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Moving Beyond the Threshold of System Dynamics: Thoughts for New Practitioners

I’ve heard students and colleagues say that learning system dynamics transformed their thinking–it gave them ways to understand complex problems in their field from a fresh perspective. “I can’t think any other way,” they’ll say. Practices like system dynamics are considered a threshold concept. Threshold concepts have been described by Meyers and Land (2008) as:

  • Transformative – it changes the way a student views a discipline. 
  • Troublesome – especially when the concepts are counter-intuitive or conceptually difficult. 
  • Irreversible – difficult to unlearn. 
  • Integrative – once learned, bring together previously unrelated concepts.
  • Reconstituted – shifts learner subjectivity in oscillations/wrestling with conceptual domains, often depicted by messy journeys back and forth and across conceptual terrains (Cousin, 2006). 
  • Liminal – leaves the learner in a suspended state of partial understanding (“stuck places”), in which understanding is based on mimicry or a lack of authenticity–it’s an uncomfortable shift that invokes questions of identity and or paradoxically a sense of loss. Similar to adolescence, one does not feel that they have arrived–not yet adults and no longer children. 

Those of us who have started this journey know there is no end to learning and a sense of imposter syndrome that comes with trying. It’s not something you simply do over the summer and get your stamp of authenticity. Becoming a system dynamicist is a commitment to lifelong learning. Practicing. Feeling inadequate. The skill decay rate is high. And yet, the insights are transformational.

To those of us who are starting this journey of learning, we must wrestle with this liminality. I suspect that few of us feel that we have “made it” to the end of system dynamics learning. When compared to some well-established fields, such as physics or biology, ours is relatively new–a mere child in the many disciplines of methodological inquiry. There are many questions to be pursued and innovations to be encountered. It is intimidating to put oneself out there to grow, so with that, I offer some ideas that I’ve done or seen others do in the field:

  1. Seek mentorship. Find people whose work you admire, read their papers/projects and talk to them frequently. Ask questions. Take notes.
  2. Attend SD conferences. The best way to learn a new language is to be immersed in the field and everyday language: the annual conference. There are few places where you don’t have to explain causal loop diagrams or feedback (not in reference to a suggestion) to have a deeper conversation on practice.
  3. Find peers and ask “dumb” questions together. At my first SD conference, peers sat me down to conduct a gentle intervention prior to my first presentation. They said, “Saras, it’s System. Dynamics. Not SystemS Dynamics. One system. Many dynamics.” Novice crisis averted (somewhat). Thanks, Jill and Mary Jo!
  4. Read the classics and model them. The System Dynamics Review is a great place to start going deeper. Additionally, reading seminal texts and rebuilding models can help hone your skills. It’s especially illuminating to read “conversations” between folks in the field to understand what debates have persisted over time. 

Are you at the threshold? Reach out and lean into this space of learning. For more seasoned practitioners, what worked for you? Use the comments below to share your wisdom!

General Motors OnStar

Name The General Motors OnStar Project
Modelers Vince BarabbaChet Huber, Fred Cooke, Nick Pudar, Jim Smith, Mark Paich
Client General Motors
Client Type Corporation

The Official Website is the official website in which you can become a member, get familiar with the services and purchase a plan.

The Issue You Tackled

In 1997, General Motors (GM) assembled a project team to develop its OnStar telematics business. Telematics is the provision of communications services to cars, including crash notification, navigation, Internet access, and traffic information. OnStar is GM’s two-way vehicle communication system that provides a variety of services that enhance safety, security, entertainment, and productivity. At the time, GM faced fundamental strategic decisions with respect to OnStar. The default and safe strategy was to market OnStar as a car feature that would improve vehicle safety and security. An alternate strategy was to view OnStar as a service business that could contribute greatly to GM’s profits.

What You Actually Did

GM formed a project team to consider alternative strategies for OnStar. GM makes important strategic decisions through the dialogue decision process, in which the project team interacts with the decision board that is responsible for actually making the decision and committing resources. Dynamic modeling can be a part of this process.

In this case, application of modeling was difficult. In the vehicle business, GM has decades of experience and plentiful historical data. Modelers can build on a wealth of previous analyses and examples of best practice. The OnStar business was very different in that the telematics market did not exist. To cope with the inherent uncertainty, we needed a modeling process that would allow integration of various methods and data sources. A simulation model was our core tool in the OnStar strategy project. The final model had six key sectors: customer acquisition, customer choice, alliances, customer service, finances, and dealer behavior.

In late 1997, the project team recommended a very aggressive strategy that included installation on all GM vehicles, recruitment of other manufacturers into the OnStar system, making the first year of service free and aggressively pursuing alliances with content partners.

The Results

Through 2001, the implementation of the OnStar business strategy has progressed very much as expected. The project contributed to creating a new enterprise mental model for GM, in which the transactions revenue is augmented with a stream of revenue from service businesses like OnStar. The OnStar project also created the new telematics business which did not exist before GM implemented its strategy. Today, Wall Street analysts project that the industry will grow to $12 billion over the next 10 years. By far, OnStar’s most important contribution is saving lives. OnStar answers thousands of emergency calls each month and has often made the difference between life and death.


A multimethod approach for creating new business models: the General Motors OnStar project Download

Did You Know?

System Dynamics Application Award

The System Dynamics Applications Award is presented by the Society every other year for the best “real world” application of system dynamics. In 2007, the Society awarded its first Applications Award to Vince Barabba, Chet Huber, Fred Cooke, Nick Pudar, Jim Smith, and Mark Paich for their work A Multimethod Approach for Creating New Business Models: The General Motors OnStar Project.

To see the citation that was made by James Lyneis at the conference, please follow this link(Jul 2007)

Pharmaceutical Product Branding Strategies

Pharmaceutical Product Branding Strategies

Name Pharmaceutical Product Branding Strategies — Simulating Patient Flow and Portfolio Dynamics
Modelers Mark Paich, Corey Peck, Jason Valant
Contact Jason Valant or Corey Peck
Client Numerous Pharmaceutical Companies
Client Type Corporation

The Official Website

Lexidyne, LLC specializes in helping clients understand and leverage key cause–and–effect relationships using the principles of System Dynamics. Combining strong facilitation skills with powerful simulation tools and over 100 years of collective System Dynamics experience, the Lexidyne team offers consulting solutions to government agencies, non–profit organizations, and several Fortune 500 companies. Headquartered in Colorado, Lexidyne works with clients across the country and around the globe.

The Issue You Tackled

Pharmaceutical companies face many complexities guiding a new drug through the development process toward the launch of the product — a complicated endeavor involving numerous milestones and a large investment of human and financial resources.

The efforts of the Brand Plan team result in a comprehensive look at the disease marketplace, the competitive landscape, currently available and pipeline treatment options, the assessment of the unmet medical needs in the market, and other information designed to inform decision makers about the conditions into which a new compound might be introduced. From a marketing standpoint, however, the key outcome of the Brand Planning process is the concept of brand positioning. Brand positioning helps establishing a series of product strategies created to leverage the collective knowledge of the disease market and effectively use resources to increase uptake of the new product. The strategies are often categorized by areas of target influence, such as patient and or physician segmentation, impact on the regulatory environment, effect on pricing/reimbursement, publication strategy, etc.

The typical brand planning process can be hindered in four key ways:

  1. Misapplication of product analogs
  2. Failure to leverage the institutional knowledge of cross-functional team members
  3. Inherent limitations of static approaches
  4. Maintaining consistent assumptions when evaluating alternative strategic options

Often there is a lack of integration between forecasts and product strategies. In the prelaunch timeframe however, rigorously testing the effects of possible strategies is impossible without an operational way to evaluate the expected outcome of strategic marketing decisions.

What You Actually Did

This updated Second Edition of Pharmaceutical Product Branding Strategies details how marketers, forecasters, and brand planners can achieve optimal success by building internally consistent simulation models to project future behavior of patients, physicians, and R&D processes. By introducing the reader to the complexities facing many pharmaceutical firms, specifically issues around cross-functional coordination and knowledge integration, this guide provides a framework for dynamic modeling of interest to several pharmaceutical markets, including epidemiology, market definitions, compliance/persistency, and revenue generation in the context of patient flows or movements.

Using clear terminology, Pharmaceutical Product Branding Strategies provides a solid framework for dynamic modeling to help marketers, forecasters, and brand planners to successfully:

  • Predict the behavior of patients, physicians, and R&D processes
  • Build a successful brand management strategy
  • Target a wider audience with your product
  • This didactic guide discusses the complexities faced by many pharmaceutical firms and explains how dynamic modeling effectively addresses these problems in a systematic way. The positive effects of this method are supported by articles from recent business publications, literature reviews, definitions sections, and collectable market-level data. Dynamic modeling is also compared and contrasted with other existing techniques to give the reader background information and context before initiating a plan.

Strategies highlighted as part of a strong brand planning formula include:

  • Cross-functional coordination and knowledge integration to assess the patient’s needs
  • Diffusion, segmentation quantification, and ultimate calibration to encourage doctors to adopt your product
  • Choice models, conjoin analysis,, competitive sets, data collection/estimation, and market calibration to create an “attractive” treatment for consumers
  • Integration of three basic analysis platforms (patient dynamics doctor adoption, and treatment attractiveness) to sell your brand.

A “typical” disease market model would follow the following evolution (Doctor Adoption and Portfolio Model examples follow similar processes):

  1. Begin by leveraging the system dynamics principles of stocks and flows to establish epidemiological projections for specific disease markets
  2. Often times a combination of modeling approaches are used within this framework

    a.System Dynamics models provide a clear aggregate view of epidemiology dynamics.

    b.While Agent Based models support the inclusion of more extensive segmentation and discrete dynamics.

  3. Each patient’s journey can be virtualized based on probabilities that are tied to conditional probabilities related to their micro-demographic epidemiology status.
  4. Once robust epidemiological underpinnings have been established, we analyze longitudinal treatment data to distill segmented therapy dynamics that are incorporated into the simulation structure thus creating opportunity for strategic insights.

The Results

Numerous pharmaceutical companies have adopted this dynamic modeling approach to evaluate disease markets, doctor adoption, and the R&D pipeline process. Over 100 models have been implemented across numerous indications both in US markets and internationally.

These models have been instrumental in creating optimal strategic initiatives and have enhanced the forecasting process. These models also often serve as the repository for analytics from “big data” as well as institutional team knowledge about the disease area and provide a transparent shared view of data driven dynamics and market assumptions. The “what if” scenario testing capability of the models provides these organizations with a tool to test marketing strategies and evaluate hypothetical changes in future market evolution dynamics, allowing these organizations to understand implications of an uncertain future.


Pharmaceutical Product Branding Strategies: Simulating Patient Flow and Portfolio Dynamics Download

Did You Know?

System Dynamics Forrester Award

The Jay Wright Forrester Award recognizes the authors of the best contribution to the field of System Dynamics in the preceding five years. In 2010, the award was presented to Mark Paich, Corey Peck, and Jason Valant of Lexidyne, LLC for their winning work Pharmaceutical Product Branding Strategies: Simulating Patient Flow and Portfolio Dynamics, published by Informa Healthcare; 2nd edition March 2009. More information on this book can be found at this link.

The citation and winners’ speech (delivered at the award ceremony in Seoul) has been published in full in the System Dynamics Review.
(Jul 2010)

Polio Eradication

Polio Eradication

Name Polio Eradication
Modelers Kimberly M. Thompson,and Radboud J. Duintjer Tebbens
Client World Health Organization (WHO)
Client Type NGO

The Issue You Tackled

Following successful eradication of smallpox and impressive progress in the elimination of polio in the Americas, in 1988 the World Health Assembly committed to global eradication of wild polioviruses by the year 2000. By 2000, the Global Polio Eradication Initiative (GPEI) had significantly reduced the global circulation of wild polioviruses. However, in 2002–3, faced with insufficient funding to continue intense vaccination everywhere, the GPEI focused its vaccination efforts. At the time, wild polioviruses continued to circulate in six countries, but many other countries remained vulnerable to importation. Political and logistical challenges led to outbreaks and exportations, and between 2004 and 2006 wild polioviruses appeared again in previously polio-free African and Asian countries.

Toward the end of 2005, debate began about abandoning the goal of eradication. How could the world continue to justify the significant use of resources (both financial and human) on polio, particularly with the number of cases globally already so low and so many other disease control and health services programs in need of resources? In 2006 a prominent editorial questioned whether polio eradication is “realistic” and expressed concern that “international assistance for polio could have negative effects on other public health efforts”. The editorial suggested that “the time has come for the global strategy for polio to be shifted from ‘eradication’ to ‘effective control’”.

What You Actually Did

Given our then current work on assessing the risks, costs, and benefits of post-eradication policies we could use many of the components we previously developed to model a shift from eradication to control. Notably, our dynamic disease outbreak model for polio allowed us to estimate potential numbers of cases. Our analysis came at a critical time. In February 2007, the WHO Director-General, Dr Margaret Chan, convened an urgent stakeholder consultation to discuss the option of switching to control. We had the opportunity to present the preliminary results of this work at that meeting.


The Results

Following publication of the paper, an article about the paper published in the same journal as the editorial mentioned above noted that our analysis provided “a nail in the coffin for the idea that there is a cheap and painless way out”. Since then, efforts have continued to focus on finding the resources needed to complete eradication and on dealing with the other complex challenges that remain.

National and global health leaders and financial supporters have recommitted to completing eradication, and this has led to several hundreds of millions of dollars of resources.


Using System Dynamics to Develop Policies That Matter: Global Management of Poliomyelitis and Beyond Download
Economic analysis of the global polio eradication initiative Download
Economic benefits of the global polio eradication initiative estimated at $40-50 billion Download
Modeling Global Policy for Managing Polioviruses: An Analytical Journey Download
Eradication versus control for poliomyelitis: an economic analysis Download



Client Centers for Disease Control and Prevention (CDC)
Authors/Consultants Jones AP, Homer JB, Murphy DL, Essien JDK, Milstein B, Seville DA

Diabetes mellitus is a growing health problem worldwide. In the United States, the number of people with diabetes has grown since 1990 at a rate much greater than that of the general population; it was estimated at 20.8 million in 2005. Total costs of diabetes in the United States in 2002 were estimated at 2 billion.

Health planners in the National Center for Chronic Disease Prevention and Health Promotion of the Centers for Disease Control and Prevention used system dynamics simulation modeling to gain a better understanding of diabetes population dynamics and to explore implications for public health strategy. A model was developed to explain the growth of diabetes since 1980 and portray possible futures through 2050.

The model simulations suggest four characteristic dynamics of the diabetes population.

First, it shows obesity’s role in driving the growth of prediabetes and diabetes prevalence.

Second, the model quantifies the “backing up” phenomenon (in which reduced outflow from a population stock causes a buildup in that stock) that may undercut the benefits of management and control efforts. Third, management and control efforts alone are unable to reduce diabetes prevalence in the long term. Fourth, there are significant delays between primary prevention efforts and downstream improvements in diabetes outcomes.

Project Management at Fluor

Project Management at Fluor

Fluor saves $1.3 billion in System Dynamics-based project management.

#United States #Engineering #Fluor #Corporation


Fluor is one of the world’s largest engineering and construction firms, with 2008 revenues over $20 billion. The US-based firm operates in every major business sector and geography.


A large part of Fluor’s work is organized in the form of projects, which are typically market-driven with aggressive cost and schedule targets and evolving client needs. It is the tension among these different objectives that is often the underlying dynamic for generating changes on projects. In an initiative by Fluor’s Chairman, a comprehensive quantitative review examined all Fluor projects over several years. For many in the industry, there is a misperception that contractors improve their performance with more changes. This company-wide review was unequivocal in refuting that notion. There is a clear, unambiguous relation between the level of changes and the cost and schedule performance of projects: more changes bring ever-worsening performance on projects.


After Fluor had identified and quantified the business need for improving the practice of project change management, two external consultants first built and piloted and validated a project model to assess change impacts on several initial projects. In the four years since then, the model has been used in the “Change Impact Assessment” system to conduct thousands of analyses on over 100 client projects. Fluor projects analyzed with this model range in size from less than million to more than billion.

The system rapidly tailors a model to simulate each engineering and construction project. Each model is then used to foresee future cost and schedule impacts of project changes, and most important, test ways to avoid the impacts.

We developed a project model based on our prior modeling work with Fluor, and built a system around it, with defined practices to rapidly and automatically tailor the model to a specific project. We set up an interface to allow dozens of trained company users to test proactively project-wide impacts of proposed design or scope changes.

We conducted worldwide training of executives and managers and analysts, ensuring the focus was on foreseeing and mitigating future change impacts. The system was applied to hundreds of Fluor projects.


A cultural change occurred in the company, focused on proactive mitigating efforts that reduce change impacts on the projects.As a result, many disputes were avoided (some had cost tens of millions of dollars), and cost impacts were reduced by proactive actions identified in the analyses, amounting to over $1.3 billion savings to Fluor and their clients.


System Dynamics modelers: Kenneth Cooper and Gregory Lee


Award: In 2009, the Society awarded its System Dynamics Applications Award to Kenneth Cooper and Gregory Lee for their work Managing the Dynamics of Projects and Changes at Fluor. See conference slides and citation.

Article: Managing the Dynamics of Projects and Changes at Fluor


Recent Business cases

General Motors OnStar

Name The General Motors OnStar Project Modelers Vince Barabba, Chet Huber, Fred Cooke, Nick Pudar, Jim Smith, Mark Paich Client General Motors Client Type Corporation The Official Website is the official website in which you can become a member, get...

Pharmaceutical Product Branding Strategies

Pharmaceutical Product Branding Strategies Name Pharmaceutical Product Branding Strategies — Simulating Patient Flow and Portfolio Dynamics Modelers Mark Paich, Corey Peck, Jason Valant Contact Jason Valant or Corey Peck Client Numerous Pharmaceutical Companies Client...

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Process Innovation at Du Pont

Process Innovation at Du Pont

Client Du Pont
Authors/Consultants Repenning NP, Sterman JD

Improvement programs such as Total Quality Management are embraced by many organizations but are often discontinued before full benefits can be reaped. With ever-increasing numbers of new techniques and methods available, as well as consultants ready to facilitate implementation, discovering improvement programs is no longer a problem. Instead successfully implementing these programs has become the biggest challenge. A research program spanning a decade, based on observation of over a dozen cases at company sites, interviews, surveys and literature analysis discovered a paradox in improvement programs. Although many organizations strive to improve performance by working smarter, what happens instead is they elevate ‘work harder’ to their standard operating procedure. They fall into the capability trap: the pressure maintain performance drives them to work harder, which prevents learning about ways to do the work smarter.

A case study at Du Pont shows how process improvement may be implemented successfully.

In 1991, a benchmarking study showed that Du Pont spent more on maintenance than its competitors yet its mechanics worked more overtime and plant uptime was lower. An in-house team developed a system dynamics model of these issues.

Policy analysis with the model showed that, while repairs to breakdowns had to continue, the company simultaneously had to invest additional resources in planned maintenance and training. This would in the short term reduce uptime and increase costs, and only show benefits later. In order to facilitate a learning process for the thousands of people that would be involved in implementing these changes, the team developed an interactive role-playing game called the Manufacturing Game. The game is based on the model and accurately captures time delays, costs and other parameters describing a typical plant. The game is used in multi-day workshops across the company and proved popular.

By the end of 1992, 1200 people had played the game and more than 50 facilitators had been trained. In plants that implemented the program by the end of 1993, the mean time between mechanical failure for pumps rose by an average of 12% each time cumulative operating experience doubled, maintenance costs fell an average of 20%. In 23 comparable plants the learning rate averaged 5% and maintenance costs increased by 7% on average.

Criminal Justice

Client Ministry of Justice, the Netherlands
Authors/Consultants Rouwette EAJA, Vennis JAM, Van Hooff P, Jongebreur W

In 2003, Significant consulting and the Methodology group of Radboud University Nijmegen started a modeling project for the Ministry of Justice in the Netherlands. The aim of the project was is to gain insight into the combined effects of three developments: an increase in the case load, investments in different phases of criminal justice administration and contextual developments such as increased complexity of cases. A group of representatives from the police force, public prosecution, courts and sentence execution, probation services, WODC (Scientific Research and Documentation Center) and different departments of the Ministry of Justice participated in constructing the model from January to August 2004. The project was named Simulatiemodel Strafrechtsketen (simulation model criminal justice chain) or SMS. The final model shows the case and person flow in the Dutch criminal justice system over a period of 14 years on a monthly basis. It contains hundreds of equations and 41 views in Vensim.

In addition to answering the original questions, the model was also used to gain insight into the effects of a proposed law. Under the new law, the public prosecution will settle a proportion of cases which are now the responsibility of courts. Several members of the original modeling team participated in an update of the SMS model which was finalized in March 2006. The modeling effort pointed to larger than expected case loads at several points in criminal justice administration, for which IT systems would need to be adapted. As a result implementation of the law reform was postponed for one year. Results of the SMS project were disseminated beyond the reference group in a number of ways. The Ministry of Justice announced the completion of the modeling effort in its communications on the Safety Plan.

A flight simulator based on the model was used in training of new employees for different departments of the ministry. The process and results of the model were (and are) met with enthusiasm in many organizations, resulting in a number of other group model building projects on topics such as DNA sampling, traffic fines, and impact analyses on new legislation and policies.

Maintenance Improvement at ONEgas

Maintenance Improvement at ONEgas

Client ONEgas
Authors/Consultants Venderbosch T, Rouwette E

ONEgas is a company owned by Shell and NAM and is responsible for gas production in the Netherlands. This modeling effort focused on the maintenance process in ONEgas. The maintenance processes at the ONEgas platforms are supported by the SAP PM module. After the implementation of SAP Blueprint it turned out to be difficult to improve the performance of the maintenance process. Uncovering the structure behind the large amount of data captured in the SAP system was thought to be a necessary to identify improvements.

Four facilitated system dynamics (group model building) sessions were held at ONEgas. The model was constructed over a period of seven months, integrated participants’ opinions with SAP system data.

After testing and validation the model was used to test improvements in maintenance in different scenarios. Recommendations focused on capacity for work preparation and base crew, purchase time for material and effective working hours. Final results were captured in a report, presentation to management and a management flight simulator.

An extensive evaluation shows that participants have increased their insight into the maintenance process and are committed to implementing recommendations. The direct client was highly satisfied with the outcomes of the modeling effort.

In summary, the benefits delivered in this case are more insight into the maintenance process in ONEgas and recommendations on how to improve its performance. Participants in the model building process indicate their confidence in conclusions and willingness to implement recommended changes.

More Information

Venderbosch, T. (2007). Using Group Model Building to optimize the maintenance process in an ERP environment at ONEgas. Unpublished master thesis, Radboud University Nijmegen, Nijmegen.

Sustainable Water Management in Laikipia District (Kenya)

Sustainable Water Management in Laikipia District (Kenya)

Author/Consultant Gallati J

Sustainable management of natural resources is a vital concern in most countries and regions worldwide. In Laikipia District in Kenya, located at the foothill zone of Mt. Kenya, water is required in the upper zone for irrigation agriculture, horticultures and livestock production as well as for urban areas. In the lower zone water is required for wildlife and natural habitats.

In an earlier study (Gallati 2008) a system dynamics model has been developed to better understand possible dynamics in collective irrigation management focusing on the feedbacks between social mechanisms of collective action and the performance of the irrigation practices. In Laikipia, however, it turned out that this model was not applicable due to the fact that large immigration had taken place in the last decades preventing inhabitants from developing close relations of exchange and reciprocity, which had been key preconditions of this model.

A stakeholder workshop in 2009 revealed that the transition towards new water management practices is one of the key concerns in the area.

Based on these insights a system dynamics model has been developed to demonstrate the effect of new water management practices in different zones along the river reflecting the fact of varying rainfall and agricultural options from uphill to downhill zone and down to the plains. In particular the users can experiment with different options such as storage capacity, increase of water use efficiency, use of flood flow, adaptation of agricultural practices, etc.
in order to analyze the effect of these practices on overall production and income. As such it is envisaged to support local participants in adopting a river (basin) perspective. The usefulness of the model is being evaluated in a second stakeholder workshop in 2010. Based on this experience further model development will be evaluated. One option is to further develop the model into a tool for broader use in capacity building and training for sustainable water management in collaboration with local or international institutions.

The project is developed in collaboration with CETRAD (Centre for Training and Integrated Research in Arid and Semi-Arid Areas Development; in Nanyuki, Kenya and is part of a larger research initiative on sustainable natural resources management. It is supported by NCCR North-South in Switzerland, which is funded by the Swiss Development Agency and the Swiss National Fund.

Contact and further information: Justus Gallati, Lucerne University of Applied Sciences

More Information

Gallati J. 2008. Towards an improved understanding of collective irrigation management: a System Dynamics approach. [PhD Dissertation]. Berne Switzerland: University of Berne.