A Project Management Flight Simulator
By T. Michael Toole1 and Cameron Hufford2 Proceedings of the ASCE CRC Specialty Conference
“Management and Leadership Issues in Construction”
Hilton Head, NC March 24-26, 2004
Abstract
The increasing complexity of AEC projects, the call for civil and construction engineering students to understand systems thinking, and the deficiencies in current project management tools all indicate the need for a project management flight simulator (PMFS). A PMFS will allow aspiring project managers to learn project management principles by functioning as managers of virtual project systems. This paper summarizes the fundamentals of systems thinking and system dynamics and the historical use of management flight simulators to teach business management principles. The paper then describes the system dynamics model that underlies a construction PMFS developed by the authors using Ithink software. The decisions that users of the PMFS must make and the causal relationships within the model that determine the outcomes of the users’ decisions are summarized. One of the key benefits of a system
dynamics-based model is that it facilitates the inclusion of qualitative variables such as goodwill, competence, and fatigue. PMFS users therefore learn that successful project management
involves managing people, not just tasks.
Introduction
Effective teaching of project management skills to architectural, (civil) engineering and
construction (AEC) students is a timely and important topic for several reasons. Perhaps more than other disciplines, engineering or otherwise, AEC work is project based. Moreover, due to the increasing technological complexity of constructed facilities, the market demand for shortest possible completion time, and the fact that concurrent design and engineering (i.e., fast tracking) is becoming more common, construction projects are becoming increasingly more complex and difficult to manage. Equipping AEC students with solid project management skills is therefore critical for the future of the individual professions and the AEC industry as a whole.
Also relevant is the suggestion by faculty and practitioners that civil engineering graduates need to possess more systems thinking skills (Revelle, Whitlach and Wright 2004). The ability to demonstrate holistic thinking is required to manage large, complex projects, to manage the deteriorating and under funded U.S. infrastructure (ASCE 2003), and to integrate sustainable engineering approaches into the profession. A popular systems thinking book, The Fifth
Discipline (Senge 1990), has increased systems thinking awareness among business managers.
1 Assistant Professor, Dept. of Civil & Environmental Engineering, Bucknell University, Lewisburg, PA 17837.
2 Undergraduate Civil & Environmental Engineering student at Bucknell University, Lewisburg, PA 17837.
Project management researchers who are familiar with system dynamics have written that current project management tools are not adequate for managing construction and other dynamic
projects (Sterman 1992, Love, Rodrigues and Bowers 1996, Love et al 2002). The structures of project management systems are too complex and volatile and contain too much inherent
randomness to be managed effectively by linear, deterministic tools that focus on one portion of the system at a time. These researchers have suggested project management principles and tools can be improved by integrating system dynamics into them. Specifically, as noted by Rodrigues and Bowers (1996), the application of system dynamics to project management has been
motivated by three primary goals:
• To take a holistic approach (recognizing that the whole is greater than sum of the parts);
• To better understand non-linear behavior within project management systems;
• To provide a learning laboratory tool for aspiring project managers.
One potential tool for addressing the challenges posed by the above factors is a project
management flight simulator (PMFS), that is, a computer simulation that enables aspiring project managers to learn project management principles and practice effective decision making in a dynamic setting. A PMFS provides the potential to achieve all three goals. Students can learn project management principles by studying the individual causal relationships within the system dynamics model that underlies the simulation. Students can also learn that such relationships are part of an overall system, the structure of which leads to non-linear system behavior and
significantly influences the outcome of the simulation. Because a system dynamics-based PMFS allow the inclusion of qualitative or “soft” variables, students also gain an understanding of how the attributes and actions of people, such as bosses and assistant project managers, can affect a project’s progress. Students are then forced to consciously formulate an approach to managing those people in order to achieve project goals. By allowing students to serve as virtual project managers, the model provides a learning tool that allows them to experiment, observe the outcomes, and revise their mental models to match the simulation model.
PMFS also provide the opportunity to meet pedagogical goals unrelated to system dynamics.
Numerous studies have shown that traditional teaching methods are not resulting in deep and lasting comprehension (National Research Council 1999). Students who multi-task nearly every minute outside of the classroom have trouble staying focused in passive learning environments.
Engineering faculty across the country are realizing that the traditional lecture method needs to be supplemented with innovative pedagogies that promote active, collaborative and problem- based learning (Prince et al 2002).
Teaching project management can be particularly challenging because the concepts are quite abstract. It has been the first author’s experience that engineering students typically learn quantitative PM techniques such as how to construct a network diagram (i.e., CPM or PERT) quite easily, but they have trouble fully grasping management concepts because they often have never worked in an organization. A PMFS may make the abstract concepts become more meaningful by embedding them in a context that simulates the “real world” and requiring them to perform on high levels of Bloom’s taxonomy (Bloom and Krathwohl 1956). That is, rather than merely memorizing terms and knowing how they are applied, students will be required to demonstrate analysis, synthesis and evaluation. Students’ learning will therefore be deeper than achieved through traditional lecture-based teaching.
After summarizing the basic principles of system dynamics, this paper describes a project management flight simulator being developed by the authors. It is hoped that this simulator will not only be used to improve the teaching of project management, but also to facilitate the
integration of systems thinking into project management.
Introduction to system dynamics and management flight simulators
To help the reader understand why current project management tools are deficient and why a systems-based project management simulation program is needed, it is appropriate to provide the reader with background information and key principles of systems dynamics.
System dynamics is a methodological approach and set of tools based on systems thinking.
System dynamics was originated by Prof. Jay Forrester at MIT and has slowly spread to other universities and into industry. Initially used to model electro-mechanical processes, system dynamics has been used extensively to model organizational, social, economic, biological and other types of systems.
Fundamental system dynamics concepts, assumptions, and tools that underlie the project management flight simulator are summarized below. These fundamental concepts are drawn from Forrester (1961, 1969, 1971), Senge (1990), and Sterman (1994).
• Systems can be defined simply as a collection of connected things, that is, a set of elements that influence one another. The things may be easily quantifiable, such as the revenues of a firm, or more intangible and qualitative, such as goodwill, motivation, and burnout.
• Much of the system structure and the underlying relationships can be depicted graphically using causal loop diagrams (CLD). For example, Figure 1 depicts a portion of a simple project management system. If the actual rate at which a task is accomplished—task productivity—increases, schedule variance will decrease. If schedule variance increases, it will cause the project manager to direct that overtime occur, which will increase the rate of task accomplishment. (A plus sign in CLDs indicates that when the variable next to the tail of an arrow increases, the variable next to the head of the arrow also increases.
A minus sign indicates that when one variable increases, the other variable decreases.) Figure 1: Causal Loop Diagram Example
• A key benefit of creating causal loop diagrams is that individual mental models can be elicited and compared. People often incorrectly assume that every one shares their understanding of how one thing affects another. When people disagree on how a specific
problem should be resolved, it is often because they share different mental models of the system in which the problem is embedded.
• Portions of the structure of a system may include feedback loops, that is, element A affects element B which affects element C which affects element A. The causality around the loop typically occurs over time, that is, a change in element A takes several iterations to cause changes in the other variables within the feedback loop. Feedback loops can be positive or negative. Positive loops are self-reinforcing; that is, variables within positive loops will continue to increase indefinitely. Negative loops are self- balancing; that is, variables within negative loops will stabilize over time.
• The pattern of changes in the values of variables within a simple system over time (i.e., the shapes of plots of the variables) can often be predicted based on the structure of the system, specifically, the number of accumulating variables within feedback loops. On the other hand, the values of variables within more complex systems—that is, systems that include many accumulating variables and more than one feedback loop—may not be easily predictable. In fact, the patterns may be highly counterintuitive.
Management flight simulators (MFS) were created to teach systems thinking principles using simulation contexts that students could understand without advanced knowledge of a specific industry or underlying technologies. Just as flight simulators allow users to be pilots in virtual cockpits, MFS allow users to be managers of virtual organizations. One well known MFS is the People Express MFS developed at MIT’s Sloan School. As the virtual CEO of the infamous discount airline, a user of this MFS is required to make strategic decisions each quarter, such as how many planes to purchase, how many employees to hire, and what prices to charge. The simulation then advances one quarter and allows the user to observe the current state of the company. Users quickly realize that what seem to be rational decisions inevitably cause their virtual company to follow a pattern very similar to the real People Express of rapid growth followed by serious problems that lead to bankruptcy. Harvard Business School has marketed a system dynamics simulation called the Balanced Corporate Scorecard in which the user is the CEO of a virtual technology-based company.
It should be mentioned that several simulation models relating to project management have been created by other researchers. AROUSAL and COPLAN were used at M.I.T. in the 1980s and 1990s to allow users to run a construction organization; however, they were not system
dynamics-based. Pena-Mora and Park (2001) used causal loop diagrams and a detailed computer simulation to investigate the effect of concurrent design and construction (fast-tracking) on building completion time and cost. Love et al (2002) used causal loop diagrams to gain insight into the effect of scope changes on construction projects. The PMFS presented here differs from these previous models in two fundamental ways. First, it focuses only on managing the
construction portion of a project, not design and construction or a construction organization.
Second, the system structure underlying the model is intended to be analyzed by users to increase its value as a learning tool. The structures underlying AROUSAL and COPLAN were not
accessible to users and the other two simulations mentioned above were intended to be used only for analysis, not for student learning.
PMFS context and operation
The authors have created a PMFS that is intended to improve the teaching of project
management concepts and skills. Because the PMFS is to be used in both a civil engineering project management class as well as a construction engineering elective, the virtual project that the model user is required to manage is the construction of a commercial office building. The simulation was created using Ithink software, a proprietary system dynamics-based software that is sold by High Performance Systems. (Ithink is very similar to STELLA, which has been used by academics for several decades.) The model is not yet ready for public distibution. It is operational but its graphical user interface is still being refined.
The simulation begins with a user briefing. She is told she is the project manager for a general contractor who has a fixed price contract with the client for the construction of an office building, which will be completed using fixed price contracts with nineteen subcontractors.
Each subcontractor is associated with one task that has a predetermined contract amount, manhour estimates, and duration. Her goals as project manager are to complete the project by the predetermined deadline within the predetermined budget, to keep the client happy, and to have no serious injuries on site.
After the briefing, the user is required to make a series of initial decisions. First, she must hire two or three assistant project managers (APM) from a pool of four candidates who range in experience, salary demands, and temperament, and assign the tasks for which each APM is responsible. She then chooses whether her new staff should be trained, the amount of autonomy she will give them, and how she will motivate them (ignore them, give them verbal praise, give them an occasional afternoon off, or award them a cash bonus). The user also chooses the relative emphases on deadlines, cost, safety and quality, the frequency of site visits and the frequency and mode of communication with her APMs, boss and client.
Once the user has entered her initial decisions through the graphical user interface, she hits the
“Run” button, causing the software to immediately simulate one week of activity on the project.
The user can then quickly identify the status of key project variables, such as schedule and cost variances associated with individual tasks and the overall project, and the overall levels of safety, quality, and client and boss satisfaction on the project. The user then has the opportunity to change the relative emphasis on specific project goals or communication frequency before directing the model to simulate through a second time period. Throughout the simulation the user can decide to crash individual tasks, which decreases task duration but increases project cost, increases the injury rate, and decreases quality. The user continues to advance the
simulation one time period at a time until the project is completed. The user’s overall success is determined by the final completion date, cost, accident history, and client and boss satisfaction levels.
Model structure
Figures 2-5 provide graphical depictions of key portions of the system dynamics model underlying the PMFS. The model consists of approximately 150 unique variables, of which approximately 50 variables play a principle role in controlling and determining outcomes for the
Figure 2: Task Sectors
T ask Complete
Daily Planned Manhours
APM Productivity Assignment
T ask Crash
Task Daily Labor Cost Task
Manhours Remainiing
Task Manhours Completed
T ask Manhours Worked
Value Per Day
Task Manhours Worked Per Day
Task Crew Size
Task Workday
Length Task Complete
Task Percent Complete Actual T ask Projected
Remaining Duration
Task Working
Task T otal Manhours Crash T ask?
Task Percent Complete Variance
Accident?
Task Planned Workday Length
Task Planned Manhours Remaining
Task Planned Manhours Completed Task Planned Working
T ask Planned Manhours Worked Per Day
Task Planned Percent Complete T ask Planned
Remaining Duration
Task Planned Duration In Days
Task Wage Rate Task
Budget T ask BCWP
Task ACWP T ask Overtime
Wage Rate
T ask Overtime Accumulation
T ask Overtime Worked Task Overtime
Man Hours Worked T ask T rigger
Task Overtime Spending T ask
Overtime Expenditures Task ON?
Change In T ask Scope
Change In T ask Scope Flow
Change Scope?
Change Scope Extent Per T ask
T ask Manhours
T ask Fatigue
Task Fatiguing
Task Fatigue Dissipation
T ask Workday
Length
T ask Complete
T ask Scope Wage Rate Change Scope
Extent Per Task
Change Scope?
Task Overtime Man Hours Worked
Task Manhours T ask Schedule
T ask Planned Schedule
T ask Budget
T ask Overtime
T ask …
Figure 3: Assistant Project Management Sector
APM Competence
APM Goodwill Change in APM
Competence
Change in APM Goodwill
+ Total Project
Fatigue Motiv ation Method
APM Independence
Av erage APM Competence
Number Of APMs
APM Selected?
Frequency of Communication
Emphasis On Deadlines
Emphasis on Saf ety APM Productiv ity APM Salaries
CPM Scheduling Chosen?
Inv olv ed In Planning Training
APM Workload APM Sector
Figure 4: Project Progress and Cost Sectors
+ Project Overtime Spending
APM Salary Bank Planned Project
Man Hours+ Sum of Planned Task
Man Hours
Project Planned
Percent Complete + Sum of Planned Task
Man Hours Completed
Project Percent
Complete Variance
Project Percent
Complete Actual
+
Sum of Task
Man Hours Completed + Sum of Total
Task Man Hours + APM Salary
Summer Change In Scope Cash
Motivation Method ACWP Construction
Budget + Sum of Task
ACWPs
BCWS
Project Budget
BCWP SV CV CPI
SPI
Cash RemainingIn Budget Total Expenditures
To DateSpending
+Daily
Labor Cost General Overhead Overtime Expenditure
To Date
Overtime Spending + Planned Project
Duration in Days
Change In Scope
Cash Flow + Sum Of
Construction BudgetsSum of
Overtime Spending Total Overtime
Expenditures
Change
Scope? Planned Project Sch…
Actual Project Schedu… Project Budget and EVA
Figure 5: Project Quality, Safety, Client and Boss Sectors
+
Quality and Saf ety Variance Weighting
+
Quality and Saf ety Variance Weighting
Accident?
Project Quality Change in Project Quality
Emphasis on Quality
Accident Rate Probability Per Man Hour
Change in Accident Rate Emphasis on
Saf ety
PM Site Visit Frequency
Change in Total Accidents
Total Number of Accidents
Client Goodwill
Rework1 Emphasis On
Deadlines
+ Sum of Total Manhours Worked
Per Day Accident Frequency
Emphasis On Budget
Emphasis On Deadlines Emphasis On
Budget
~ Quality Variance
Weighting
~
Accident Variance Weighting
+
Total Project Fatigue
Boss Goodwill Changing Boss Goodwill
Changing Client Goodwill
Project Percent Complete Variance Total Number
of Accidents
CPI SPI
Accident Frequency
Project Quality
~
Variance Goodwill Weighting Quality Sector
Saf ety Sector
Client & Boss Goodwill
simulation. The remaining variables are analogous to accounting variables for performing cost and progress calculations.
There are three basic types of variables in the model: stocks (the rectangular boxes), flows (the pipe and valve type structure) and converters (circles). Stocks act as reservoirs, storing their previous values and causing things to accumulate over time. The accumulation or depletion of things within a given stock is governed by the flows entering and leaving that stock. That is, flows act as valves by regulating how quickly things accumulate within a stock or are dispensed out of a stock. A flow can operate based on a fixed, user-determined, equation, or can vary with time, based on the inputs from other stocks, flows and converters. Converters do not store previous values, but merely take one or multiple inputs, perform a mathematical operation on that input and provide an output to another converter or flow.
On the whole the equations governing the relationships between variables are relatively
straightforward and usually consist of basic algebra and one or more “If/Else” statements. Due to space limitations, the equations that govern the relationships between the variables of the model are not provided here but can be found at http://www.facstaff.bucknell.edu/ttoole/PMFS Equations.doc .
The various interconnected variables are grouped into 12 sectors (identified by the solid boxes around a set of variables) for visual clarity. One of the five task sectors controls how much work is accomplished on a given task each time period. Other task sectors take this and other
information and determine the progress, cost, schedule variance and other calculations for that particular task. The progress and cost data are then taken from each individual task and combined to perform an earned value analysis (EVA) for the entire project. Other sectors take user inputs and determine the accident rate, the overall project quality and the happiness of the owner and user’s “boss” for the project. One of the most critical sectors is the APM sector. This sector determines how effective each of the APMs is at moving the project towards completion.
All of these sectors are interconnected and influence each other, some in very obvious, and some in very counterintuitive ways.
Figure 2 shows the five sectors associated with task 1’s planned and actual progress and costs, use of overtime and resulting worker fatigue. The portions of the model associated with tasks 2- 19, which are identical to the portion shown in Figure 2, are not shown. Figure 3 shows the APM sector, which includes the competence, workload and level of goodwill (i.e., morale) of the assistant project manager 1. Identical sectors for the other APMs are not shown. Figure 4 shows the three sectors associated with the budget and progress of the entire project, which are
aggregates of the budget and progress of the nineteen tasks. Figure 5 shows the three sectors associated with the levels of quality and safety on the project and the goodwill of the project client and user’s boss.
Several key relationships substantially affect the project outcomes. First, each APM’s
productivity influences the progress achieved on all tasks for which the APM is responsible (see Task Schedule Sector in Figure 2). An APM’s productivity is determined by his competence, goodwill towards his boss (the user), his workload and the user’s relative emphasis on deadlines (see APM sector in Figure 3). APM competence always grows as the project progresses but is
elevated initially if he is involved in project planning and received training, and is accelerated by frequent communication with the user. APM goodwill reflects his workload, whether he was involved in project planning and received training, and the user’s choice of motivation method.
Exposing users to the portion of the model involving APM productivity helps them understand one of the key trade offs that project managers must make. Investing in APMs through higher initial salaries (because initial salaries are commensurate with experience), involving them in project planning, training them, and effectively motivating them results in immediate budget pressures, but generally pays off in the end due to increased productivity.
Another key set of variables are the user’s emphases on deadlines, budgets, quality and safety.
The model assumes that these criteria are somewhat mutually exclusive. That is, one cannot effectively emphasize all four criteria simultaneously. For example, if deadlines are emphasized, then quality and safety will suffer to some extent. The principle is that the user should put more emphasis on the one or two criteria that seem to be most important at each point in the
simulation.
The need for trade offs between project goals is reflected in the option to crash any task.
Crashing a critical task means requiring a certain amount of overtime, which increases the rate at which the task is accomplished and decreases the task duration. But overtime is paid for from the project budget, so it increases cost variance while reducing schedule variance (if the task is critical). Overtime also may lead to worker fatigue, which decreases quality and productivity and increases the probability that a lost-time injury will occur.
A key project management principle that users learn is that project managers need to ensure they receive timely information about the status of critical tasks, and then exert timely and effective project control. Users are not provided the status of each task unless they intentionally seek it.
Because tasks may not progress as planned due to external events or lower APM productivity, it is important that users monitor the status of critical tasks at a minimum and then crash one or more tasks if appropriate.
The model includes less important but debatable causal relationships for boss and client goodwill towards the user (see Client and Boss Goodwill sector in Figure 5). Not surprising, both boss and client goodwill are affected by schedule variance. In addition, client goodwill reflects the overall quality level while boss goodwill reflects cost variance, client goodwill, and injury history. (Client goodwill does not depend on cost variance because her contract with the user’s company is fixed price.)
The final project management principle that should be mentioned is that unanticipated events happen regularly on construction projects and project managers must respond appropriately to best achieve project goals. Several one-time events occur either at predetermined points in the simulation or in response to some variable in the model reaching a critical value. These “crises”
range in severity and include events such as differing site conditions, design conflicts, material delays, change orders, and an OSHA inspection after a serious injury occurs.
One of the key benefits of the model is that it can be viewed by users after running the
simulation in the hopes that they might be able to correlate behavior seen during simulation with the structure of the model, then to use this deeper comprehension to change their actions both in the simulation and on an actual construction site. The simulation can also be run multiple times with different goals. One user might be directed to place an emphasis on safety, while another might try to minimize cost, while another tries to minimize construction time. The results from each of these simulations can then be compared to enhance a deeper understanding of how different priorities influence project management success.
Conclusions
This paper has suggested that a project management flight simulator could help integrate system dynamics into project management and improve teaching of project management principles. The overall structure and key individual causal relationships of a PMFS for the virtual management of a construction project created by the authors was described. The authors will gladly provide a copy of the model to interested readers who have access to Ithink software.
Project management tools and simulations have historically failed to include variables associated with people because those variables were not easily quantifiable. This model is valuable because it allows users to realize how “soft” variables associated with people can have a significant influence on the progress or quality of a project, sometimes in a very counterintuitive manner. It is hoped that users of the PMFS will understand that successful project management requires effective management of people as much as it requires effective management of tasks.
It is believed that the overall structure of the construction PMFS described in this paper could be modified with relatively little difficulty to create an engineering design PMFS. Instead of APMs, the user would have a design team composed of junior engineers. Instead of performing the work associated with construction trades, tasks would consist of design work packages from concept through construction documents. Instead of construction site injuries, accidents could be associated with conflicts between design documents or not meeting clients’ functional
requirements.
As is true for all models, the value of the PMFS described here will not be realized until the model is validated. The validation is planned to occur through four processes. First, it is hoped that academic researchers will provide constructive criticism of the model based on this paper and their use of the PMFS. Second, fourth year CEE students at Bucknell University will be required to use the PMFS, discuss the underlying model, and comment on their perception of its effectiveness. Third, graduate engineers working for contractors and construction managers will be asked to use the PMFS and comment on its accuracy. Fourth, the outcomes of several real construction projects will be compared to simulation outcomes when the decisions made on the real projects are entered into the PMFS.
The simulation program described in the paper may be viewed as merely a modest programming effort to be used for teaching purposes. The authors, however, are cautiously optimistic that this and future project management flight simulators may make meaningful contributions to several larger goals, including:
• Increasing the depth of student learning of project management principles;
• Improving project management principles by providing researchers and practitioners with a tool for eliciting and discussing mental models of project management systems and processes; and
• Increasing the integration of system dynamics principles and tools into the project management body of knowledge and into civil and environmental engineering curricula.
Acknowledgements
Melissa Lawson (Bucknell BSCE 2002) created an earlier version of a PMFS that was helpful in developing the current model.
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