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Machine Learning and System Dynamics: a Threat or an Opportunity?

The recent webinar presented by Hesham Mahmoud, an experienced professional in the fields of multinational corporations, academia, and the United Nations, focused on the intersection of Machine Learning (AI) and System Dynamics. This post summarizes the webinar’s key points, including the Q&A session, offering insights into how these two methodologies can be effectively integrated.

Presenter Background:

Hesham Mahmoud, currently a Junior Lecturer and Machine Learning Researcher at Radboud University, brings a wealth of experience from his roles in various sectors, including his tenure at the Food and Agriculture Organization of the United Nations. His expertise in Economics and Political Economics, combined with his professional experience, provides a unique perspective on the application of Machine Learning in System Dynamics.

Webinar Summary:

  1. Defining the Relationship between AI, Machine Learning, and System Dynamics: Mahmoud began by delineating the distinctions and connections between AI, Machine Learning, and System Dynamics. He emphasized the potential of Machine Learning to enhance the analytical capabilities in System Dynamics, moving beyond the traditional view of AI as a complex and opaque field.

  2. Addressing Bias and Ensuring Objectivity: The webinar highlighted the challenge of bias in Machine Learning algorithms. Mahmoud discussed the importance of feature selection and the role of human oversight in ensuring that machine-learning models are as objective and unbiased as possible.

  3. Data Challenges in Machine Learning: Mahmoud pointed out that both the scarcity and abundance of data present challenges in Machine Learning. He stressed the importance of careful decision-making in feature selection to avoid introducing biases into the models.

  4. Complementarity of Machine Learning and System Dynamics: The discussion underscored how Machine Learning could uncover patterns in data that might not be immediately apparent in System Dynamics models, while System Dynamics can provide a human-centric approach to guide Machine Learning analyses.

  5. Practical Applications in Healthcare: The webinar touched on the application of these methods in healthcare, demonstrating how combining Machine Learning with System Dynamics could enhance predictive models in medical systems.

Key Points from the Q&A:

  • Machine Learning as a Complement to System Dynamics: Mahmoud clarified that Machine Learning should be seen as a complementary tool to System Dynamics, not as a replacement. He emphasized its utility in providing data-driven insights that can inform and refine System Dynamics models.

  • Bias Mitigation in Combined Approaches: In response to concerns about bias, Mahmoud discussed how integrating Machine Learning with System Dynamics could help mitigate biases from both fields. He suggested that the triangulation of data-driven insights and System Dynamics models could lead to more balanced and objective outcomes.

  • Ethical Considerations in Model Design: Mahmoud acknowledged the importance of ethical considerations in the design and application of Machine Learning models, especially when used in conjunction with System Dynamics. He stressed the need for ethical frameworks to guide decision-making in these integrated approaches.

  • Technical Aspects of Integration: Addressing the technicalities, Mahmoud mentioned the availability of tools and packages in programming languages like R, which facilitate the integration of traditional System Dynamics models with Machine Learning techniques.

This webinar provided a comprehensive overview of how Machine Learning can be integrated with System Dynamics to enhance model accuracy and objectivity. The key takeaway is that these two methodologies, when combined, can offer a more robust approach to understanding and solving complex problems, particularly in fields like healthcare. The Q&A session further reinforced the idea that Machine Learning and System Dynamics are not competing but are complementary tools that, when used together, can lead to more effective and ethical outcomes.

This session is organized and led by MINDS – the student-led System Dynamics Association at the University of Bergen in Norway.

Watch the recording below

About the Speaker

Hesham Mahmoud has working experience in Multinational Corporations, Academia, and the United Nations. Double Master’s degree holder in Economics and Political Economics with ten years of professional experience in the domains of ICT, Business Analysis, Business Development, Partnerships, Project Management, and Marketing. Before becoming a researcher at Radboud University in the Netherlands, Hesham served as Due Diligence Senior Analyst at the Food and Agriculture Organization of the United Nations and currently works as a Junior Lecturer and Machine Learning Researcher at Radboud University.

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Systems Thinking Through Storytelling

 

“”The recent webinar hosted by the System Dynamics Society featured Linda Booth Sweeney and Philip Ramsey, offering an overview of the world of systems thinking and its application in education and storytelling. Linda, an accomplished educator and author, shared her journey in integrating systems thinking into various educational materials and books. Philip Ramsey, an author and senior lecturer at Massey University, New Zealand, discussed his Billy Bonk and Frankel series, aimed at teaching systems thinking to children.

Linda Booth Sweeney: Bridging Systems Thinking and Storytelling

Linda emphasized the importance of early exposure to systems thinking, noting that it can become a habitual way of thinking if introduced at a young age. Her approach involves using simple, yet powerful, questions to encourage children to visualize and discuss the connections in the world around them. She introduced her new book, “Apart Together,” which aims to cultivate systems literacy, a growth mindset, and resilience in children.

Key Takeaways from Linda’s Presentation:

Early Systems Thinking: Linda highlighted the necessity of introducing systems thinking to children, potentially preventing the fragmentation of knowledge as they grow older.

Curious About Connections: She introduced her methodology involving three questions – “What’s happening here?”, “What if this is connected to that?”, and “What caused what to change?” – to help children visualize and think about systems.

Access the free companion guide when you subscribe to the “Curious About Connections” Newsletter here.

Practical Implementation: Linda shared how her methodology and book can be utilized in various everyday scenarios, from family discussions to classroom settings, to instill a sense of curiosity and interconnectedness in children.

Philip Ramsey: Storytelling with Systems Thinking

Philip Ramsey’s part of the webinar focused on his Billy Bonk and Frankel series, republished recently with the help of the System Dynamics Society. These books are designed to teach systems thinking to children around age 10. He emphasized the role of storytelling in making complex concepts accessible and engaging for young minds.

Key Insights from Philip’s Presentation:

Targeted Age Group: Philip’s books are primarily aimed at children around 9-11 years old, making them a suitable tool for educators and parents to introduce systems thinking at a slightly more advanced level.

The Power of Storytelling: He underscored storytelling as an effective method to communicate systems thinking concepts, making them relatable and easier to understand for children.

Series Development: The Billy Bonk and Frankel series were developed to not only entertain but also educate children, demonstrating the practical application of systems thinking in various scenarios.

The webinar with Linda Booth Sweeney and Philip Ramsey was an enlightening exploration of systems thinking through storytelling. Linda’s approach of simple questioning to induce systems thinking in young children and Philip’s use of storytelling to teach more complex systems concepts to older children present a framework for educators and parents. These methods can be instrumental in fostering an early understanding and appreciation of systems thinking, shaping a more aware and connected generation.

Watch the recording below

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Resources

About the Speakers

Linda Booth Sweeney is an educator, writer and strategist for healthy socio-ecological systems change, Dr. Linda Booth Sweeney is internationally recognized for her efforts to make systems thinking actionable by a wide range of audiences. In her Systems Leadership Labs, Linda creates experiences for leaders to think differently, and to experiment with language, visualization tools and knowledge architecture that better mirrors the complexity they are navigating. She is co-author of The Systems Thinking PlaybookThe Climate Change Playbook, and numerous other books and journal articles. Her newly published book, Apart, Togetheris a gentle introduction to systems thinking for children.  Linda holds her doctorate from Harvard’s Graduate School of Education and lives outside of Boston where she enjoys swimming in Walden Pond and amateur farming.  For more information, see:

(systems) www.lindaboothsweeney.net

(children’s) www.lindaboothsweeney.com

(metaverse) https://togglelab.com

How Children’s Stories Help Us See Real-World Patterns (on Medium.com)

PBS Learning Media Systems Literacy Pilot

Philip Ramsey is the author of the Billibonk and Frankl series, jungle-based fables aimed at teaching children systems thinking through fascinating animal adventures. Phil is a Senior lecturer at Massey University in New Zealand, where he teaches Organizational Learning, Leadership, and Teamwork. He also works as a Director of Incite Learning, a consulting company that mainly works with schools, helping school leaders apply Organizational Learning concepts to solve tough problems.

Billibonk and Frankl Series

Billibonk and the Thorn Patch Front Cover
Billibonk and the Bugs
The Floods of Knith Cover: Billibonk and Frankl Story
The Dark Jungle Billibonk Series Front Cover

Webinar Highlights and Recording: Navigating Client Dissatisfaction

Dennis Sherwood, a stalwart in the consulting industry, recently presented a webinar that peeled back the curtain on the not-so-glamorous aspect of consulting—client dissatisfaction. With over 20 years under his belt and roles in prestigious firms like Deloitte and Goldman Sachs, Dennis brought a wealth of experience to the discussion, centered around a challenging government project that spanned a decade.

The Struggle for Educational Reform: Dennis dove into the intricacies of a flawed examination grading system he audited, which affected countless students’ futures. Despite his comprehensive analysis and solution-oriented approach, he encountered stiff resistance from the powers that be, illuminating the daunting task consultants often face when their findings and client expectations don’t align.

Advocacy in the Face of Resistance: His unwavering commitment to rectify the grading system, even after being turned away, highlights a consultant’s duty to pursue the truth and advocate for change. Dennis’s call to action rallied lawyers, educators, and policymakers to join the fight for transparency and fairness in education.

The Takeaway: Dennis Sherwood’s “Navigating Client Dissatisfaction” session is a testament to the perseverance needed in consultancy, especially when advocating for societal change against entrenched systems. For those inspired by his dedication, Dennis remains open to dialogue and can be reached for further insight at dennis@silverbulletmachine.com

Watch the recording below

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Resources

About the Speaker

Dennis Sherwood has championed organizational innovation and creativity for over 20 years through his UK-based consultancy. A fervent advocate of systems thinking, Dennis’s vast experience ranges from being a consulting partner at Deloitte to holding pivotal roles at establishments like Goldman Sachs and SRI Consulting. An acclaimed author, Dennis has authored 15 books on topics spanning innovation, creativity, and systems thinking, with titles such as “Seeing the Forest for the Trees” and “How to Be Creative: A Practical Guide for the Mathematical Sciences.” Join us as Dennis unravels the nuances of client relationships, providing invaluable insights from his distinguished career.

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Fast-Track Cities Uses System Dynamics to Enhance HIV Care

EXECUTIVE Summary

  • Low levels of viral suppression at 69% for people with HIV make it hard to believe the 95% target level will be achieved by 2030 in St. Louis, USA.

  • As a solution, Fast-Track Cities-STL opted to use group model building means to 1) gain understanding of the fragmented service landscape and 2) to empower the community to address causes of health inequality.

  • The causal loop diagram revealed the importance of community-building for care engagement and created the foundation to build capacity by generating interest and commitment among participants. It also shed light on policies’ unintended consequences leading to service fragmentation and the need for rapid start programs to consider holistic social support for sustained care.

#Fast-Track Cities #HIV #Health #USA

The Problem

Levels of viral suppression at 69% for people living with HIV in St. Louis are far below the targeted goal of 95% required to end the HIV Epidemic in the City of St. Louis, U.S., by 2030.

To increase viral suppression, Fast-Track Cities- St. Louis created a subcommittee dedicated to expand access to rapid initiation of antiretroviral (ART) medication (also called Rapid Start) and to utilize a collaborative governance model to engage in consensus-oriented change. This initiative organized a series of group model building sessions, bringing together diverse stakeholders: those living with HIV, patient advocates, healthcare professionals, researchers, and policymakers. The objective of these sessions was threefold:

    1. To identify structural barriers affecting the adoption and accessibility of Rapid Start services in St. Louis.
    2.
To comprehend the varying perceptions and mental models of providers and clients concerning HIV diagnosis and treatment.
    3. To guide a standardized approach for implementing Rapid Start ART across different service providers.

Figure 1 – Behavior-over-time graph displaying the percentage of virally suppressed people receiving ART medication in the St. Louis region according to different outcome perceptions.

The Solution

The landscape of Rapid Start in St. Louis remains intricate. Despite three major healthcare providers rolling out rapid start programs, the data and insights from these initiatives have remained compartmentalized. Recognising this challenge, Fast-Track Cities-STL was compelled by the group model building approach as it offered an opportunity to map both the service provision landscape as well as important factors impacting quality HIV service while building capacity among communities. Fast-Track Cities-STL finds it incredibly important to utilize empowerment approaches to build a more agile, responsive public health HIV response.

Hence, the aim of Fast-Track Cities-STL was to use participatory group model building not only as means to uncover a greater understanding about the system of access to rapid start ART in the St. Louis region, but also to lay the groundwork for a grassroots community led collaborative in HIV care that aims at addressing the root causes of health inequities and outcomes. Fast-track Cities-STL anticipated that an interactive approach like group model building could help generate interest and community commitment in working on the solutions.

The resulting model combined insights from participatory GMB sessions involving those living with HIV in St. Louis, healthcare providers, and advocacy groups. The Causal Loop Diagram (CLD) crafted from this process comprised three reinforcing loops and eight feedback loops. Factors like mistrust, personal agency over health, peer support, and access to pivotal services emerged as the most influential determinants in the model’s behavior.

Figure 2 – The resulting CLD based on the GBM sessions.

Three fundamental feedback loops are highlighted. The “Problem of Delays” is a reinforcing loop where waiting for healthcare coverage causes individuals to lose their sense of control over their health. This diminished sense of control subsequently leads to decreased engagement within care spaces. Essentially, the longer the wait, the less control individuals feel, leading to even lesser engagement in care, creating a reinforcing cycle.

In the “Mistrust” balancing loop, experiences of racism and interpersonal stigma within the care system amplify mistrust. This heightened mistrust deters individuals from engaging with care, particularly in “non-peer-led groups.” As a result, they face a reduced risk of encountering cultural incompetency from providers. This loop underscores a critical insight: individuals who face racism and interpersonal stigma in care settings are more likely to disengage due to these negative experiences, balancing out their exposure to potential further harm.

The “Operation in Silos” balancing loop highlights how navigating care within bureaucratic silos makes individuals feel dehumanized, akin to “feeling like cattle.” This sentiment fosters greater mistrust, leading to decreased engagement in care. The more compartmentalized the care, the more individuals feel like mere numbers, leading to a balancing effect where they trust and engage less with the system.

Outcomes

The tangible outcome of the project was the CLD that participants developed and validated during the participatory group model building sessions. Key insights from the model revealed the importance of community-building opportunities for engagement in care, especially in the uptake of Rapid Start ART. The model also shed light on how some well-intentioned policies inadvertently led to service fragmentation and undermined the autonomy and peer support vital for those living with HIV. Furthermore, the model highlighted that the factors influencing the initiation of medication were intrinsically linked to the continuity of care. In essence, the model underscored the need for rapid start programs to holistically consider the social scaffolding essential for individuals to initiate and sustain care.

As aimed by the project, the Group Model Building approach also provided means to build capacity among communities disproportionately impacted by HIV and leverage their insights for system change. On the one hand, the insights offered by the model pointed out the importance of building community to generate engagement in care—including the uptake of Rapid Start ART.  On the other hand, the project stimulated interest among participants and recruited them to continue their engagement with the organization; several individuals expressed a commitment to continue working on the project beyond the exercise.

Regrettably, a major setback led to a leadership vacuum, halting the initiative. Nonetheless, a participating organization has taken the baton, advancing the rapid initiation of ART services.

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System Dynamics Unravels ICU Tensions at the Portuguese Oncology Institute

EXECUTIVE Summary

  • The Portuguese Oncology Institute (IPO) faced a critical challenge in its intensive care unit (ICU) where doctors and nurses experienced high turnover due to tensions with surgeons and limited resources, ultimately leading to a high death rate. Surgeons accused ICU staff of holding patients longer than necessary, leading to a backlog in surgeries. Despite utilizing external ICUs, the issue persisted.

  • Multicriteria and optimization approaches did not provide a solution. Only a System Dynamics approach using a management flight simulator enabled IPO’s leadership to understand the dynamics and discuss the causes and leverage points around the problem.

  • Based on the System Dynamics model, the IPO established 10 new beds in the appropriate care units leading to the resolution of tensions and decreased turnover and death rates in the ICU.

#IPO #PatientCare #Oncology #Health #Portugal

The Problem

Despite the demanding nature of the work in IPO’s intensive care unit (ICU), doctors and nurses were passionate about their roles. However, the turnover rate was high due to strained relationships with their colleagues. Surgeons, who would only schedule delicate surgeries when the ICU had availability, accused the ICU staff of unnecessarily prolonging patient stays and not vacating beds promptly. As a result, the ICU became a bottleneck, leading to a growing waiting list for surgeries. To address the issue, management began utilizing external ICUs, but this did not alleviate the resentment or reduce turnover. Figure 1 displays the growth of the quitting rate of the IPO’s ICU waiting list, which is the rate at which patients leave the waiting list without being admitted to the ICU for surgery. 

Figure 1 – Cumulative distribution of waiting list quitting rate

The ICU faced a high death rate, and care sharply declined when patients left for general wards, as readmission was rare due to bed shortages. To prevent readmission or premature deaths, the ICU kept patients until they were stable for general wards. Surgeons hesitated to operate on fragile cancer patients without available ICU beds. Despite being undersized, the ICU’s high cost per bed (equipment and staff) deterred expansion. IPO’s management hesitated to invest in ICU or intermediate care, as adjacent wards needed more beds for patients awaiting surgery. This compromised the organization’s performance.

The Solution

By employing the System Dynamics approach, stakeholders in the IPO’s ICU, including doctors, nurses, surgeons, and management, were able to gain valuable insights into the intricate interactions and dilemmas that existed. The approach provided a platform to examine the underlying causes of the tensions, identify the systemic drivers contributing to high turnover, and uncover the unintended consequences of certain actions. Figure 2 shows the causal loop diagram (CLD) that was developed along with the medical team and used to discuss the uses of a management flight simulator.

Figure 2 – CLD used for discussion with medical team

Through the use of the management flight simulator, stakeholders were able to witness the unfolding dynamics of the problem. This new understanding enabled IPO’s leadership to make informed decisions and take proactive steps to address the challenges. Figure 3 shows the core components of the System Dynamics model underlying the management flight simulator.

Figure 3 – Simulator core, showing the waiting list at the bottom

Outcomes

The application of the model had a significant impact on IPO’s management. As a result, a new Intensive and Intermediate Care Unit was established, reducing reliance on external ICUs and improving resource management. The ICU’s limited capacity and absence of intermediate care beds had previously led to high death rates and compromised patient care during the transition to general wards. Additionally, the practice of retaining patients in the ICU until they were fit for general wards caused delays in surgeries and increased costs.

With the opening of the new unit, equipped with 6 intensive care beds, 4 intermediate care beds, and 2 isolation rooms, these issues were effectively addressed. IPO’’s dependence on external ICUs was significantly reduced, leading to improved resource allocation within the organization. The expanded capacity and inclusion of intermediate care beds ensured smoother patient transitions and better continuity of care. Timely scheduling of surgeries enhanced efficiency and reduced costs while curbing death rates. The establishment of the Intensive and Intermediate Care Unit demonstrated IPO’s commitment to improving patient outcomes, resulting in a positive impact on both patient care and organizational performance.

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A Digital Twin Business Model in 40 Hours

Though long past the date when I should have ‘retired’, I just can’t resist interesting projects, like the one a new friend brought me a few weeks ago. It concerns a small but fast-growing B2B business, providing a management-system SAAS platform for a certain type of real estate company.

The business model is essentially simple. The company gets revenue from [a] implementation fees as clients come on board and [b] continuing SAAS fees from live clients already using the system. Operating costs are dominated by staff in the various teams – sales and marketing, platform development, client onboarding, and customer support. Then there are the costs of running the SAAS platform and the usual overheads of finance, HR, and premises.

The initial problem? … Clients have questions or problems with the platform, so raise support-request ‘tickets’. A support team works to resolve those tickets. Although they already struggle to cope with the rate these tickets arrive, the business expects to double in size this year, and keeps adding new features to the system! So, you may say – hire more staff! But it takes months to learn about the platform and how to resolve client issues, so new staff contribute little to solving tickets and need to be coached while they are learning by the very people who are already struggling to cope.

Meanwhile, new clients must be ‘on-boarded’ – getting them on the system and learning how to use it. But the onboarding team, too, can’t cope with the rate of new clients and the ever-growing feature-set, so those new clients ‘go live’ with too little understanding of the system, so they raise still more tickets. Even after onboarding, ‘novice’ clients continue to raise 3x the rate of tickets of more mature clients.

The wider system. The business has captured the most valuable potential clients over the last 4 years – those with the largest property portfolios, the greatest need for the platform, the highest revenue potential, but also the greatest demand for platform functionality. So the platform developers are under constant pressure to build and release new features. And that pressure means features are released with too many bugs, which leads to more support tickets.

Those tickets come in 3 types – easy “How do I do this?” questions; complaints about things that don’t work; and more complex implementation challenges. So the support team is split in 3 groups too. The overload for each team means that backlogs of unsolved tickets build up. When those backlogs get unacceptable, they have to run “ticket-bashing” sessions when anyone who can help gives up extra time to clear the backlogs. And those sessions are happening more and more often. This puts pressure on the staff, risking a rise in turnover and further loss of even the limited support capacity that exists.

The onboarding team already struggles with existing features, and all new features come with unknown bugs, in spite of developers’ efforts to find them. So clients get new features OK, but fall over more bugs as a result … so the support team gets more support tickets to handle … but they don’t understand the new features either.

And, because the best clients have already been won, the business has no choice but to go for the mid-and small-scale clients. But those clients are harder to win – they have to be made aware, have the platform’s functionality explained, and have to be helped over the challenges that they fear will arise if they adopt it. And then those smaller clients deliver less value when they are won! And they don’t have great staff resources themselves, so they generate proportionally more support requests. Except that they don’t need the fancy extra functionality demanded by the top-range clients.

What my friend needs. First, he needed to just get a handle on the growing imbalance between the flow of support tickets and the capacity of the service team to deal with those requests. So that’s what the initial model did. It showed just how badly the problem would escalate over coming weeks if nothing changed, and pointed to solutions.

But he wanted more than just a working model of the support team’s workload because he could see that was just a symptom of a more complex set of issues, encompassing the whole business, all the way from initial marketing right through to the product development and staffing that has to happen. And of course, he and his colleagues need to understand not only where business development and performance may be going, but the financial implications as well.

So he needs a complete “digital twin” business model, playing out how everything in the system changes over time – platform IT development; marketing and partner acquisition, promotion and sales to 3 client segments, client on-boarding and support; all that ticket-handling across 3 teams; the staff development for all these activities; and all the financial results. And because things change constantly, typical quarterly results and monthly management reports are no use – he needs a model showing how the business is working week by week.

To understand what is going on, we need history – and because the issues surfacing today were triggered by events way back in the past, we need a model that starts in 2021 and goes out to 2024 and beyond.

What we did. My friend is the perfect client – he really “gets” how a living business model works, how powerful it could be, and how to guide its development. And he is willing to put in the hard yards to chase down the data needed. (At least this business has the data, which is often not true. In those cases, we have to estimate and triangulate between known items to fill in the gaps).

So I actually had the lighter load in all this. I spent some 40 hours building the model with the client’s guidance. He put in twice that effort to get that data and figure out with colleagues what had really been happening.

BUT we did it! – a complete digital-twin model that they can now use to work out priorities and policies to fix the current challenges and get the business in a state where it can grow, sustainably, into the future. And do so without the pressures and crises that currently risk overwhelming it. And all by relentlessly following the 4 steps of the strategy dynamics method you can discover in our online course on how to build dynamic business models:

Dynamic Business Modeling

An online course that guides you in creating a quantified, simulated “digital twin” of your business, highlighting inter-dependencies and feedback points.

Society members get a 20% discount

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Webinar Highlights & Recording: Digital Twin Business Models for Strategy and Operational Management

We had the opportunity to delve into the world of business model simulations with industry experts Kim Warren, founder of Strategy Dynamics, and Martin Lycko, Customer Success Manager at Silico, this webinar provided invaluable insights into strategic planning and operational management using System Dynamics.

The concept of a “Digital Twin” is a dynamic virtual model of a business or part of a business. The transformative potential of digital twins lies in their capacity to closely mirror actual performance and predict the evolution of broader systemic influences. Lycko highlighted Silico’s role in implementing effective digital twin architectures that can significantly improve organizational performance.

The participants were walked through examples where System Dynamics and digital twins had been leveraged to optimize business performance and drive competitive advantage. These examples underscored the expansive scope and adaptability of these business tools, validating their efficacy and viability.

In a rapidly evolving business landscape, the utilization of System Dynamics and Digital Twins presents a compelling approach to strategy and operational management. This enlightening webinar underscored the importance of these tools in enabling organizations to better understand their systems, optimize their performance, and drive competitive advantage. As we look to the future, it’s clear that these innovative models will play an increasingly vital role in business planning and strategy.

Related Courses

Dynamic Business Modeling

An online course that guides you in creating a quantified, simulated “digital twin” of your business, highlighting inter-dependencies and feedback points.

Society members get a 20% discount

Read More

Strategy Dynamics for Leaders

An online course that will teach you how to build strategic plans with a digital twin of your business or challenge. Based on rock-solid strategy dynamics frameworks, you will have full confidence that your plans and decisions will achieve your performance targets.

Society members get a 20% discount

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Resources

About the Speakers

Kim Warren is the founder of Strategy Dynamics, helping organizations improve their planning and strategy implementation. Kim spent 15 years in senior strategy roles in business before joining the Strategy faculty at London Business School, where he taught MBA and Executive programs. Realizing limitations with existing strategy methods, he developed powerful strategy dynamics frameworks. Kim is the author of several books, including Competitive Strategy Dynamics and Strategic Management Dynamics, and has collaborated with leading consulting firms to extend the scope of Strategy Dynamics’ impact. His work has also been directly applicable to issues in international development. Kim has an engineering background, an MBA, and a Ph.D. from London Business School. He was awarded the Jay Wright Forrester Award by the International System Dynamics Society in 2005 and was the Society’s President in 2013.

Martin Lycko is a Customer Success Manager at Silico. Silico’s simulation platform empowers organizations to create Digital Twins that span teams and functions. With the ability to connect these Digital Twins to real-time data from across the organization, businesses can simulate the impact of decisions virtually in a Digital Twin of the Organisation.

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Webinar Highlights & Recording: System Dynamics Modeling with R.

System Dynamics is a powerful approach to understanding complex systems in various fields, such as public health, ecology, and economics. Prof. Jim Duggan, an expert in applying System Dynamics and data science methods to public health, recently hosted a webinar on System Dynamics Modeling with R. In this session, Prof. Duggan provided insights into R’s capabilities for data analysis, visualization, and manipulation, as well as building and running Ordinary Differential Equation (ODE) models. Additionally, he discussed using R’s Shiny framework for creating interactive web apps and the prospect of forming a joint digital science tools group for R and Python users.

Exploring R’s Capabilities

Prof. Duggan showcased R’s potential by introducing packages such as ggplot2 and dplyr for data manipulation and visualization, deSolve for building models, and Tidyverse for data analysis. Moreover, he demonstrated creating interactive web apps using the Shiny package.

Visualizing Data with ggplot2 and GGCloud2

Ggplot2, a library from the Tidyverse, enables users to create graphs in R. Prof. Duggan demonstrated creating scatter plots with different visual properties, such as color, using the ggplot function with specific arguments. He also highlighted GGCloud2 as a flexible visualization tool that adds a fourth dimension to a plot using the facetwrap function.

Tidying Data in R

Prof. Duggan emphasized the importance of tidying data for easier processing in R. He showcased the use of pivot_longer for converting wide data into a tidy data format and dplyr as a powerful package for summarizing data. Techniques like grouping, summarizing, and data transformation can be performed using Tidyverse tools.

Building and Running ODE Models

R can be used to build and run ODE models, as Prof. Duggan demonstrated by defining stocks using vectors and generating models automatically with the deSolve package. He also discussed how Markov Chain Monte Carlo (MCMC) can be used to calibrate data using System Dynamics models, helping to find parameters from a dataset.

Interactive Web Apps with Shiny

Shiny, R’s framework for creating interactive web apps, was another key focus of the webinar. Prof. Duggan explained how Shiny’s reactive programming automatically updates outputs based on input changes, requiring minimal HTML, CSS, and JavaScript knowledge. The server function links inputs to outputs and generates new graphs based on input changes.

Conclusion

Prof. Jim Duggan’s webinar on System Dynamics Modeling with R provided valuable insights into the capabilities of R for data analysis, visualization, manipulation, and ODE model building. The session was an excellent resource for those interested in exploring R’s applications in System Dynamics and data science.

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About the Speakers

Prof. Jim Duggan’s research focus is the area of applying System Dynamics and data science methods to public health. This includes using System Dynamics to support pandemic preparedness and response, modeling scenarios of infectious disease outbreaks and applying agile software design methods to develop a safety planning mobile app to address suicidality in young people attending community mental health services in Ireland. Prof. Duggan is a Managing Editor for the System Dynamics Review and a member of the World Health Organisation’s Global Outbreak and Response Network (GOARN).

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System Dynamics Helps Farmers Escape Poverty Trap in Guatemala

EXECUTIVE Summary

Guatemala holds the 4th highest global ranking for chronic malnutrition, and climate change is intensifying the challenges subsistence farmers face in providing food for their families. Utilizing a System Dynamics model facilitated meaningful discussions among various stakeholders and government entities, ultimately leading to the provision of strategic resources, such as livestock and expert guidance, for 152 families. This support enabled farmers to break free from the poverty trap and make valuable investments in their agricultural endeavors.

The System Dynamics model effectively illustrated the interplay between numerous variables, empowering farmers to comprehend and take control of key leverage points essential for their well-being and prosperity.

#Food security #Farmers #Agriculture #Guatemala

The Problem

Guatemala faces a staggering 55% prevalence of chronic malnutrition (a widely recognized indicator of food insecurity) in rural areas and ranks as 4th worst globally. Climate change is amplifying the difficulties subsistence farmers in Guatemala face in providing food for their families. Although climate change affects all food producers in various ways, subsistence farmers are especially vulnerable due to their limited access to irrigation and crop insurance, which could help mitigate the effects of fluctuating weather conditions. Furthermore, because subsistence farmers depend on their own production to feed themselves and their families, low yields and crop loss often result in malnutrition and starvation.

This underscores the need to understand how local farmers can collaborate and work with both local and central governments to enhance their food security.

The Solution

The model was developed using Group Model Building (GMB) and stakeholder engagement in two distinct communities. In each case, we collaborated with farmers and representatives from central and local governments to create causal loop diagrams that illustrated the primary relationships within their respective food systems and the variables influencing food security resilience in the face of climate change. This understanding was then converted into quantitative System Dynamics (SD) models, which facilitated discussions regarding potential policies.

The model comprises five main components:

  1. Maize local market: This component captures the primary dynamics of the local market using a traditional commodity model.

  2. Households: This element represents the dynamics affecting household cash availability and purchasing power, with revenues generated from maize production, livestock, and other activities.

  3. Livestock: This component illustrates the primary dynamics of livestock (specifically poultry) production on local farms.

  4. Soil: This part demonstrates the dynamics of organic nitrogen and organic carbon in the soil.

  5. Water and irrigation: This component captures the infrastructure used for irrigation and exogenous variables like rainfall.

The model features three main feedback loops:

  1. Commercial agriculture (R1): Revenue from maize increases household cash, which enhances their ability to invest in farming (such as seeds, fertilizers, and irrigation systems), resulting in improved soil quality and increased water uptake.

  2. Poverty trap (R2): A portion of maize production is allocated for self-consumption. The higher the proportion dedicated to self-consumption, the less maize is available for the market, which reduces revenue and subsequently investment in farming. Low investment in farming leads to low yields, further decreasing the amount of maize that can be sold.

  3. Reserves driving wellbeing (R3): Higher production rates result in more maize being available for self-consumption, reducing the need to spend money on purchasing food. This increases farmers’ available cash for investing in the next year’s harvest.

Outcomes

The policy recommendations from our study were utilized by local NGOs and government representatives to lobby their representatives in parliament and the Minister of Agriculture (MAGA). As suggested in the report, 152 families were provided with livestock in small quantities and supported by technical experts to develop small production farms.

This demonstrates the compelling nature of using System Dynamics in addressing complex issues, such as food security in the context of climate change. System Dynamics enabled to graphically represented the system through causal loop diagrams, providing a crucial visual aid that facilitated communication with local stakeholders in a way that other modeling approaches, mainly focused on mathematical formulations, could not achieve. Further, the emphasis on stocks in System Dynamics allowed for the effective representation of food stocks and key resources like soil organic matter, while maintaining model simplicity.

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Exploring Careers in System Dynamics

The Exploring Careers in System Dynamics webinar, held on March 29, 2023, was an engaging panel discussion featuring experts Sharon Els, Eduardo Romanenko, and Georg Pallaske, moderated by Shane Carnohan. Attendees had the opportunity to learn about the career journeys of these three System Dynamics experts, as well as their personal experiences and views on the field.

Sharon Els, with over two decades of experience in business modeling and simulation, provided insights into her extensive client work, which includes predicting market changes, optimizing corporate resource allocation, and improving project performance. She has advised corporate executives and government leaders on complex strategy, policy, and program challenges at various organizations. According to Sharon, “You want to simplify as much as you can. You don’t simplify the System Dynamics modeling itself. But you simplify your messages and you focus on what they need to solve and their challenges.”

Eduard Romanenko, a System Dynamics expert with a Ph.D. from the University of Bergen, highlighted the relevance and transparency of System Dynamics as a tool, which he believes will remain important in the future. He has been working as a full-time SD modeler and researcher for the past 3.5 years, providing insights into complex issues related to various projects.

Georg Pallaske, a System Dynamics expert and project manager with a passion for sustainability, shared his experience working closely with clients to develop customized analyses to support government decision-making. Georg is also pursuing a Ph.D. at the University of Bergen, focusing on integrated sustainable development policies. According to Georg, “If we talk about scarce resources, inequality, greenhouse gas emissions & climate change, we cannot address these things in isolation. We need people who are able to use System Dynamics and systems thinking, and to actually support them.”

The panelists discussed the skills and knowledge needed to succeed in the field of System Dynamics, as well as the challenges and opportunities that come with it. Attendees gained valuable insights into addressing complex business challenges and integrating System Dynamics into real-world professions.

Whether you’re a student exploring your career options or a seasoned professional looking to broaden your understanding, the “Exploring Careers in System Dynamics” webinar provided an excellent opportunity to learn from experts in the field.

Watch the recording below

Whoops, this recording is available for members and ticket purchasers only. Please login to verify. If you’re not a member, purchase a membership here. You can also buy a ticket to watch the recording here.

About the Speakers

Sharon Els is an expert in business modeling and simulation with over two decades of experience. Her extensive client work includes predicting market changes, optimizing corporate resource allocation, and improving project performance. She has advised corporate executives and government leaders on complex strategy, policy, and program challenges at various technology, aerospace, finance, gaming, and government organizations. Sharon’s insights and expertise have been instrumental in providing actionable recommendations and solutions to her clients. She currently works at Sage Analysis Group, where she continues to help organizations navigate complex business challenges.

Eduard Romanenko is a System Dynamics expert who holds a Ph.D. in System Dynamics from the University of Bergen (2022) and a European Master’s degree in System Dynamics (2014). He has been working as a full-time SD modeler and researcher for the past 3.5 years. Eduard has provided SD modeling capacity for various projects, including a project on urban stormwater management in Oslo and a 5-country EU-funded project on adolescent overweight and obesity. His contributions have provided insights into complex issues related to these projects.

Georg Pallaske is a System Dynamics expert and project manager with a passion for sustainability. He works at KnowlEdge Srl., where he develops customized analyses to support government decision-making. Georg is also pursuing a PhD at the University of Bergen, focusing on integrated sustainable development policies. As a project manager, he works closely with clients from inception to the presentation of results.

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