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Dynamic Business Models for Dealing with Competition

In the latest webinar of the System Dynamics for Business Innovation special series, Kim Warren delved into Dynamic Business Models for Dealing with Competition. This session, rich in practical advice and strategic insights, is pivotal for professionals seeking to sharpen their competitive edge. Here’s a breakdown of the webinar’s key points.

Key Takeaways from the Webinar:

  1. Understanding Competitive Dynamics: The session focused on the use of dynamic business models to navigate competitive business environments. Warren highlighted the importance of understanding how competition plays out in real-time and the impacts on prices, marketing spending, and profits.
  2. Case Studies and Applications: Warren used two illustrative case studies to demonstrate the practical application of these models. He discussed GSK’s travel vaccines business and its response to a new competitor, and Beyond Meat’s strategy in creating a market for non-meat products. Both cases highlighted the strategic challenges and responses in dynamic competitive environments.
  3. Types of Competition: Warren outlined three standard competition mechanisms, covering most competitive scenarios. These include racing to capture new potential customers, maintaining existing customers while attracting competitors’ customers, and handling situations where customers can switch between suppliers.
  4. Practical Strategy Development: The session also touched upon practical aspects of strategic planning, like leveraging dynamic models for both long-term strategy and short-term operational decision-making.

Invitation for Further Learning

This webinar is a must-watch for those interested in refining their competitive strategies and understanding the application of dynamic business models. Viewing the recording will provide a deeper insight into the discussed concepts.

Furthermore, the series will continue to offer rich content on strategic business areas such as IT systems architecture and environmental impact reporting. These upcoming sessions represent a unique opportunity to gain advanced knowledge and skills in the field.

Join us for the upcoming webinars to deepen your understanding of dynamic business models and their critical role in shaping successful competitive strategies

Certificate

Attendees who participate in at least 6 out of the 9 webinars in the series will be eligible to receive a certificate titled “Introduction to Dynamic Business Models.” This is an opportunity to gain recognition of your learning in this innovative field. For those who cannot attend live sessions, recordings will be available, allowing you to review the material and complete the series at your convenience.

Watch the recording to get your certificate code.

Watch the recording below

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About the Speaker

Kim Warren is an experienced strategy professional, teacher, and publisher of online courses and teaching resources on business modeling – fast becoming a mainstream capability for executives, consultants, and business students. He was awarded the Jay Wright Forrester Award by the International System Dynamics Society in 2005 and was the Society’s President in 2013.

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Escape from Model Land – How Mathematical Models Can Lead Us Astray and What We Can Do About it

In the third episode of MINDS CAST, we had the privilege of engaging with Erica Thompson, the author of “Escape from Model Land” This session delved into the intricacies of using mathematical models in decision-making, highlighting both the potential benefits and the pitfalls of relying heavily on these models. Here, we summarize the key insights from this enlightening discussion, including Thompson’s perspectives on the role of models in understanding complex issues and the importance of critical engagement with these tools.

About the Presenter:

Dr. Erica Thompson, Associate Professor of Modelling for Decision Making at UCL’s Department of Science, Technology, Engineering, and Public Policy, brings a wealth of expertise to the topic. Her research focuses on the application of mathematical and computational models in decision-making, with a particular emphasis on developing techniques for assessing and interpreting models. As a Fellow of the London Mathematical Laboratory and a Visiting Senior Fellow at the LSE Data Science Institute, her work also delves into the philosophical aspects of model outputs and their integration with expert judgment in decision-making processes.

Webinar Overview:

  1. The Essence of Models in Decision-Making: Thompson opened the discussion by exploring the role of mathematical models in aiding our understanding of complex issues like economic regulation, climate change, and pandemics. She emphasized that while models are crucial tools, they do not produce perfect predictions of the future.
  2. Challenges in Mathematical and Social Contexts: The conversation highlighted the mathematical and social challenges in using models for decision-making. Drawing on examples from climate change and COVID-19, Thompson underscored the importance of recognizing the limitations and assumptions underlying these models.
  3. Best Practices for Responsible Modeling: The session also touched upon best practices for responsible modeling, especially in politically charged contexts. Thompson stressed the need for transparency, accountability, and the acknowledgment of value judgments embedded within models.
  4. Navigating Model Land: The concept of ‘Model Land’ – a metaphorical space where models exist in their idealized form – was a key focus. Thompson discussed the risks of becoming overly reliant on models and strategies for effectively navigating out of Model Land while still leveraging the benefits they offer.

Key Points from the Discussion:

  • Model Diversity and Assumption Transparency: Thompson advocated for the diversity of models and perspectives in modeling processes. She emphasized the need for models to make their assumptions and biases explicit, allowing for more critical engagement and evaluation of their outputs.
  • The Role of Experts in Modeling: The discussion also addressed the evolving concept of expertise in modeling. Thompson highlighted the importance of involving a range of experts in the modeling process to ensure diverse perspectives and assumptions are considered.
  • Models as Conviction Narratives: Thompson described models as ‘conviction narratives,’ tools that provide a structured way of thinking about the future. She stressed the importance of understanding models as narratives that require translation and interpretation to be effectively used in decision-making.
  • Future of Modeling: Looking ahead, Thompson envisioned a modeling landscape that is more integrally connected with social and political contexts. She called for models that are transparent about their value judgments and capable of incorporating diverse perspectives to enhance trust and utility in decision-making.

Erica Thompson’s insights in the MINDS CAST episode provide a valuable perspective on the complexities of mathematical modeling. Her emphasis on critical engagement, diversity of perspectives, and the need for transparency in modeling practices offers a roadmap for navigating the challenges and potentials of models in decision-making. As we continue to rely on models to understand and shape our world, Thompson’s call for a more nuanced and responsible approach to modeling is both timely and essential.

Watch the recording below

About the Speaker

Dr. Erica Thompson holds the position of Associate Professor of Modelling for Decision Making at UCL’s Department of Science, Technology, Engineering and Public Policy, where I work on a programme of research funded by a UKRI Future Leaders Fellowship. She is also a Fellow of the London Mathematical Laboratory, where she leads the research programme on Inference from Models, and a Visiting Senior Fellow at the LSE Data Science Institute.

Her research focuses on leveraging mathematical and computational models to guide decision-making in practical contexts. Dr. Thompson has dedicated her efforts to developing mathematical and statistical techniques for assessing and interpreting models. Additionally, she explores essential philosophical inquiries concerning the true significance of model outputs and the integration of models with expert judgement in decision-making processes.

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Dynamic Business Models for Strategic Management Accounting

In the first session of the System Dynamics for Business Innovation special series, led by business strategist and academic Kim Warren, attendees gained valuable insights into the use of dynamic business models in strategic management accounting. This blog provides an overview of the key topics covered in the webinar ti

Key Points from the Webinar:

  1. Role of Strategic Management Accounting (SMA): SMA is essential for helping accountants and leaders in developing and monitoring their business strategies. It requires detailed analysis of business data, forecasting, and preparing forward-looking reports.

  2. Responding to Market Changes: Strategic Management involves reacting to unexpected competitive threats and market shifts. This includes strategies such as acquiring companies or entering new markets.

  3. Use of Financial and Non-Financial Data: Strategic management requires ongoing analysis and interpretation of both financial and non-financial data. This encompasses aspects like capacity factors and their connections within the business.

  4. Tools for SMA: SMA uses a combination of financial and non-financial tools. The Balanced Scorecard, for example, connects financial outcomes with operational activities and customer focus.

  5. Customer Focus in Business Success: Understanding customer needs is important for business success. Tools like the value curve and the business model canvas help align business offerings with customer needs and set apart from competitors.

  6. Budgeting Techniques: The webinar discussed traditional and zero-based budgeting, underlining the need for budgeting approaches that suit current business requirements.

  7. Tools for Predictive Analysis: It’s important to have tools that can forecast business outcomes in various scenarios. These tools should consider factors such as revenue, competitor behavior, and strategic objectives.

  8. Case Study Application: The webinar demonstrated these concepts through a case study of a new table service restaurant, focusing on sales and profit growth projections.

Invitation for Further Learning:

For those interested in deepening their understanding of Strategic Management Accounting, we recommend watching the recording of this webinar. Further, participation in the upcoming sessions of this series will offer expanded knowledge on topics like competitive strategy, IT systems, and the impact of business decisions on society and the environment.

Join us to further explore the field of Strategic Management Accounting and apply these principles to enhance your business strategies.

Certificate

Attendees who participate in at least 6 out of the 9 webinars in the series will be eligible to receive a certificate titled “Introduction to Dynamic Business Models.” This is an opportunity to gain recognition of your learning in this innovative field. For those who cannot attend live sessions, recordings will be available, allowing you to review the material and complete the series at your convenience.

Watch the recording to get your certificate code.

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 Speaker

Kim Warren is an experienced strategy professional, teacher, and publisher of online courses and teaching resources on business modeling – fast becoming a mainstream capability for executives, consultants, and business students. He was awarded the Jay Wright Forrester Award by the International System Dynamics Society in 2005 and was the Society’s President in 2013.

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Recording: Workshop Visualize and Analyze Complex Challenges the Easy Way

Kai Neumann’s recent workshop  provided a practical and in-depth exploration of modeling complex challenges using the iModeler. With over 25 years of experience in systems thinking and modeling, Neumann’s session was a treasure trove of insights and hands-on learning.

The core of Neumann’s workshop was an engaging walkthrough of qualitative modeling using the iModeler software. His approach emphasized the significance of starting with a clear target factor and systematically exploring influences and outcomes. This process highlighted the power of qualitative models in identifying key levers in complex systems.

Participants engaged in an interactive modeling exercise focusing on the creation of a qualitative model. Neumann adeptly guided the exercise, starting from the identification of a primary factor – in this case, the availability of drinking water – and progressively building a complex model. This exercise illuminated the practical aspects of modeling, such as identifying direct influences, categorizing factors, and understanding the interplay between different elements.

A standout feature of the session was Neumann’s demonstration of the “insight matrixes” technique for analyzing qualitative models. This method provides a nuanced understanding of the impacts of various elements within a model over different time frames. This analysis is crucial for pinpointing effective strategies and interventions in complex scenarios.

This workshop offered an invaluable perspective on System Dynamics modeling. By focusing on the practical aspects of creating and analyzing qualitative models, Neumann equipped participants with tools and methodologies essential for tackling complex challenges in various domains. His expertise in the field, coupled with a hands-on approach to teaching, made this workshop a great learning experience for anyone keen on learning System Dynamics.

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About the Presenter

Kai Neumann has over 25 years of experience serving as a full-time systems thinker and modeler, both as a business consultant and scientist. His expertise spans a broad range of topics, including systemic strategy development, process optimization, global energy transition, food security, and transformation. Additionally, he delves into e-mobility, artificial intelligence, welfare economics, and political strategies. His extensive experience and diverse skill set make him a valuable asset in various fields.

 

About Consideo iModeler

Consideo iModeler is a cutting-edge software tool designed to visualize and analyze complex situations, making it easier for users to plan, make decisions, and communicate more effectively. This software stands out for its unique Insight Matrix feature, which provides a revolutionary way to handle complexity. It allows both qualitative and quantitative modeling, including System Dynamics for scenario modeling.

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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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