De-risking Deep Tech Ventures with Techno-Economic Modeling | The Scenarionist

A Deep Dive with Chris Burk, Techno-economics Specialist

Techno-economic modeling can feel like a complex puzzle, but today we’re breaking it down piece by piece. Together, we’ll explore what these models really are, how to interpret them with confidence, and how to use them to make informed, strategic decisions.

The idea is to allow you to help you generate a smart thoughtful list of questions you can ask—whether you’re preparing a model or evaluating one—to ensure you’re always a step ahead.

To guide us, we are thrilled to introduce Chris Burk, a recognized leader in the field of techno-economics. Chris brings years of experience and practical insights that will help you approach this topic with clarity and method.

If you’re ready to deepen your understanding and sharpen your skills, join us for the full conversation!

Key themes covered:

  • 📈 Why Techno-Economic Modeling Matters for Deep Tech Ventures?

  • 🎯 Addressing Uncertainty: Integrating Technical and Economic Components

  • 🌪️ Prioritizing Key Parameters with Tornado Diagrams

  • ⏳ When and Why Should You Build a Techno-Economic Model?

  • 🚩 5 Red Flags in Evaluating Techno-Economic Models

  • 🏭 Modeling from Pilot to Commercial Scale


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KEY INSIGHTS FROM THE EPISODE

📈 Why Techno-Economic Modeling Matters for Deep Tech Ventures?

Unlike traditional software tech industries, Deep Tech projects are defined by extended timelines to generate revenue, high capital demands, and considerable uncertainty.

Techno-economic modeling plays a crucial role in helping early-stage entrepreneurs and investors navigate these complexities, offering a structured approach to evaluate investments and address the unique challenges of deep tech ventures.

By integrating both technical and economic components and appropriately addressing uncertainty, these models provide a structured framework for informed decision-making.

By breaking broad assumptions into granular components, techno-economic models enable stakeholders—including founders, engineers, investors, and funding agencies—to identify and focus on key parameters influencing a project's economic success.

Moreover, beyond simple cost estimation, these models establish a shared language among diverse stakeholders, facilitating objective communication about expectations, uncertainties, and outcomes.

This alignment is critical, ensuring that technical development proceeds efficiently toward market readiness while maintaining transparency in assumptions, and targeting specific challenges with clarity.

🎯 Addressing Uncertainty: Integrating Technical and Economic Components

It's important to understand that techno-economic analyses and models go beyond traditional cost models that lack the necessary technical depth.

A comprehensive techno-economic model combines both technical parameters—such as reaction rates and yields—with economic considerations. This integration is essential for investors and entrepreneurs to gain actionable insights into a technology's feasibility.

In a bioprocess model, for instance, technical settings like rates and yields are as pivotal as economic metrics like material costs.

This dual focus ensures a more thorough evaluation of the technology, enabling stakeholders to assess both its technical viability and economic potential.

From Single Data Points to Probability Distributions

Presenting results as single data points is a common mistake in techno-economic modeling. Instead, results should be understood as probability distributions to account for the inherent uncertainty in early-stage projects.

While a full Monte Carlo analysis can illustrate this uncertainty, even simpler methods can highlight a range of possible outcomes. Grasping the nature and bounds of this uncertainty allows teams to prioritize efforts on mitigating the most impactful variables.

🌪️ Prioritizing Key Parameters with Tornado Diagrams

A tornado diagram is an effective tool for visualizing how sensitive a model's outcomes are to individual input parameters. By ranking these parameters based on their impact, stakeholders can focus on the most influential variables.

For example:

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