SYST/0R 699-Project Proposal

Modeling the Mason Research Enterprise

February 16th, 2017

George Mason University SEOR Department Page 0 of 11

Sponsors:Dr. Stephen NashDr. Art Pyster

Supervisor:Dr. Kathryn Lasky

Prepared By:

Noran AbrahamJames LeeChristopher Murri

Table of Contents

1 INTRODUCTION 2

1.1 BACKGROUND 21.2 PROBLEM STATEMENT 21.3 OBJECTIVES AND SCOPE 31.4 STAKEHOLDERS 4

2 SYSTEM REQUIREMENTS 4

2.1 FUNCTIONAL REQUIREMENTS 42.2 USABILITY REQUIREMENTS 42.3 INPUT REQUIREMENTS 42.4 OUTPUT REQUIREMENTS 4

3 TECHNICAL APPROACH 5

3.1 METHODOLOGY 53.2 DATA SOURCES 6

4 EXPECTED RESULTS 6

5 PROJECT PLAN 6

5.1 RESOURCES 65.2 TIMELINE 75.3 KEY MILESTONES 8

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1 Introduction

1.1 Background

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 (GMU,

2016). Comparing GMU to institutions in Top-tier (R1) category of Carnegie Classification, GMU is “the

new kid on the block.” (GMU, 2016). However, since 2012, the GMU community has made major

investments in research to achieve R1 status. Such investments resulted 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,

2016). On February 1, 2016, that dream became a reality, as GMU moved into the Top-tier (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 (GMU, 2016). The increase in

research expenditures was driven by growth in research expenditures in science and engineering, which

doubled during that period (GMU, 2016). The university also increased the number of doctoral degrees

it conferred by 27 percent in that same period (GMU, 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, 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.

1.2 Problem Statement

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.

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Figure (1): An economic model of complex academic enterprises that captures the key flows (Rouse, 2016)

1.3 Objectives 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 is one of the top contributors to growth in

research expenditures of science and engineering, which doubled during the period of 2013-2014

(“Mason achieves top research,” 2016), and VSE has 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:

1. Assessing the overall health of the research enterprise at Mason.2. Examining key indicators relating to income, expenditures, and facilities and the causal relationships

among them.3. Projecting trends on indicators of interest and their dependence on strategic decisions and

investments.4. Examining “what if” scenarios for different investment strategies.

-The team will not be providing any recommendations such as:

1. What is the optimal solution?2. Which investment is better than others?

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1.4 Stakeholders

Primary Stakeholders Secondary Stakeholders1. Sponsors:

Dr. Stephen NashVSE Senior Assoc. Dean

Dr.Art PysterVSE Assoc. Dean for Research

2. VP of GMU Research: Dr.Deborah Crawford

3. Major Decision Makers.

1. Private Sector2. Federal Agencies3. State Agencies4. All Faculty members5. All Students

2 System 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.

2.1 Functional Requirements

FR1: The model shall represent the causal relationships among key indicators.

FR2: The model shall quantify the causal relationships among key indicators.

2.2 Usability 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 model will be available for the user to download).

UR2: The model shall be usable by sponsors and key stakeholders by referring to user guide / manual.

2.3 Input Requirements

IR1: The model shall allow the user to adjust the rate at which each indicator is affected by another.

IR2: The model shall allow the user to 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).

2.4 Output Requirements

OR1: The model shall output trends on indicators of interest.

OR2: The model shall output projected value of indicators of interest.

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3 Technical Approach3.1 Methodology

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 complex systems over a

period of time using stocks, flows, and feedback loops. It is an aspect of systems theory that is used 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” (Wikipedians, n.d., p. 144).

There are a variety of software packages 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 with stock

and flow and causal loop diagrams as shown below in Figure (2). In this model, the stock variable is

measured at one specific time and it represents a quantity of a variable at a point of time, while a

flow variable represents a change during a period of time and is measured over an interval of time.

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Figure (2): shows an example for Stock and flow diagram of new product adoption model (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).

3.2 Data 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.

Enrollment Data (Undergraduate, and Graduate Students). Faculty Data (Tenure Track, Tenured, Term, Adjunct, and Research Faculty). Research space base data. Educational space base data.

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Causal Loop Diagram

A Flow is the rate of accumulation of the Stock

4 Expected ResultsA 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.

5 Project Plan5.1 Resources

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.

5.2 TimelineBelow is the 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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5.3 Key Milestones

Table (1): Project Key Milestones

Figure (3): Milestones Timeline

George Mason University SEOR Department Page 9 of 11

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 √

MarchMilestone 4 Progress Report 1 03/09/17Milestone 5 Progress Report 2 03/30/17

AprilMilestone 6 Final Tool 04/20/17

MayMilestone 7 Final Website 05/08/17Milestone 8 Final Report 05/08/17Milestone 9 Final Presentation 05/12/17

Works Cited

Wikipedians (Eds.). (n.d.). Complexity and dynamics: Complexity theories, dynamical systems and applications to biology and sociology. Mainz, Germany: PediaPress.

Mason achieves highest Carnegie research classification. (2016, February 7). Retrieved February 24, 2017, from https://president.gmu.edu/mason-achieves-highest-carnegie-research-classification

Mason achieves top research ranking from Carnegie. (2016, February 3). Retrieved February 24, 2017, from https://www2.gmu.edu/news/182106

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 https://en.wikipedia.org/wiki/System_dynamics

George Mason University SEOR Department Page 10 of 11