Follow Harry Munro on his journey to revolutionise simulation education:
or explore the School of Simulation, a platform dedicated to making simulation skills accessible to professionals across industries.
After an impressive career developing simulations for major projects like the London Underground, Harry Munro CEng MIMechE MSc BEng (Hons.) combined his expertise in mechanical engineering, Python programming, and discrete-event simulation to create the School of Simulation. His platform aims to bridge the gap in simulation education by offering practical, hands-on learning tailored to real-world challenges.
This interview offers an authentic and unfiltered insight into Harry’s journey—from his early career and the inspiration behind founding the School of Simulation to his vision for transforming how professionals learn and apply simulation skills. By preserving his original responses, we provide a genuine perspective on his mission, creative process, and plans for the future of simulation education.
What inspired you to establish the School of Simulation, and what challenges did you encounter while developing this platform?
In 2014, I joined the London Underground and was tasked with developing a probabilistic model of the network to test the capacity and reliability of upcoming upgrade programmes. The goal was to account for variability, such as passenger delays and train failures, in a way that traditional static models couldn’t. At the same time, I was pursuing my MSc in Engineering Management during the evenings and happened to be taking a module on discrete-event simulation – a methodology particularly well-suited to modelling complex systems in heavy industry. Unlike time-stepped simulations that update the entire model at regular intervals (i.e. clock-based), discrete-event simulation focuses only on key events, making it far more computationally efficient and meaning long time periods can be simulated or many simulations can be run to look at probabilistic outcomes (which is called Monte Carlo simulation).
As I explored potential software tools for this task, I found some excellent options, such as SimEvents (a MATLAB Simulink extension) and AnyLogic. However, the licensing costs for these tools made me uneasy – especially given that I was working for the public sector and felt a strong responsibility to deliver value for taxpayers. Around the same time, I’d taken an interest in Python, inspired (as cliché as it sounds) by the film Ex Machina and its focus on artificial intelligence. This led me to discover SimPy, a Python library for discrete-event simulation. It was a perfect fit: SimPy enabled me to build the simulations the London Underground needed, without the financial burden of software licenses.
Over the following years, my team and I supported a range of major capital projects at the London Underground using Python-based simulations. We even developed ways to animate the models to make them more engaging for stakeholders. The result was remarkable: we delivered significant value without incurring any software costs, which was immensely satisfying.
This experience cemented my belief in SimPy as a powerful tool for simulation. However, I realised that many professionals either didn’t know what discrete-event simulation was or weren’t aware that it could be done so effectively – and for free – using Python. As my expertise grew, I began to see how this unique combination of skills; mechanical engineering, statistics, Python, and simulation, could significantly enhance a career. People often don’t like talking about finances, but I think it’s important in the context of making decisions about one’s education and future skillset to aspire to. If it was clearer how earnings correlated with choice of university subjects then I think we would see a lot more people studying STEM for instance. Simulation skills paired with my engineering skills certainly transformed my earnings, enabling me to progress over 12 years from a £23,000 starting salary as a graduate engineer to generating over £200,000 annually as a contractor.
I also noticed a gap: there were so few resources to guide people in developing these skills. Many professionals wanted to learn but didn’t know where to start, or even that it was possible. In 2024, I realised I could address this gap by teaching others the skills that had been so pivotal in my career.
The School of Simulation was born out of this vision. My mission is to bring as many people as possible onto the Python simulation bandwagon, because I genuinely believe that industries can benefit enormously from this approach. By making simulation accessible, affordable, and practical, I aim to empower professionals to unlock new opportunities and solve complex problems, just as I’ve been fortunate enough to do.
There have been plenty of challenges! But I de-risked myself significantly by starting small by building a beginners course in Python first. Since this was my first foray into proper content creation, I needed to figure out how to record lessons and provide content in a way that was useful for students without worrying about the business and marketing aspects. So I built this beginners course last year and launched it on Udemy. At the time of writing nearly 3,000 students have enrolled in that course which has been a fantastic start! So after this I set up my own platform, built the initial content for the School of Simulation and started doing the marketing myself. I officially launched the School of Simulation platform just before Christmas 2024.
How does the approach at the School of Simulation enhance learning outcomes and how does it compare to traditional teaching methods?
The School of Simulation is 100% online. I’ve adopted a structured and engaging approach to online pedagogy that blends traditional teaching methods with practical, hands-on learning. The platform consists of video lessons that are carefully designed to balance theory and application. For theoretical concepts, I use slides to provide clear, concise explanations. When it comes to practical topics, I walk students through coding examples step by step, demonstrating how to apply the concepts in real-world scenarios.
To ensure students can actively engage with the material, all the code shown in the videos is available for download. This allows them to follow along and run the examples locally on their own computers, fostering a deeper understanding through hands-on practice.
To reinforce learning, I’ve included quizzes throughout the course. These quizzes help students solidify their understanding of key concepts before moving on to the next topic.
Additionally, the courses feature assignments designed to encourage students to apply what they’ve learned in a meaningful way. These assignments not only provide valuable practice but also allow students to build a portfolio of work that showcases their new skills – something they can present to potential employers or colleagues.
Feedback from students has been overwhelmingly positive so far. Here’s an example of a recent email I received from somebody that joined the platform (and also read my book):
“I have decided to expand my simulation skills. Previously, I primarily worked with Tecnomatix and AnyLogic, but I’ve always wanted to learn how to create my own models without relying on specialized software. The ability to customize models freely has been a long-standing goal of mine. However, I struggled to find useful information on SimPy or high-quality simulation examples in Python. Thanks to you, I now have a wonderful opportunity to explore simulation in Python more deeply. I also thoroughly enjoyed your book – it’s incredibly insightful and well-prepared.
I’m truly grateful for all the effort and dedication you’ve put into sharing your experience and skills. Thank you so much for making this knowledge accessible!”
Ultimately, the goal is to make simulation both accessible and actionable, empowering students to immediately see the value of what they’re learning. By combining traditional teaching methods with a focus on practical application, the School of Simulation ensures that students not only learn the theory but also gain the confidence to use these skills in their careers.
What technologies and tools are central to your learning framework, and how do they contribute to creating an immersive learning experience?
I host all of my content on Teachable, a leading platform for online courses. Teachable provides an intuitive and user-friendly experience, both for students navigating the course and for me as the course creator. Its features – such as seamless video streaming, downloadable resources, quizzes, and assignments – make it an ideal foundation for delivering high-quality, engaging content. As a leader in the space I take comfort in the reliability it offers.
In terms of the simulation framework itself, the star of the show is Python with the SimPy library. SimPy is a powerful yet accessible tool for discrete-event simulation, and it’s central to the practical side of my courses. By focusing on this open-source library, I ensure that students not only gain practical skills but also avoid the financial barriers associated with proprietary software. I also cover other libraries as simulations in SimPy can be seamlessly integrated with other libraries in the Python ecosystem. For example, if you are familiar with Python libraries, we integrate with Pandas for data management, Seaborn for visualisation and Pygame and Tkinter for animation.
To make the learning experience even more immersive, I provide all the code demonstrated in the videos as downloadable files. Students can run this code locally on their computers, experiment with it, and make modifications to deepen their understanding. This hands-on approach transforms passive learning into active problem-solving, which is critical for mastering simulation.
Another key methodology is the integration of real-world scenarios into the assignments. Students get to work on simulations that mirror challenges they might face in professional settings. This practical focus, coupled with accessible tools, ensures that learners can immediately see the relevance of their skills.
While I don’t require specific hardware beyond a device capable of running Python, the framework is designed to be flexible and accessible. Students can work in the environment that suits them best – whether it’s a Windows PC, a Mac, or a Linux system. For those unable to run Python locally, Google Colab provides a free, cloud-based alternative that requires no installation and works directly in a web browser. This flexibility aligns with my mission to democratise simulation education and make it as widely accessible as possible.
By leveraging Teachable, Python, SimPy, and a project-based approach, I’ve created a learning experience that’s not only immersive but also deeply practical. It empowers students to build confidence and competence in simulation, equipping them with skills they can immediately apply in their careers.
Your platform caters to various industries and disciplines. How do you ensure that the simulations remain relevant and tailored to each sector’s unique requirements?
That’s a great question, especially as my approach is intentionally industry-agnostic. I focus on incorporating as much variety as possible in the case studies featured in my courses. While it’s impossible to cover every potential use case, the methods I teach are highly generalisable. I find that once students explore the diverse examples on the platform, they begin to think about modelling in a way that allows them to adapt the techniques to their own problems and industries.
The examples I include are a mix of scenarios drawn from my professional experience and hypothetical case studies. For the latter, I approach them as if I’ve been consulted on a new challenge in an unfamiliar sector. This means diving into research – reading about the systems and processes I’m modelling, and sometimes reaching out to experts in my network for insights. My goal is to understand and describe the problem in a way that uses accurate industry terminology and reflects real-world complexities.
Ultimately, the key outcome isn’t just building a simulation; it’s teaching students how to frame and model a system effectively to answer specific questions. This skill – knowing how to adapt general techniques to unique scenarios – is what ensures the relevance of the training across industries.
While I don’t formally collaborate with institutions (at the moment), I do draw on a network of subject matter experts when needed, and I’m always researching new industries to expand the breadth of examples. This approach allows me to refine the course material continuously and keep it applicable to a wide range of disciplines.
Using the Teachable platform has been a key decision in ensuring scalability and accessibility. Teachable allows me to host my courses for an unlimited number of students without any technical constraints. But it’s more than just a course delivery platform – I’m building a community within it. Students can comment on lessons, engage in discussions, and ask questions, all in a transparent and collaborative environment. I personally respond to these comments, fostering a sense of connection and support. As the platform grows, I anticipate more organic interaction among students, with experienced learners helping newer ones. This creates a virtuous cycle: as students spend more time in the ecosystem, they not only upskill themselves but also contribute to the learning of others. It’s a self-reinforcing dynamic that helps scale student support effectively.
To address affordability, I’ve implemented several flexible payment options. Students can choose a quarterly payment plan directly with me, or they can take advantage of “buy now, pay later” options offered through Stripe and PayPal. I also offer a 30 day money back guarantee, so if somebody joins the platform and then decides it’s not right for them, they can have their money back. I’ve designed these options to reduce financial barriers while still delivering high value. When you consider the career potential of the skills I teach, the return on investment is incredibly compelling. For example, senior simulation engineers in the US earn between 
270,000 per year, according to Glassdoor, and similar figures apply to UK contractors based on my own experience.
Beyond career progression, there’s also significant value for business owners. For instance, one of my students runs a business using simulation to design logistics warehouses for his clients. By learning SimPy, he’s been able to eliminate costly licence fees for proprietary discrete-event simulation software. Some of these tools cost anywhere from a few thousand pounds to as much as £60,000 per seat per year – figures I confirmed through quotes last year. By adopting Python and SimPy, students can achieve the same results at a fraction of the cost.
Finally, I’ve ensured that the platform is as accessible as possible by using widely available and easy-to-use tools. Students who face technology barriers, such as not being able to run Python locally, can use Google Colab, a free, cloud-based environment that runs Python code directly in a web browser. This ensures that anyone with an internet connection can participate in the course, regardless of their technical setup.
By combining scalability through Teachable, flexible payment options, and pointing people towards tools like Google Colab, I’ve created a platform that is adaptable to different user groups and provides a good-value for money accessible learning experience. And on the subject of accessibility, all of my video lectures come with subtitles for the hard of hearing.
How does the School of Simulation incorporate user feedback into its development process?
User feedback is absolutely fundamental to the success of the School of Simulation. I actively gather input both from current students within the platform and from potential students who are considering joining. This feedback serves two key purposes:
- Improving the current content by fixing bugs or adding related new examples.
- Developing entirely new and innovative content based on people’s specific needs.
For example, I was recently asked whether the platform included a section on cost modelling in simulation – specifically balancing total cost of ownership against system performance for different system configurations. At the time, this wasn’t covered in the course, and I kicked myself slightly because it’s an area I’m very familiar with. However, this request provided a perfect opportunity to create a new case study. I’m now developing a scenario focused on designing a supply chain for a hypothetical client entering a new region. The case study explores how to evaluate different supply chain designs, balancing total cost of ownership with system performance to find the optimal solution. Once completed, it will be available to everyone on the platform.
This iterative approach ensures that the platform evolves in line with students’ needs. By treating feedback as an ongoing conversation, I can make the content more relevant, practical, and impactful for everyone involved. Ultimately, the platform grows and improves not just because of my experience but because of the valuable input from the people who use it.
What are your future goals for the School of Simulation?
My primary goal for the School of Simulation is to continue enriching the content and growing the community. As I add new case studies and lessons, the platform becomes more valuable and attractive to both existing students and new entrants. The lifetime access model plays a key role here – it allows me to keep adding value for current students while making the platform increasingly compelling for future learners. As this process continues, I expect the flywheel effect to accelerate, creating a self-reinforcing cycle of growth and improvement.
At the moment I am selling globally, which is wonderful to be in the middle of. I am experimenting with advertising and I continue to publish regular content on LinkedIn which is growing my audience.
Looking ahead, one exciting possibility I’m exploring is introducing a certification programme. This would involve offering an exam that students could take to earn a formal certification of competence in simulation using Python and SimPy. This could be a valuable credential for professionals looking to advance their careers or demonstrate their expertise. However, implementing such a programme would require careful thought to ensure its integrity – particularly in the age we live in now where AI tools are readily available and make the possibility of cheating an issue. I’d need to look into the effectiveness of proctoring software for online exams. If that’s not possible then I might look into something like in-person exams or even partnering with a university.
As for integrating emerging technologies, I’m keeping an open mind. While I haven’t yet committed to incorporating tools like AI or VR, I see potential in these areas. For instance, AI could be used to provide personalised feedback or assist students in debugging their simulations, while VR might offer a way to visualise and interact with models in an entirely new way. These ideas are on my radar, and I’ll continue to evaluate how they could enhance the learning experience in meaningful and practical ways. I also have the idea of a certificate on my radar, something which could be a valuable credential for professionals looking to advance their careers or demonstrate their expertise. But these are all very much in the “idea” camp at the moment!
Ultimately, my vision is for the School of Simulation to remain at the forefront of simulation education – offering not just the best tools and techniques, but also a vibrant, supportive community where students can grow and succeed.
