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Systems Innovation Practice

Delivering Big Insights with Small Models

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In the 1950s Jay Forrester wanted to take the feedback principles central to his work in servomechanism and computer design and apply them to business. His basic tenet was that the feedback structure of social systems would determine how they performed. Most social systems are quite complex and defy intuitive understanding.

Jay’s solution, and one we still use today, was to build up computer models that capture both how material moves around and the way that information is used in making decisions. These models can then be simulated and the behavior they generate compared, both qualitatively and quantitatively, to what is observed. The results are both surprising (sensible decisions coming together to create disasters) and expected (persistent problems that just won’t go away).

While our tools and the range of applications have grown over the years, the fundamental message is still the same: Structure causes behavior.

Our goal is to help you uncover that structure, and explore with you ways to change it so that you get better results.

Overview

Facilitating strategic insights

Systems Innovation Practice is a small group of professionals and academics who have extensive experience and training in system dynamics. We’ve teamed up with isee systems to offer clients an alternative to traditional consulting services.

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We strive to achieve big insights using small models and clear explanations. These insights are the building blocks for solutions to important problems in business and public policy.

Applying system dynamics

We work closely with our clients to generate insights in a collaborative environment while utilizing the rigorous scientific method of system dynamics. Our work involves constructing models of how materials flow and people make decisions.

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Using computer simulations and systems thinking analysis creates deep understanding of the problem at hand — an essential requirement for designing interventions that avoid unwanted future outcomes.

Understanding complexity

Tomorrow is not simply today plus one. Our models use the rich structure of interaction that defines our complex world to offer strategic insights into why things are happening, and what might happen.

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We keep our clients involved in model conceptualization and development which are important parts of understanding complex problems. We strive for compact, easily explainable models. Clarity of models leads to clarity of understanding!

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Services

Whether you are stuck on a formulation or need better solutions to difficult and persistent business problems we have the knowledge, experience and aptitude to help. We are flexible in the way we work with clients and can provide of variety of services, including:

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  • One-on-one coaching and modeling support to help you develop your own models and insights.
  • Customized training and workshops so your team can build its modeling skills or internalize the insights that come from existing models.
  • Group modeling projects to collaboratively develop insights and understanding together.
  • Policy analysis and decision support with deep expertise across a range of industries and functional areas we can also work on the issues you identify, and bring you the critical insights needed to develop solutions and seize opportunities

   Group Model Building

Group model building is an intense set of sessions that combines problem articulation, the development of hypotheses about sources of the problem and potentially experimentation with a model or models that embody those hypotheses. The purpose of group model building is to use the wisdom of participants to bring together a shared understanding of why things are happening. We lead the group in articulating the problem, identifying key measures, sketching out observed and potential behavior of those measures, and in identifying feedback relationships that could generate the behavior. The process relies on the knowledge of the group so that the group owns the model or models developed. Bringing a model to the point where it can be simulated is typically handled offline with the group reconvening one or more days later to conduct experiments and explore solutions. The resulting models may be sufficient in themselves, or may warrant further development based on discussion with other people and additional data gathering.

   Breakthrough Sessions

Less formal than a group model building session, we can help brainstorm issues using the tools of systems thinking. Drawing out specific reference modes showing the behavior of key measures over time can be an effective means of getting agreement on where things stand. Differences in perceptions of behavior are often responsible for discordance in management teams, and can usually be resolved by looking to agreed upon sources of data and facts. Having people articulate their theories about why something is happening can be helpful in identifying differences in mental models that can also lead to conflict. Even though it may not be clear which is the better way to think about an issue, identifying these mental models as the basis for different opinions can help diffuse the emotions around an issue.

   Model Development

The underlying process for model development is fundamentally the same as that of group model building, except the work is done over a longer period of time with small group interactions and other forms of data gathering. The resulting models are tested and many of the apparently conflicting views of the way things work can be resolved as work progresses. The diligent development process leads to models of high quality suited for addressing the problem of concern. The groups involved in the model development discussions get some sense of ownership, though not as great as in the case of group model building. The models created can be used by the client and can also form a basis for issue-focused training, or the development of model based learning environments or process-embedded models.

   Mentoring

Our practitioners all have years of experience in both using models to address important problems and in teaching. We can provide mentoring to individuals or groups to help build their experience level in system dynamics while they are getting work done. Mentoring works best when the client takes ownership of the modeling work used to address the problem. The mentor can critique work that has been done, suggest alternative approaches and formulations, help the modelers get unstuck and point out potential pitfalls in an approach. While mistakes are integral to learning, spending a great deal of time going down paths that are not fruitful can often be avoided without detracting from the lessons learned.

   Model-based Learning Environments

A model allows the consequence of policies and decisions to be studied quickly without impacting the organization. Creating a learning environment, also called a management flight simulator and in essence a computer game, allows people to experiment with running things. Learning environments can be implemented as standalone experience for individuals or teams working together, or as part of a larger group interaction where teams might be competing or might represent different functional areas within an organization. When combined with appropriate curriculum development, learning environments can be highly effective at providing training in compressed time frames.

   Issue-focused Training

It takes years to master the skills necessary to build system dynamics models. In many cases, however, a subset of those skills is what is needed to develop and refine models in a specific application area. We can develop training that is specific to such an application area, or even to a family of models, that will allow people to work productively with the models.

   Process-embedded Models

System dynamics models provide important insights into the operations of a system, but they can also be used to show the logical consequence in the future of actions being taken today. By embedding a model in a business process, it is possible to understand better where things currently stand and what can be expected in the future. The embedding can be human, in which someone adjusts inputs and reviews results, or automated, in which data are fed electronically to a model which is run with results passed to the next stage. That next stage might be a report, or the model might pass results electronically to another computational stage.

   External Review

By bringing in a fresh eye it is possible to both increase the quality of models by identifying and correcting model faults and to increase the acceptance of models by building confidence in their quality. Our team will work collaboratively with yours to go through the model and highlight areas that exceptionally well done or potentially problematic. Drawing on decades of combined experience we have seen a huge variety of formulations and can point out cases where an alternative structure or equation might better serve your needs. Though review is, by its nature, judgmental, we normally approach it as a constructive collaborative activity.

   Specialized Model Component Development

A system dynamics model typically has quite a number of working parts, and some of these may require special care in development. For example, you may want to perform computations that are outside the scope of regular system dynamics software. We can provide tools for alternative computational techniques that work with, but outside, the software in this case. We may also be able put together formulations that meet the requirements using more traditional stock and flow formulations. In every case we aspire to transparency and develop solutions that can be used by others working with the model.

   Analysis and Reporting

In a standard consulting engagement there is a period of discussion with a client, followed by analysis work done by the consultant with a report written and presented to the client. While we feel that system dynamics is most effective when the problem owner is engaged in the model development and analysis process, a high level of engagement throughout the project is not always practical. Our practitioners are all capable of operating independently and creating reports that meet client needs. Such reports can be provided in addition to learning environments or other interactive tools that embody key project lessons. Ultimately we want to make sure our clients get the most out of the insights generated

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Projects

Systems Innovation Practice’s experience in facilitating strategic insights covers a broad range of fields and applications. A sample of dynamic modeling projects we have completed in Health Care, Business Strategy, and Energy, along with the key insights that were discovered along the way, appears below.

Healthcare

Healthcare

Pandemic patient management

The chief medical officer of a managed care organization was concerned about the potential impact on covered costs from a pandemic. The actuaries calculated a number of plan members who would become infected and the cost of treating them, which they added to other covered costs to arrive at a startling new estimate for the year. In our group, we noted that overall capacity was tightly constrained and most pandemic patients would either displace less severe cases or be untreated. Over the longer term, costs likely would decline.

Managing employee health care coverage

A pharmacy store-based nurse practitioner initiative sought to be added to health plan coverage, which would create a new provider group and headaches for claims processing systems. Marketing dearly wanted to please its clients with coverage. The question was how the initiative would affect plan costs. We built a system dynamics model to simulate its effect, which indicated that most visits would be for non-covered conditions and volume at each site would be too low to sustain the initiative. Coverage was granted but the initiative fizzled.

Management of patients with chronic disease

An ounce of prevention is worth a pound of cure. That adage has underlain many public health initiatives, and definitely applies to communicable diseases. As attention focuses on chronic conditions, however, the relationship between treatment, health and cost becomes more subtle. A small model demonstrated that disease related mortality can apparently rise with better treatment. The seeming contradiction is easily explained as a result of timing (death can be deferred, but rarely avoided). That insight is helpful in choosing metrics for program performance.

Organ transplant system in the US

A system dynamics model of the patient and service chains was created to experiment with various policies and practices in order to understand their impact on waiting times and long term impact on services, developed as a part of a larger study on transplant organ potential. An important insight of the study was the recognition of policy resilience of the system. It furthermore helped to identify effective measures that were initially under the radar screen.

Model-based Learning Environments

A model allows the consequence of policies and decisions to be studied quickly without impacting the organization. Creating a learning environment, also called a management flight simulator and in essence a computer game, allows people to experiment with running things. Learning environments can be implemented as standalone experience for individuals or teams working together, or as part of a larger group interaction where teams might be competing or might represent different functional areas within an organization. When combined with appropriate curriculum development, learning environments can be highly effective at providing training in compressed time frames.

Managing stroke patient chains

A simple model of the patient chain for understanding the impact of various service improvement initiatives, developed for training purposes. One advantage of having a simple model was that people could relate to it while it also lent itself easily to experimentation. Experimentation with the model showed that local process improvement efforts may create adverse affects for the whole chain.

Managing medical referrals

A specialty medical practice was having difficulty remaining profitable with referrals to the practice fluctuating over time. A system dynamics model was developed that captured the referral cycle and showed the managing partner his referral base was too large and that too many referrals created a backlog of patients that increased waiting times to the point where patients and referring doctors went elsewhere. The managing partner was able to use the model to convince the other partners to implement a strategy of controlling referral sources. Since the policy was implemented the practice has tripled in size and remained profitable.

Business

Business

Supply chain management without demand forecasting

Accurate demand forecasting is an essential part of most supply chain management algorithms. Yet, supply even in the most diligently managed chains can deviate widely from demand. Additionally, any disturbance in demand will often be amplified in a supply chain creating what is known as bullwhip effect. This simulation attempts to demonstrate using three parsimonious models that supply can be regulated reliably without forecasting demand. The inspiration for these models comes from PID (Proportional, Integral, Derivative) control in engineering controllers that utilize multiple functions of error (discrepancy between goal and actual condition) to regulate a quantity.

Changing fleet maintenance strategies

A company wanting to improve the reliability of its fleet and decrease labor costs associated diagnosis and repair of failed systems was moving to a new more proactive maintenance practice. They were concerned about the short and long term implications of this for its workforce. Specifically, they wanted to know how much overtime they would need to handle the transition and how much smaller the number of workers could be afterward. The model developed demonstrated that the real transition constraint was parts usage, as this would spike when older parts were culled.

Air traffic safety improvement strategies

Air transportation safety is both very high and a matter of great concern. The agencies charged with improving this are also continually struggling with funding. One choice they must make is how much time to devote to investigating accidents and incidents versus prospectively addressing issues. Though it seems desirable to get ahead of the next problem, it is actually much more efficient to investigate things that have already happened. A small model, and even simple logic, easily demonstrates that to serve the future, scarce resources should be devoted to the past.

Product launches

When launching a new product into a crowded but differentiated marketplace companies expect to take over existing market share as well as reach new customers. In a model used to capture the effects of different pricing decisions and marketing activities it was observed that a product launch might in fact increase the sales of competitive products. This result, though surprising, was recognized as a logical consequence of marketing efforts that made people more aware that products were available to serve specific needs. The company was able to use this insight, which was actually realized after launch, to better understand their role in the marketplace.

Effectiveness of Agile software development techniques

The Agile software development process is designed to improve cost and delivery of projects in the face of uncertain customer requirements, but is it effective? A systems dynamics model was developed comparing Agile methods to the traditional waterfall model and Agile was shown to perform better when customer requirements are unknown, but not consistently otherwise. In particular, the practices of nightly builds, frequent releases, and avoiding complicated designs until proven needed showed the greatest impact in reducing the schedule and cost.

Does a subscription model lead to more stable revenues?

A small business was evaluating whether to switch from charging for new product updates and features to charging an annual subscription fee that includes all new updates and features, with the goal of stabilizing the revenue stream. The system dynamics model showed that under very specific circumstances, an annual subscription stabilized revenues. However, if the conditions for these circumstances were not met, the subscription model could further destabilize, and most likely decrease, the revenue stream. Detailed analysis indicated a number of places to improve data collection and reporting, and also highlighted the importance of the subscription price relative to the cost of renewing a lapsed subscription.

Energy

Energy

Utility conservation programs in the Pacific Northwest

The nation’s electric utilities were facing serious problems with planning and financing in the 1970s and early 1980s. A surprising remedy for many of the problems was to purposely slow the growth in electricity demand through utility conservation programs. Models were developed for the Bonneville Power Administration (BPA) during the 1980s-90s that helped BPA lead the way in the development of major conservation programs. System dynamics delivered valuable insights over the course of many years.

Boom and bust in construction and the California electricity crisis

California introduced major changes in electricity markets in the late 1990s with the goal of encouraging a more competitive market for electricity. The new markets opened in 1998, and the state was experiencing sky-rocketing wholesale prices and chronic brownouts/blackouts within just two years. Major distribution companies went bankrupt and many private generating companies went bankrupt thereafter. System dynamics models were developed for the California Energy Commission to demonstrate the underlying reasons for the “Boom & Bust” pattern in construction of energy infrastructure and for the vulnerability to sky-rocketing rates. The modeling results were used in California’s assessment of a new course to follow in the wake of the crisis.

Our Team

Karim Chichakly, Steering Committee Member

Karim Chichakly

Karim has completed modeling projects in business strategy, project management, health care, public policy, and the environment. He also has extensive experience in both teaching and training. Dr. Chichakly is Adjunct Professor of System Dynamics at Worcester Polytechnic Institute and Adjunct Professor of Computer Science at Capitol College. He holds degrees in mathematics, engineering, computer science, and system dynamics. Karim has been the Director of Product Development at isee systems, inc. for over 20 years, where he was the architect and designer of the major redesigns of iThink and STELLA for both version 4.0 authoring and the new version 10.0 STEAM engine. He also developed the draft XMILE standard for interchanging and archiving System Dynamics models.

Robert L. Eberlein, Steering Committee Member

Bob Eberlein

Bob Eberlein has a broad range of experience helping organizations understand and address complex business and social problems. As a founding member of the System Dynamics Society he has been active in the field, and with the Society, since 1984 and now serves as the Society’s Vice President Electronic Presence. He has extensive experience using dynamic models to address problems in project management, economic development, health care, manufacturing, marketing, telecommunications, retail sales, energy, and equipment maintenance. Dr. Eberlein was the primary architect and developer of the Vensim software from its inception through 2010 and has developed other software systems. He has taught many courses and workshops in System Dynamics and currently teaches a graduate course in model analysis techniques at Worcester Polytechnic Institute.

Andrew Ford

Andrew Ford

Andrew Ford is Professor in the School of the Environment at Washington State University and Adjunct Professor of System Dynamics at the Worcester Polytechnic Institute. His classes focus on system dynamics modeling of energy and environmental problems in the western United States. His classes during sabbaticals at the London Business School and the Sloan School of Management at MIT focused on system dynamics applications to business and public policy. Dr. Ford’s modeling applications range widely, from models to support planning in the national parks to models to support policy formulation in the energy industries. His conservation modeling for the Bonneville Power Administration was honored in 1996 with the Jay W. Forrester Award for the outstanding contribution to the field of system dynamics. His current work focuses on the impact of incentives to increase renewable generation and to reduce carbon dioxide emissions. His most recent study makes use of system dynamics to demonstrate the value of large scale electricity storage that would enable rapid growth in solar and wind generation.

Mark Heffernan

Mark Heffernan

Mark is a simulation practitioner based in Australia with Masters Degrees in Engineering and Business. He brings a Systems Engineering discipline to his modelling and spends as much time helping to solve the correct problem as he does solving the problem correctly. He ensures that the models he builds with clients combine the dreams of Strategy with the constraints of Finance while always complying with the laws of Physics. His FleetDoctor simulation for the RAAF F-111 fleet was nominated for a Prime Minister’s Award for Innovation. The model has been converted into a generic fleet board game to help kinaesthetic learners experience the compromises required by operations, maintenance and logistics to reach the organisation’s goals. He formerly ran Evans & Peck’s Decision Modelling Service Line. For several years Mark co-chaired the Business Stream of the ISDC with Jim Thompson and has written several papers on Health Care delivery with Dr Geoff McDonnell.

Rod MacDonald

Rod MacDonald

Rod has 15 years experience developing system dynamics models as decision support tools primarily for decision makers in local and state governments. The problems addressed have included testing policies for HIV/AIDS, Social Security disability determinations, drunk driving and drunk driving recidivism, environmental conservation issues, delivery of mental health services, criminal justice, traffic safety, fleet maintenance, and tobacco use. Rod has also designed and delivered system thinking workshops to over 2,500 state employees in numerous agencies. Rod is the director of the Initiative for System Dynamics in the Public Sector located at Rockefeller College of Public Affairs and Policy at the University at Albany and an Adjunct Professor in Public Administration where he teaches courses in system dynamics modeling and public policy at the graduate and undergraduate levels.

John Morecroft

John Morecroft

John Morecroft is Senior Fellow in Management Science and Operations at London Business School where he teaches system dynamics, problem structuring and strategy in MBA, PhD and Executive Education programmes. He has served as Associate Dean of the School’s Executive MBA and co-designed EMBA-Global, a dual degree programme with New York’s Columbia Business School. He is a leading expert in strategic modelling and system dynamics and has written numerous journal articles. He has co-edited three books and published a system dynamics textbook, Strategic Modelling and Business Dynamics (Wiley 2007). He is a recipient of the Jay Wright Forrester Award of the System Dynamics Society, a Past President of the Society and a Founding Member. His research interests include the dynamics of firm performance and the use of models and simulation for strategy development. He has led applied research projects for international organisations including Royal Dutch/Shell, AT&T, BBC World Service, Cummins Engine Company, Harley-Davidson, Ericsson, McKinsey & Co and Mars. Before joining London Business School he was on the faculty of MIT’s Sloan School of Management where he received his PhD. He also holds degrees from Imperial College, London and from Bristol University.

Oleg Pavlov

Oleg Pavlov

Oleg Pavlov is an Associate Professor of economics and system dynamics and a member of the Healthcare Delivery Institute at Worcester Polytechnic Institute. His expertise is in multi-sector computational analysis that accounts for feedback and resistance to change often exhibited by complex systems. He published on such topics as electronic marketplace, medical education, computational finance, political economy and economic growth. Dr. Pavlov is past president of the Economics Chapter of the System Dynamics Society and a Coleman Foundation Faculty Entrepreneurship Fellow. He holds a B.S. in Physics/Computer Science as well as M.A. and Ph.D. in Economics from University of Southern California and an MBA from Cornell University.

Khalid Saeed, Steering Committee Chair

Khalid Saeed

Khalid Saeed has worked on consulting and training projects with, among other organizations, Veteran’s Administration, Health Resources and Services Administration, United Nations, Asian Development Bank, World Business Council for Sustainable Development, Booz Alan Hamilton, and McKinsey & Company. He is Professor of Economics and System Dynamics at the Worcester Polytechnic Institute, where he also directs the system dynamics program. A recipient of Jay Wright Forrester Award and a dual PhD from MIT, he has served as President of System Dynamics Society and Associate Editor of System Dynamics Review. Dr. Saeed’s recent work includes project on education, healthcare, supply chain management, public policy, energy, and the environment.

James Thompson

James Thompson

Jim’s work nearly always begins with a question: What causes that? Because health care systems are messy, complicated and dynamically complex, answers do not come easily. Dr. Thompson has worked with a wide group of stakeholders to resolve issues in health care systems – new pharmaceuticals introductions, evaluating medical technologies, challenges unique to care for frail elderly, control of infectious diseases, and capacity planning. His favorite consulting methodology is system dynamics. Working within a rigorous conceptual framework, he combines clinical, social, economic, epidemiologic, and demographic perspectives in models that simulate system performance and allow finding novel solutions. Before joining The Practice, Jim was head of the Health Systems Design Laboratory at Duke-NUS Graduate Medical School in Singapore, a strategy consultant, and director of economic research at a US managed care organization. He continues to teach courses in health systems and applied system dynamics at Worcester Polytechnic Institute.

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