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WHO Utilizes System Dynamics Simulation to Maintain Resolve for Investment in Global Polio Eradication

EXECUTIVE Summary

This System Dynamics model underpinned a 192 country resolution to eradicate polio globally and led the Bill and Melinda Gates Foundation to give Rotary International $100 million to fund the polio eradication effort.

The model made a case for continued funding policy eradication by providing compelling evidence that polio outbreaks will cost more than continued intense vaccination. While the reduction in the incidence of cases was making it look like the cost of immunization was exceeding its benefits, this application of System Dynamics shows that dealing with ongoing, long-term sporadic outbreaks resulting from stopping or slowing down immunization programs is even more costly than dealing with sporadic outbreaks.  The process resulted in a simulation model that estimates the costs of two alternative policies. Option 1 was to continue efforts to eradicate polio and option 2 was to reduce the immunization rate and deal with sporadic outbreaks.

This 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 from eradication to control. Clearly showing the dynamics and giving the wavering commitment a name helped key stakeholders appreciate the options quantitatively and with a much longer time horizon. Since then, efforts have continued to focus on finding the resources needed for complete eradication and on dealing with the other complex challenges that remain. With the support of the simulation model, national and global health leaders and financial supporters re-committed to completing eradication, which led to several hundreds of millions of dollars of resources.

#Polio #WHO #Vaccination #Health

The Problem

Following the successful eradication of smallpox and impressive progress in the elimination of polio in the Americas, in 1988 the World Health Assembly committed to the 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, a 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?

The Solution

The dynamic disease outbreak model represents a more complicated version of the standard SIR model used in a popular System Dynamics textbook (Sterman, 2000). However, in Polio, we must deal with different types of imperfect immunity (i.e., from historic or recent exposure to polioviruses – including the oral poliovirus vaccine and/or vaccination with the inactivated poliovirus vaccine – as well as a latent period and routine or supplemental immunization rates). Modifying and expanding our existing model allowed us to determine that it was not possible to “effectively control” (i.e., achieve low cases) at a low cost. This means that control either implies high costs and low cases, or low costs and high cases, but not low costs and low cases.

Stock an Flow Diagram

However, our most significant insight came from exploring the dynamics of the economic investment in eradication. After watching the GPEI deal with the reintroductions of wild polioviruses in previously polio-free countries between 2004 and 2006, we recognized that reducing vaccination led the stock of susceptible individuals to build up and ultimately to outbreaks after some delay. Responding to the outbreaks requires reinvesting in intensive vaccination, which after some delay contains the outbreak and reduces or eliminates the circulation of the virus. With success comes a perception that the high level of investment compared to the low incidence is no longer justified. If policymakers succumb to the resulting pressure to reduce vaccination spending, this creates a situation in which populations again become vulnerable to new outbreaks.

“If policymakers succumb to the resulting pressure to reduce vaccination spending, this creates a situation in which populations again become vulnerable to new outbreaks.”

To capture this behavior, we constructed the negative feedback loop shown here, which we called  “wavering”. We incorporated this feedback loop into our dynamic disease model and tailored the model to two populous northern Indian states in which wild poliovirus still circulates. We explored two options: (1) vaccinate intensively until eradication;  and (2) vaccinate intensively only if the costs per incident case remain below a certain acceptable level, but reduce the vaccination intensity otherwise  (i.e.,  a “control” option with the possibility of wavering).

Causal Loop Diagram Polio
Simulation Chart Polio

Outcomes

This application of System Dynamics highlights the systemic causes of overruns and emphasizes the importance of understanding the complex physical and social systems within which large projects operate. We, fortunately, saw the wavering commitment loop when no one else seemed to see it, and we went beyond just seeing the loop to build and use a model that provided answers to critical questions at the time the decision makers could use them (and needed them and asked us). In the presentation to the stakeholders, we showed the results to tell the dynamic story in the simplest possible way (i.e., by comparing a firm commitment to a wavering commitment showing the cumulative costs and cases).

We did not focus on explaining the model itself to attempt to walk the decision makers through the equations or diagrams. Instead, we focused on communicating the key insights based on what they already knew (e.g., the 2002–3 reduction in vaccination led to big outbreaks and high costs). However, we anticipated and received (as anticipated) some criticism from economists who did not recognize in the model a traditional health economic analysis, but these were relatively limited.

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Credit Card Giant Halts Market Share Slide With Scenario Planning

The Problem

MasterCard, a major credit card company, once held a dominant market position US Market Share with only one primary competitor, Visa. They woke up one day and realized that, after six years of steady decline in revenues and market share, they had lost their leadership position. The company was stymied. Everyone was blaming each other. The erosion continued despite every effort to turn things around. Conventional wisdom, based on past experience, was not working. It was pretty desperate, nobody knew what to do, and it was feared that they would be out of business in the next five years if they couldn’t turn the “death spiral” around. Despite multiple initiatives to slow or reverse the decline, nothing seemed to work. Our work began with a more limited focus, but soon our objective became finding a solution to reverse the market share decline. It became clear, given the changes in the marketplace, that a new way of understanding the forces at play was necessary.

The Solution

The company called in the System Dynamics group of PA consulting as part of a larger project to change the company’s fortunes. System Dynamics (SD) and PA consulting were chosen because of their ability to look at the problem in a whole new way: people within in the company were thwarting each other’s efforts, and a more holistic approach for the organization was necessary. SD provided the necessary perspective by looking at the system as a whole, without losing crucial details.

Among the early modeling insights, was a simple recognition of the perilous situation in which MasterCard found themselves. The fact was that the company’s clients, credit card issuers, tended to focus all of their marketing efforts on the market leader, while ignoring the runner-up. In this case, when Visa was king, clients only promoted Visa’s product to the detriment of MasterCard. This was a case of a common systems trap called “Success to the Successful” and, if left unchecked, would surely have led to the demise of the less successful entity, MasterCard.

MasterCard also learned that there was a limit on spending due to minimal payments. Customers would not use the cards to the full advantage if they were not being paid off. Counter-intuitively, the best observed solution for this problem was to create a higher minimum payment, to allow for increased monthly spending.

We designed, built and tested a model of the US credit card industry and market–the cardholders, the choices they made using different cards, the businesses that accepted the cards. The model simulated MasterCard performance and that of each of the main competitors-Visa, Amex, Discover.

We tested ideas that came from all parts of the organization, but none had a significant and lasting impact. At one point they told us, “Just go experiment and let us know what you can find that WILL make a difference.” After several analyses, we identified four factors that when COMBINED would have that significant, lasting market share benefit being sought. A few discussions quickly identified “co-branding” (though we didn’t know what to call it then) as a strategy that would “pull all 4 levers”. We just described it as partnering with consumer-facing companies.

The outcome

PA consulting worked to collect real world experience, expert interviews, quantitative and qualitative data, and cultural factors. The information was cross checked, and at first, client focused. But a unique benefit of SD is its ability to scale upwards to encompass the entire market and capture the inherent complexities within the system. This model was scalable and could be moved from department to department to test a wide variety of initiatives. Hundreds of factors could be tested within the model’s nonlinear and time delayed system, to find the true leverage points which would allow MasterCard to regain and improve their market share. Consequently it was discovered that efforts such as increased value added assurance and increased issuer preference held the most value while conventional solutions like increased advertising held little leverage. This led MasterCard to be the first to market with the technique called co-branding, a partnership between a credit card brand and company, which allowed them to regain 6 points of market share.

Co-branding revolutionized the industry. Today, if you have a credit card, it is likely co-branded–the GM MasterCard, the Target MasterCard… Co-branding had the advantage of being a win-win-win… a win for the consumers, as the “points” gained by consumers provided noticeable discounts on their purchases…a win for the co-branders, who achieved higher customer loyalty as a result…and a big win for MasterCard, gaining almost exactly the market share and the timing predicted by the modeling. They hit the ground running and it was years before Visa and others could mount a similar set of alliances.To the MasterCard member banks the increase was worth billions in relative market share. Since then, co-branded cards have taken off in the United States and have redefined profits within the industry.

For MasterCard, System Dynamics changed not only the way the company saw itself, its competitors and its products, but it also helped drive the creation of an entirely new product line. MasterCard was able to efficiently understand their strategic resources and move on to a new field of growth by recognizing the key leverage points within their own system, identified thanks to the work done by PA consulting.

“In the end, everyone took credit for the work.”―Sharon Els

Do you want to know more?

Modelers Kenneth Cooper, Sharon ElsJim Lyneis, David Starr
The Official Website For added information, or with any questions, see CooperSDNetwork.com, or contact Ken via email: Ken.Cooper@CooperSD.com

MasterCard Case: Part 1

(Narrated by Ken Cooper) 9:47 minutes

MasterCard Case Part 2 – The Back Story

(Narrated by Ken Cooper) 3:07 minutes

System Dynamics XMILE Webinars

On November 19, 2013 David Starr from Cooper Human Systems, and Sharon Els from PA Consulting Group presented their work in a webinar hosted by Steve Adler from IBM, and Karim Chichakly from isee systems.

This webinar was the third in the Big Data, System Dynamics, and XMILE webinar series jointly sponsored by IBM, isee systems and the OASIS XMILE Technical Committee. The series was showcase exemplar applications of System Dynamics in the areas of environment, business, health care, and public policy. (Nov 2013)

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System Dynamics Helps Reduce Waiting Lines for NHS Patients

System Dynamics Helps Reduce Waiting Lines for NHS Patients

EXECUTIVE Summary

The impact of COVID-19 on the National Health Service (NHS) in England, led to growing record waiting lists, deterioration of patients and recovery of elective care became the number one priority.

Creative solutions were needed to tackle the elective recovery backlog, however, the health service in Norfolk and Waveney in the East of England were constrained by limited resources. System Dynamics allowed operational and clinical staff to test the impact of major interventions, aiding decisions for leaders in the system about where best to allocate resources and transform services to benefit patients and reduce waiting times.

The modelling exercise explored ways to address backlogs in the Musculoskeletal pathway with the aim of returning them to sustainable levels while providing good outcomes for patients. The insights provided by the model considered not only capacity restrictions but also the final outcomes for patients.

The model starts from the population and incidence of activity, and through the community (including GP referrals, diagnostics, community physiotherapy), before then moving through into secondary care. Moving into secondary care it covers first appointment, follow up, waiting lists, beds, and incorporates how trauma impacts on elective waiting lists while considering constraints in theatres capacity and community services.

#England #NHS #Health #Waiting lists #Musculoskeletal

The Problem

Integrated care systems (ICSs) are partnerships of organisations that come together to plan and deliver joined up health and care services, and to improve the lives of people who live and work in their area. Norfolk and Waveney Integrated Care System (ICS) in England had a number of challenges across the health system which comprises three acute hospitals, two community providers, and a mental health trust. Prior to COVID-19, elective care across Norfolk and Waveney ICS was already seeing increases in waiting lists and it was not meeting national standards with some patients waiting over 52 weeks for planned care. COVID-19 then saw the postponement of the elective care which further impacted the situation negatively, leading to one of the biggest cohorts of 104 week waits in England. 

To tackle the challenges across the whole system, clinical and non-clinical colleagues came together with the support of NHS England and Whole Systems Partnership, to develop three System Dynamics models in Stella Architect starting with the Trauma and Orthopaedics speciality within the Musculoskeletal (MSK) pathway and then also models for the Dermatology and Eye Care pathways. The models were used to test different scenarios and identify how healthcare operational interventions designed to reduce the elective care backlog impact patients across the Norfolk & Waveney ICS system.

The Solution

For the MSK pathway model, a group of analysts across the system first engaged with clinical and operational colleagues in stakeholder workshops identifying the key issue, exploring what-if questions, and developing a high-level conceptualisation of the model.

The model was developed using a modular approach by building separate parts of it first and then bringing the various components together. A user interface was created to support operational colleagues to understand the outputs of the model and enable decision-making. Operational colleagues used the interface to interactively adjust variables such as expected demand and see impact projections on capacity and waiting lists.

The model was able to test the following ‘what if’ scenarios:

What if…

 …we provided open access to early self-referral and self-management including advice on holistic/lifestyle choices to improve ultimate outcomes and minimize the impact of delays caused directly or indirectly by the COVID epidemic?

…we deployed a multi-disciplinary team Advanced Clinical Practitioners (ACP) to triage long-wait patients and fast track those in greatest need, possibly to other providers, facilitating this with integrated IT systems?

…we eliminated interruptions to the capacity and therefore flow along the elective pathway from trauma or unscheduled care requirements, i.e. protected capacity?

MSK diagram 1

Once the high level model conceptualisation was agreed upon, a prototype model was developed by analysts across the system, who had begun to receive technical training in using the software. Analysts from across the system each took a different part of the system to model and once these were completed, the different parts were brought together to create the complete system model. The stock and flow diagram below shows main components in the model. Please note that the actual model is more complex than the one illustrated in the diagram.

SFD MSK

The user interface was accessible through a link on the internet, which allowed password holders to use and run the model.

Outcomes

There were many significant benefits and impact for doing this work including enabling high quality operational decision making around the elective recovery. This work helped to develop cohesion within the ICS, improving working relationships between analysts, clinicians, and operational staff across all of the stakeholders in the region.

 Here are some highlighted benefits and impact of creating a system model in Norfolk and Waveney:

> Different impacts of scenarios could be understood and visualised through the model

> End users are now testing their own what-if questions, and understanding how changes to parts of the pathway will impact other parts of the system

> An iterative process captured a multiplicity of voices with real life experiences of the service

> This project complemented existing quality improvement and elective recovery work happening in Norfolk and Waveney ICS

> Staff shifted from silo working to genuine system working

> More conversations are now happening about the harmonisations or pathways, the locations of diagnostic assessment centres and community hubs.

> This approach is empowering discussions with Integrated Care Boards at a system level

> Analysts are now equipped with technical skills in order to conduct system modelling

> Analysts have developed much closer relationships with clinicians.

Do you want to know more?

Model
Please note that this is an example model, it is not validated and it does not contain genuine data – it should be used to understand what the tool looks like and should not be used operationally.

Connect with Vanessa Perez Perez

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General Motors OnStar

General Motors OnStar

The Official Website

onstar.com 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.

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

Do you want to know more?

Publications

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)

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Pharmaceutical Product Branding Strategies

Pharmaceutical Product Branding Strategies

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.

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

Do you want to know more?

Publications

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)

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Diabetes

Diabetes

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.

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

Do you want to know more?

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Project Management at Fluor

Project Management at Fluor

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

#United States #Engineering #Fluor #Corporation

CLIENT: FLUOR

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.

CHALLENGE: CHANGES DON’T IMPROVE PROJECTS, QUITE THE OPPOSITE

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.

ACTION: FLEXIBLE SYSTEM DYNAMICS MODEL TO PROJECT COST OF CHANGES

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.

RESULTS: COST REDUCTION OF $1.3 BILLION

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.

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System Dynamics modelers: Kenneth Cooper and Gregory Lee

AWARDS & PUBLICATIONS

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

 

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Process Innovation at Du Pont

Process Innovation at Du Pont

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.

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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.

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Authors/Consultants Repenning NP, Sterman JD

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Criminal Justice

Criminal Justice

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.

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Authors/Consultants Rouwette EAJA, Vennis JAM, Van Hooff P, Jongebreur W

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Maintenance Improvement at ONEgas

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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.

Client ONEgas
Authors/Consultants Venderbosch T, Rouwette E

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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.

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