prerequisite: MGTSC 705 or consent of the instructor. Approval of the Associate Dean, PhD Program is also required for non-PhD students.

MGTSC 707. Applied Business Analysis of Time Series and Panel Data
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. 

2. Research Methods Minor

Students must complete a Research Methods Minor, consisting of 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 major area requirement.

3. Cognate Minor

Students must complete a Cognate Minor 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 Minor could be in one of the other PhD majors offered in the School of Business.