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Management Systems Engineering Ph.D.

Management Systems Engineering (MSE) is the definition and application of engineering design and analysis processes and methodologies to systems involving people and technologies within organizations. One of the primary objectives is to define and develop the science of designing complex management systems. MSE is focused on the research, design, development, deployment, measurement, and improvement of systems comprised of decision-makers, information, organizational structures, technologies, decision tools, and work processes, with an emphasis on the interactions among these components. The MSE graduate option is designed to provide students with the knowledge and skills to meet the challenges posed by increasingly complex organizational systems within dynamic, global environments.
The MSE academic curriculum is designed to expose students to a breadth of industrial engineering topics at the graduate level, depth in management systems engineering topics, and the opportunity to take technical electives in specialized areas within industrial engineering and other areas.
The MSE curriculum and faculty research interests include the following content areas:

• Virtual systems/organizations
• Quality, productivity, and performance management
• Technology in group decision-making
• Quality management applications, practices, and tools
• Team-based work systems
• Supply chain management
• Compensation management
• Macroergonomics/work system analysis and design
• Organizational and process change
• Economic engineering
• System modeling and simulation
• Enterprise integration
• System performance
• System thinking
• System dynamics modeling
• Data Envelopment Analysis
• Fuzzy methods for data imprecision
• Data visualization for improved decision-making
• Health care systems applications
• Design of engineering systems

Research methodologies used by faculty and integrated within the curriculum are diverse and include simulation, experimentation, field survey research, and mathematical modeling. The Ph.D. degree provides an opportunity for in-depth independent research in a highly focused area approved by the graduate advisory committee. Doctoral study requires a minimum of 92 total credits beyond a bachelor’s degree (57 course credits and 35 research credits). All students pursuing a Ph.D. must take (or have the equivalent of) the core industrial engineering and MSE courses required for the M.S. degree, in addition to required preparatory courses where appropriate. All students must also take additional coursework in areas of advanced performance measurement, research methods, macroergonomics, optimization, and probability and statistics.
A Ph.D. curriculum is constructed to meet the needs of each particular student, taking into account his or her background, academic needs, and research focus. Thus, each student's curriculum will contain technical electives to support the planned doctoral research. The student and his or her advisor will carefully choose the technical electives, with the assistance of the advisory committee. A student’s graduate advisory committee may require the student to take courses in particular areas to fulfill technical elective requirements or remedial needs.

 

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