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Monday, 19 January 2026

20 Mental Models for effective thinking in- and outside the plastics industry

Hello and welcome to a new blog post. Today we cover mental models and why the are not only important for us in polymer engineering, but also for private and other professional areas of life too.

In 2026, mental models—conceptual, often simplified frameworks for understanding the world—are critical for navigating a landscape defined by rapid AI advancement, geopolitical instability, and extreme information saturation. They serve as "cognitive toolkits" that allow individuals and leaders to parse complex, ambiguous data into actionable decisions.

Mental models are important in 2026 for the following key reasons:

1. Navigating Complexity and AI-Augmented Environments

Example - Cutting Through "Noise": With AI generating massive amounts of data, mental models help filter information and focus on high-impact factors (e.g., using the Pareto 80/20 Rule to identify crucial data points).
Example - Contextualizing AI Outputs: As AI becomes ubiquitous in 2026, human judgment remains essential for interpreting AI insights; mental models provide the necessary framework for this contextualization.
Example - Systems Thinking: Understanding how different components (remote work, global supply chains, AI tools) interact is crucial. Systems thinking helps identify patterns and leverage points for change rather than reacting to symptoms.

2. Professional Adaptability and Decision Speed

Example - Rapid Decision Making: In 2026, business leaders must make decisions 2.5 times faster than competitors. Mental models (like the OODA Loop—Observe, Orient, Decide, Act) enable swift, effective, and reasoned actions under pressure.
Example - Mitigating Cognitive Bias: The fast-paced environment increases the risk of emotional or faulty decision-making. Mental models like Inversion (considering how to avoid failure) or Second-Order Thinking (evaluating long-term consequences) allow for more objective, strategic choices.
Example - Increased "Model Literacy": Success in 2026 requires understanding multiple mental models across disciplines—a "latticework" that allows for more versatile, creative problem-solving rather than relying on a single, outdated framework.

3. Personal Resilience and Growth

Example - Managing Cognitive Load: The sheer volume of information can cause "cognitive fatigue." Mental models help organize information and reduce the mental effort required to make sense of new, complex situations.
Example - Developing Emotional Stamina: As global instability creates stress, mental models assist in building emotional resilience and maintaining a "growth-oriented" perspective, allowing individuals to adapt to change rather than being overwhelmed by it.
Example - Lifelong Learning: In 2026, continuous learning is essential for career longevity. Mental models facilitate the learning of new concepts by connecting them to existing knowledge, accelerating the transition from novice to expert.

4. Improved Collaboration

Example - Shared Mental Models: In hybrid and remote work environments (which are standard by 2026), shared mental models enable teams to align on goals and expectations, leading to more cohesive and efficient collaboration.

My 20 Mental Models for Effective Thinking

Over the past decade of my career in polymer engineering, I have systematically collected and applied a range of mental models to enhance my professional effectiveness. In the accompanying sketchnote (Figure 1), I have outlined the 20 mental models I utilize most frequently.

These models are categorized into four key areas: Core Frameworks, Decision Making and Bias, Change and Adaptation, and System and Interaction.

Figure 1: 20 Mental Models for effective thinking in- and outside plastics industry

In summary, as we move through 2026, mental models are no longer optional, abstract concepts; they are the essential, daily tools for maintaining clarity, speed, and sanity in a rapidly evolving (plastics) world.

Thanks for reading & #findoutaboutplastics

Greetings,

Herwig Juster

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Literature:

[1] https://tetr.com/blog/mental-models-for-business-success-secrets-for-aspiring-founders#:~:text=Mental%20models%20are%20cognitive%20frameworks,identify%20patterns%20others%20might%20miss.

[2] https://taproot.com/mental-models/#:~:text=Simplifying%20Complexity:%20In%20today's%20fast,productive%20discussions%20and%20innovative%20solutions.

[3] https://medium.com/@chamiduweerasinghe/the-future-of-personal-development-trends-to-monitor-in-2026-and-beyond-0537609878f8#:~:text=Companies%20that%20invest%20in%20building,Technology%20as%20the%20Growth%20Engine

[4] https://academic.oup.com/ct/article/35/4/250/8166013#:~:text=These%20models%20map%20onto%20elements,.%2C%202004%2C%202007).

Friday, 9 January 2026

AI & Machine Learning Transforming the Plastics Value Chain

Hello and welcome to a new blog post. AI and machine learning are rapidly transforming every stage of the plastics value chain—from material innovation to recycling and sustainability.

Introduction

Just as elephants in Africa can sense approaching storms and tsunamis before they arrive—prompting smaller animals to follow their lead—the plastics industry is witnessing its own early warning signs. Today, the “big elephants” of the economy—major tech companies—are already on the move, rapidly embracing AI and machine learning to transform every stage of the plastics value chain. If we want to keep pace and avoid being left behind, now is the time to act.

AI & Machine Learning Transforming the Plastics Value Chain

In this sketch note, I explore how AI is accelerating polymer research, revolutionizing part design, optimizing manufacturing, and enabling smarter, more sustainable choices across the industry.

Whether you’re in R&D, engineering, production, or sustainability, discover practical examples and key takeaways on how AI is reshaping the future of plastics.

Don’t wait for the storm to hit—see how your organization can leverage these advancements for a competitive edge!

AI and Machine Learning: Transforming the plastics value chain.

1) AI & Machine Learning Transforming the Plastics Value Chain

Material Development and Polymer Innovation

AI and machine learning (ML) are actively accelerating materials research, enabling predictive modeling of polymer properties, virtual screening of candidates, and synthetic data generation to overcome experimental gaps. This approach dramatically speeds up the discovery of sustainable and high-performance plastics.

In advanced research, machine learning models have been used to design crosslinker strategies that strengthen polymer networks and could be applied to real industrial plastics to reduce waste and extend service life.

Emerging tools are focused on materials that combine sustainability with performance (e.g., biodegradable or recyclable polymers), addressing long-standing limitations in replacing conventional plastics.