Table of Contents
1Introduction
1.1Background
1.2Problem Statement
1.3Objectives and Scope
1.4Stakeholders
2System Requirements
2.1Functional Requirements
2.2Usability Requirements
2.3Input Requirements
2.4Output Requirements
3Technical Approach
3.1Methodology
3.2Data Sources
4Expected Results
5Project Plan
5.1Resources
5.2Timeline
5.3Key Milestones
1Introduction
1.1Background
OOne of George Mason University’s (GMU) strategic goals over the past decade was to become a top- tier research university.A strong consensus emerged among GMU faculty and leaders during the inclusive strategic planning process in 2012-2013. The consensus was that GMU needed to continue to strengthen its investment in research as a continuation of the growth of the university and as a fulfillment of the public mission to act as an engine of innovation for its community and region ((“Mason achieves top research,”GMU, 2016). Comparing GMU to institutions in “Highest Research Activity”Top-tier (R1) category of Carnegie Classification, GMU is “the new kid on the block.” (“Mason achieves highest Carnegie,(GMU,” 2016). However, since 2012, the GMU community has made major investments in research to achieve R1 status. Such investments were seenresulted in an increase in the school’s total research expenditures that grew from $77 million in 2008-2009 to $99 million in 2013-2014 (GMU,, “Mason achieves top research,” 2016). On February 1, 2016, that dream became a reality, as GMU moved into the “Top-tierHighest Research Activity” (R1) category of Carnegie Classification, based on a review of its 2013-2014 data that was performed by the Center for Postsecondary Research at Indiana University Schools of Education (“Mason achieves highest Carnegie,”(GMU, 2016). The review showed that Mason’s total research expenditures grew from $77 million in 2008-2009 to $99 million in 2013-2014 (“Mason achieves top research,” 2016). The increase in research expenditures was driven by growth in research expenditures in science and engineering, which doubled during that period (GMU,2016, 2016). The university also increased the number of doctoral degrees it conferred by 27 percent in that same period (GMU,, “Mason achieves top research,” 2016).
The Carnegie Classification is a prestigious classification that shows the intense competition between the universities in our nation. There is a total of 335 universities in this classification: 115 of them are R1, 107 are R2, and 113 are R3 (GMU,, (“Mason achieves highest Carnegie,” 2016). While reaching such a classification is a remarkable achievement for GMU, the new goal for GMU is to have a robust, high-impact research program that will lead Carnegie to maintain its categorization of GMU as a top-tier research university.
R1 in research..
One of George Mason University’s (GMU) strategic goals over the past decade was to become a top tier research university.A strong consensus emerged among GMU faculty and leaders during the inclusive strategic planning process in 2012-2013. The consensus was that GMU needed to continue to strengthen its investment in research as a continuation of the growth of the university and as a fulfillment of the public mission to act as an engine of innovation for its community and region (“Mason achieves top research,” 2016). Comparing GMU to institutions in “Highest Research Activity” (R1) category of Carnegie Classification, GMU is “the new kid on the block.” (“Mason achieves highest Carnegie,” 2016). However, since 2012, the GMU community has made major investments in research to achieve R1 status. Such investments were seen in increase in the school’s total research expenditures that grew from $77 million in 2008-2009 to $99 million in 2013-2014(“Mason achieves top research,” 2016). On February 1, 2016, that dream became a reality, as GMU moved into the “Highest Research Activity” (R1) category of Carnegie Classification, based on a review of its 2013-2014 data that was performed by the Center for Postsecondary Research at Indiana University Schools of Education (“Mason achieves highest Carnegie,” 2016). The review showed that Mason’s total research expenditures grew from $77 million in 2008-2009 to $99 million in 2013-2014 (“Mason achieves top research,” 2016). The increase was driven by growth in research expenditures in science and engineering, which doubled during that period. The university also increased the number of doctoral degrees it conferred by 27 percent in that same period (“Mason achieves top research,” 2016).
The Carnegie Classification is a prestigious classification that shows the intense competition between the universities in our nation. There is a total of 335 universities in this classification: 115 of them are R1, 107 are R2, and 113 are R3 (“Mason achieves highest Carnegie,” 2016). While reaching such a classification is a remarkable achievement for GMU, the new goal for GMU is to have a robust, high-impact research program that will lead Carnegie to maintain its categorization of GMU as R1.
1.2Problem Statement
The importance of the ability to forecast the key indicators that would affect the research development is obviously very important for GMU. What is not as obvious is how GMU would accomplish this feat. There are so many correlating factors that affect research as shown in figure 1, but there is currently no known tool or a model that conducts tailored analysis and characterization of such factors in order to assess the overall health of the research enterprise at GMU.
Figure (1): An economic model of complex academic enterprises that captures the key flows (Rouse, 2016)
1.3Objectives and Scope
-The objective of this project is to develop a model to represent relationships among key drivers of the Mason research enterprise and their interactions with other major activities at the university, focusing initially on Volgenau School of Engineering (VSE). VSE has contributed VSE is one of the top contributors to growth in research expenditures ofscience and engineering, which doubled during the period of 2013-2014(“Mason achieves top research,” 2016), and VSEhas the most complete data that will be accessible to the team during the project.
-The model required should be implemented as a tool to support:
- Assessing the overall health of the research enterprise at Mason.
- Examining key indicators relating to income, expenditures, and facilities and the causal relationships among them.
- Projecting trends on indicators of interest and their dependence on strategic decisions and investments.
- Examining “what if” scenarios for different investment strategies.
-The team will not be providing any recommendations such as:
- What is the optimal solution?
- Which investment is better than others?
1.4Stakeholders
Primary Stakeholders / Secondary Stakeholders- Sponsors:
- Dr. Stephen Nash
- Dr.Art Pyster
- VP of GMU Research:
- Dr.Deborah Crawford
- Major Decision Makers.
- Private Sector
- Federal Agencies
- State Agencies
- All Faculty members
- All Students
2System Requirements
Below are the preliminary requirements for this project. These requirements are subject to change based on feedback from our sponsors during the assigned period of the project and our class professor in addition to the lessons will be learnt from our experiments.
2.1Functional Requirements
FR1: The model shall represent the causal relationships among key indicators.
FR2: The model shall quantify the causal relationships among key indicators.
2.2Usability Requirements
UR1: The model shall be accessible by sponsors and key stakeholders.(GMU has an academic license for the software used, hence the software for running the proposed modelwill be available for the user to download).
UR2: The model shall be usable by sponsors and key stakeholders by referring to user guide / manual.
2.3Input Requirements
IR1: The model shall allow the user to adjust the rate of howat which each indicator is affected by another.
IR2: The model shall allow the userto adjust the amount of each indicator of interest.
Note: The indicators mentioned above are referring to the correlating factors that affect research as shown above in figure (1).
2.4Output Requirements
OR1: The model shall output trends on indicators of interest.
OR2: The model shall output projected value of indicators of interest.
3Technical Approach
3.1Methodology
By referencing the knowledge of systems engineering processes that we acquired over the course of the SEOR graduate program at GMU and by leveraging the work and internship experience we gained in different fields, we have formulated a technical approach that will give the team the best chance of achieving the sponsor’s goals.
The first of our approaches is an Excel-based numerical model from Dr. William Rouse at Stevens Institute of Technology. Following the relationships laid out in his text, Universities As Complex Enterprises (2016), the Rouse model takes University financial, academic, and research data and outputs long-term projections for various metrics of research and University health. Per our agreement with Dr. Rouse, the team cannot share technical details of the model save for a handful of approved faculty (such as our sponsors).
We base our second solution around a System Dynamics Model. System Dynamics (SD) is an approach that facilitates understanding of the linear and nonlinear behaviors of highly complexsystemsover a period of time using stocks, flows, and feedback loops. It is an aspect ofsystems theory that is usedto understand the dynamic behavior of complex systems. The basis of SD is the recognition that “the structure of any system — the many circular, interlocking, sometimes time-delayed relationships among its components — is often just as important in determining its behavior as the individual components themselves” (Wikipedians, n.d., p. 144).
There are avariety of softwarepackages that have been used for system dynamic modeling. The team will use the academic license for the Vensim Software tool that is provided to them through the SEOR department. Vensim is a powerful software tool that provides a graphical modeling interface withstock and flowandcausal loop diagrams as shown below in Figure (2). In this model, thestockvariable is measured at one specific time and it represents a quantity of a variable at a point of time, while a flowvariable represents a change during a period of time and is measured over an interval of time.
By using the knowledge of the Systems Engineering processes that we acquired throughout the SEOR graduate program at GMU and by leveraging the work and internships experience we gained from working in different fields, we have formulated a technical approach that will provide the team with the best chance for achieving the sponsor’s goals.
We plan to approach the problem by using a System Dynamics Model. System Dynamics (SD) is an approach to understand the nonlinear behavior of complexsystemsover time using stocks, flows, internal feedback loops, and time delays. System dynamics is an aspect ofsystems theory usedas a method to understand the dynamic behavior of complex systems. The basis of SD is the recognition that the structure of any system,—the many circular, interlocking, sometimes time-delayed relationships among its components is often just as important in determining its behavior as the individual components themselves (Malczynski, 2011).
SD is utilized through simulation. There are avariety of softwarepackages that have been optimized for this. The team will use the academic license for the Vensim Software that is provided to them through the SEOR department. Vensim is a powerful software that provides a graphical modeling interface withstock and flowandcausal loop diagrams as shown below in figure (2). In this model, astockvariable is measured at one specific time and represents a quantity existing at that point in time that may haveaccumulatedin the past, while a flowvariable is measured over an interval of time.
SD is utilized through simulation and there are avariety of softwarepackages that have been optimized for this. The team will use the academic license for the Vensim Software that is provided to them through the SEOR department. Vensim is a powerful software that provides a graphical modeling interface withstock and flowandcausal loop diagrams as shown below in figure (2). Where astockvariable is measured at one specific time, and represents a quantity existing at that point in time which may haveaccumulatedin the past, while a flowvariable is measured over an interval of time.
Figure (2): shows an example for Stock and flow diagram ofnew product adoptionmodel(System Dynamics,2017)
The steps involved in SD simulation are:
“Defining the problem boundary. Identifying the most important stocks and flows that change these stock levels. Identifying sources of information that impact the flows. Identifying the main feedback loops. Drawing a causal loop diagram that links the stocks, flows and sources of information. Writing the equations that determine the flows. Estimating the parameters and initial conditions using statistical methods, expert opinion, market research data or other relevant sources of information. Simulating the model and analyze results.” (Wikipedians, n.d., p. 144).
The steps involved in SD simulation are:
Define the problem boundary
Identify the most important stocks and flows that change these stock levels
Identify sources of information that impact the flows
Identify the main feedback loops
Draw a causal loop diagram that links the stocks, flows and sources of information
Write the equations that determine the flows
Estimate the parameters and initial conditions. These can be estimated using statistical methods, expert opinion, market research data or other relevant sources of information.
Simulate the model and analyze results.
3.2Data Sources
The following categories of data are the ones that have been identified at this point of the project. There will be a more specific data collection plan developed as the semester progresses. The sponsors will provide the Project team with the following data:
- Enrollment Data (Undergraduate, and Graduate Students).
- Faculty Data (Tenure Track, Tenured, Term, Adjunct, and Research Faculty).
- Research space base data.
- Educational space base data.
4Expected Results
A tool that models the expected trends in both general and research-specific measures of University health, as well as the effect of user-entered hypothetical research investments on these measures. Users will be able to perform sensitivity analysis on the underlying assumptions driving baseline expected trends. Documentation and a user’s manual will be provided.
5Project Plan
5.1Resources
The team working on the project is made up of three (3) full-time students and full-time teaching assistants in the SEOR department: Two (2) systems engineering students and One (1) Operations Research student. The team will use the academic license for the Vensim Software that is provided to them through the SEOR department.
5.2Timeline
Below isthe Work Breakdown Structure (WBS) for this project. These dates and duration for some of the tasks are subject to change based on feedback from our sponsors during the assigned period of the project and our class professor in addition to the lessons will be learnt from our experiments.
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5.3Key Milestones
Milestone / Due DateFebruary
Milestone 1 / Preliminary Problem Definition / 02/02/17 / √
Milestone 2 / Problem Definition & Scope / 02/09/17 / √
Milestone 3 / Project Proposal / 02/16/17 / √
March
Milestone 4 / Progress Report 1 / 03/09/17
Milestone 5 / Progress Report 2 / 03/30/17
April
Milestone 6 / Final Tool / 04/20/17
May
Milestone 7 / Final Website / 05/08/17
Milestone 8 / Final Report / 05/08/17
Milestone 9 / Final Presentation / 05/12/17
Table (1): Project Key Milestones
Figure (3): Milestones Timeline
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Works Cited
Wikipedians (Eds.). (n.d.). Complexity and dynamics: Complexity theories, dynamical systems and applications to biology and sociology. Mainz, Germany: PediaPress.
Malczynski, L. A. (2011).Best practices for System Dynamics Model Design and Construction with Powersim Studio(Rep. No. SAND2011-4108). Albuquerque, NM: Sandia National Laboratories.
Mason achieves highest Carnegie research classification. (2016, February 7). Retrieved February 24, 2017, from
Mason achieves top research ranking from Carnegie. (2016, February 3). Retrieved February 24, 2017, from
Rouse, W. B. (2016).Universities as complex enterprises: how academia works, why it works these ways, and where the university enterprise is headed. Hoboken, NJ: John Wiley & Sons, Inc.
System dynamics. (2017, February 17). Retrieved February 24, 2017, from
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