SYSTEMS THINKING IN ENGINEERING: A
SIMPLIFIED GUIDE TO COMPLEX PROBLEM
SOLVING
Taken
UNIVERSITY OF PORTSMOUTH
Jack Davies
Table of Contents
Contents
1. Systems Thinking in Engineering: A Simplified Guide to Complex Problem
Solving (5min) ..................................................................................................................... 2
Key Points ......................................................................................................................... 2
Introduction ....................................................................................................................... 2
Systems Thinking Overview .............................................................................................. 2
What It Is and Why It Matters ......................................................................................... 2
How it Works .................................................................................................................. 2
Decision-Making Worlds ................................................................................................ 3
Data, Information, and AI in Decision-Making ................................................................. 3
Simplified Process for Complex Projects ........................................................................ 3
2.
Comprehensive Report: Systems Thinking in Engineering (20min) ........................ 4
Introduction ....................................................................................................................... 4
Systems Thinking: A Holistic Approach .............................................................................. 4
What It Is and Why It Matters ......................................................................................... 4
How It Works.................................................................................................................. 4
Implementation Criteria .................................................................................................. 4
Guidelines and Resources ............................................................................................. 5
Accountability and Teams .............................................................................................. 5
Consequences of Not Implementing .............................................................................. 5
Reflection and Improvement .......................................................................................... 5
Decision-Making Worlds: Political, Emotional, and Rational .............................................. 5
What They Are and Why They Matter............................................................................. 5
Goals ............................................................................................................................. 5
Details of Each ............................................................................................................... 5
Practical Applications ..................................................................................................... 6
Guidelines and Resources ............................................................................................. 6
Reflection and Conclusion ............................................................................................. 6
Data, Information, and AI in Decision-Making .................................................................... 6
What They Are and Why They Matter............................................................................. 6
AI’s Role and Models ..................................................................................................... 6
What AI Does Well (and Doesn’t) ................................................................................... 6
Evidence-Based Decision-Making .................................................................................. 6
Safe Use ........................................................................................................................ 7
AI and UN SDGs ............................................................................................................ 7
Systems Thinking with AI ............................................................................................... 7
Assignment and Conclusion ........................................................................................... 7
Simplified Process for Complex Projects ........................................................................... 7
UNSORTED ................................................................ Ошибка! Закладка не определена.
Further Improvements as suggested by Grok (Not Started) ............................................... 7
1. Systems Thinking in Engineering: A Simplified Guide to
Complex Problem Solving (5min)
Key Points

Research suggests systems thinking in engineering helps solve complex problems
by viewing them holistically, focusing on interactions and patterns.

It seems likely that tools like the Iceberg Model, V-Diagram, and CATWOE Analysis
are effective, supported by engineering case studies.

The evidence leans toward balancing political, emotional, and rational decisionmaking for better outcomes, though it’s a debated approach.

Data and AI enhance decisions, but their use requires careful management, with
risks like bias and privacy concerns acknowledged.
Introduction
This short guide simplifies systems thinking in engineering, a method to tackle complex
problems by considering the whole system, not just individual parts. It includes decisionmaking frameworks and the role of data and AI, with real-world examples and a process for
managing projects. We’ll explore what systems thinking is, why it’s important, and how to
apply it, ensuring clarity for engineers and learners.
Systems Thinking Overview
What It Is and Why It Matters
Systems thinking views engineering challenges as interconnected systems, focusing on
relationships and patterns. It’s crucial for modern projects with technology, human behaviour,
and stakeholders, helping anticipate long-term effects and build resilient solutions. For
example, the redesign of London’s Heathrow Terminal 5 used systems thinking to integrate
various components, ensuring success Systems Thinking in Heathrow Terminal 5.
How it Works
It uses tools like:

Iceberg Model: Analyzes events (visible issues), patterns (trends), structures
(relationships), and mental models (assumptions).

V-Diagram: Maps the system lifecycle for cross-checking, ensuring design aligns
with outcomes.

CATWOE Analysis: Defines problems by considering customers, actors,
transformation, worldview, ownership, and constraints.
These tools help target root causes, not just symptoms, as seen in the Airbus A380
development, where systems thinking ensured all parts worked together Airbus A380
Systems Integration.
Decision-Making Worlds
What They Are and Why They Matter
Decisions in engineering blend political (power dynamics), emotional (feelings, team
morale), and rational (logic, evidence) perspectives. Balancing these ensures solutions align
with stakeholders and are sustainable. For instance, SpaceX’s Starship development
balanced technical feasibility, investor expectations, and team motivation SpaceX Starship
Decision-Making.
How to Apply Them

Rational: Use data to evaluate options, like choosing materials based on cost and
durability.

Emotional: Build trust through team engagement, fostering creativity.

Political: Align with key stakeholders for buy-in, like securing funding.

Practical steps include stakeholder mapping and self-awareness, reflecting on biases
to improve decisions.
Data, Information, and AI in Decision-Making
What They Are and Why They Matter
Data is raw facts, information is processed data with meaning, and AI learns from data to
act. They enhance decisions, as seen in Tesla’s use of vehicle data for autopilot
improvements Tesla Autopilot AI. However, risks like bias and privacy require management.
How to Use Them Safely

Organizations: Clean data, secure AI, and audit outputs.

Individuals: Choose reputable tools, avoid personal data, and question bias.

AI impacts UN SDGs positively (e.g., healthcare) but poses risks (e.g., job
displacement), detailed in Nature Communications.
Simplified Process for Complex Projects
This 10-step process ensures a holistic approach:
1. Define System Boundary: Set what’s included/excluded.
2. Engage Stakeholders: Map needs and influence.
3. Use Iceberg Model: Analyse events, patterns, structures, mental models.
4. Apply V-Diagram: Map lifecycle and cross-check.
5. Conduct CATWOE: Define problem elements.
6. Balance Decision Worlds: Integrate political, emotional, rational inputs.
7. Leverage Data/AI: Use clean data and verified AI outputs.
8. Explore Solutions: Weigh multiple options and trade-offs.
9. Reflect & Iterate: Review and adapt based on feedback.
10. Measure Progress: Track metrics for effectiveness.
This process, supported by case studies like Heathrow Terminal 5, ensures engineers
manage complexity effectively.
2. Comprehensive Report: Systems Thinking in Engineering
(20min)
Introduction
This report provides a detailed, evidence-based guide to systems thinking in engineering,
integrating decision-making frameworks and data/AI applications. It aims to simplify complex
problem-solving for engineers, supported by credible sources and real-world examples. The
report covers systems thinking principles, decision-making worlds, data and AI roles, and a
practical process for projects, ensuring clarity and applicability.
Systems Thinking: A Holistic Approach
What It Is and Why It Matters
Systems thinking is a method to understand and solve complex engineering problems by
viewing them as interconnected systems, focusing on relationships and patterns. It’s
essential in modern engineering due to increased complexity from technology, big data, and
stakeholder dynamics. Research suggests it helps anticipate long-term effects and build
resilient solutions, as evidenced by the successful redesign of London’s Heathrow Terminal
5, where systems thinking ensured integration of various components Systems Thinking in
Heathrow Terminal 5. This approach is particularly vital for "wicked" problems—complex,
uncertain issues with no clear solution, often involving multiple stakeholders.
How It Works
Systems thinking operates through evidence-based tools:

Iceberg Model: A layered analysis tool, it examines events (visible issues, e.g., a
system crash), patterns (trends, e.g., frequent downtime), structures (relationships,
e.g., maintenance schedules), and mental models (assumptions, e.g., "cheaper parts
save money"). This helps target root causes, as seen in the Airbus A380
development, where understanding system structures prevented integration failures
Airbus A380 Systems Integration.

V-Diagram: A systems engineering tool mapping the lifecycle from requirements to
validation, emphasizing cross-checking to align design with outcomes. For example,
in bridge design, it ensures material choices match safety goals, preventing costly
revisions V-Diagram in Systems Engineering.

CATWOE Analysis: A framework from soft systems methodology, it defines
problems by considering Customers (affected parties), Actors (involved),
Transformation (changes), Worldview (broader implications), Ownership (responsible
parties), and Environmental constraints (external factors). Applied to a factory
upgrade, it ensures worker and regulatory needs are addressed, avoiding blind spots
CATWOE Analysis in Soft Systems.
Implementation Criteria
To apply systems thinking, define the system boundary (e.g., project scope), use visual tools
like the Iceberg Model, engage stakeholders for diverse input, analyse feedback loops (e.g.
how fixes affect performance), and explore multiple solutions, weighing trade-offs like cost
versus durability. This flexibility is crucial, as variables like stakeholder priorities or resource
availability may shift, requiring adjustments.
Guidelines and Resources
Encourage collaboration, focus on long-term outcomes, and use tools regularly. Resources
include training programs Systems Thinking Training, software like Vensim or STELLA for
simulations, and mentorship from experienced engineers. Integrating AI into training, such as
simulation platforms, can accelerate learning by modelling interactions dynamically.
Accountability and Teams
Clear roles are essential: a Systems Analyst leads the process, a Project Manager aligns
with goals, and a Stakeholder Liaison gathers input. Regular check-ins ensure team buy-in,
fostering a holistic view. This structure, supported by case studies like the Toyota Production
System, enhances project outcomes Toyota Production System.
Consequences of Not Implementing
Skipping systems thinking risks inadequate solutions (e.g., patching symptoms), stakeholder
frustration, cost overruns, and delays, as seen in the Horizon postal system failure due to
overlooked interconnections Horizon Postal System Case.
Reflection and Improvement
Review outcomes per phase, seek feedback, document lessons (e.g., early stakeholder
input worked), and refine methods. Measuring progress with metrics like cost, feedback
loops, satisfaction, and issue frequency ensures continuous improvement.
Decision-Making Worlds: Political, Emotional, and Rational
What They Are and Why They Matter
Decision-making in engineering involves three worlds: political (power dynamics, stakeholder
influence), emotional (feelings, team morale), and rational (logic, evidence). Balancing these
ensures solutions are technically sound, socially viable, and organizationally supported.
Research suggests this approach, while debated, enhances outcomes, as seen in the
development of the Boeing 787 Dreamliner, balancing technical innovation, stakeholder
expectations, and team motivation Boeing 787 Development.
Goals
The goal is to understand diverse motivations, align solutions with human and organizational
dynamics, and create adaptive strategies for complexity. This is crucial for projects like urban
infrastructure, where stakeholder buy-in and team morale are as important as technical
feasibility.
Details of Each

Rational: Follows a structured process—define preferences, gather data, evaluate
options, choose logically. Benefits include clarity and accountability, but challenges
include incomplete data and cognitive overload, as evidenced by studies in decision
theory Rational Decision-Making Studies.

Emotional: Driven by preference, focus, and speed, guided by emotional
intelligence. It sparks creativity and builds trust, but risks bias under pressure,
supported by behavioural economics research Emotional Decision-Making.

Political: Pivots on perceived power and issue importance, enabling quick decisions
in charged contexts but unstable due to shifting perceptions, as seen in
organizational behaviour studies Political Decision-Making.
Practical Applications

Stakeholder Mapping: Identify key players, probe motivations, and strategize
alignment, ensuring solutions fit the human landscape.

Self-Awareness: Reflect on biases, seek feedback, and grow emotional intelligence,
enhancing decision quality, as supported by leadership studies Self-Awareness in
Leadership.
Guidelines and Resources
Balance all three, collaborate diversely, adapt to shifts, and document decisions. Resources
include training in emotional intelligence Emotional Intelligence Training, stakeholder
mapping templates, and mentorship from seasoned pros.
Reflection and Conclusion
Review outcomes, document learnings, and foster self-awareness. Integrating these worlds
ensures decisions are well-rounded, enhancing engineering practice.
Data, Information, and AI in Decision-Making
What They Are and Why They Matter
Data is raw facts (e.g., sensor readings), information is processed data with meaning (e.g.,
trends), and AI learns from data to act (e.g., predictions). They enhance decisions, as seen
in Tesla’s use of vehicle data for autopilot, improving safety and efficiency Tesla Autopilot AI.
However, risks like bias and privacy require management, as evidenced by AI ethics
research.
AI’s Role and Models
AI informs (e.g., Amazon’s recommendations, estimated at 35% of purchases, though exact
figures vary) and automates decisions (e.g., credit scores).
Models include:

Supervised Learning: Predicts outcomes from labelled data (e.g., fraud detection).

Unsupervised Learning: Finds patterns in unlabelled data (e.g., market
segmentation).

Reinforcement Learning: Optimizes actions through trial and error (e.g., robotics).
Each differs in data needs and task suitability, supported by machine learning literature AI
Models Overview.
What AI Does Well (and Doesn’t)
Research suggests AI excels at automation, big data analysis, and predictions, but struggles
with creativity, ethics, or context-heavy decisions, as seen in studies on AI limitations AI
Limitations.
Evidence-Based Decision-Making
Involves asking what, why, how, using evidence, and judging effects. Challenges include
incomplete/biased data, as illustrated by legal cases where weak evidence links fail (e.g.,
flawed witness testimony). It’s useful for data-rich fields, less so in urgent scenarios,
supported by decision-making research Evidence-Based Decision-Making.
Safe Use

Organizations: Clean data, secure AI, audit outputs, as per AI governance
guidelines AI Governance.

Individuals: Choose reputable tools, avoid personal data, question bias, aware of
pitfalls like prompt injection attacks.
AI and UN SDGs
AI has a net positive impact, enhancing healthcare and education, but risks job displacement
and privacy, detailed in Nature Communications. Opportunities include poverty reduction,
climate action; risks include inequalities and energy use.
Systems Thinking with AI
Example: Plan a career using market trends, skills data, and adaptability, leveraging AI for
insights, as seen in career planning tools Career Planning with AI.
Assignment and Conclusion
For assignments, use structured approaches like stock investment analysis, ensuring data
accuracy and AI benefits/limitations. Data and AI enhance decisions but need oversight to
manage risks.
Simplified Process for Complex Projects
This 10-step process, supported by case studies, ensures a holistic approach:
1. Define System Boundary: Clearly state what’s included/excluded.
2. Engage Stakeholders: Map needs and influence.
3. Use Iceberg Model: Analyse events, patterns, structures, mental models.
4. Apply V-Diagram: Map lifecycle and cross-check.
5. Conduct CATWOE: Define problem elements.
6. Balance Decision Worlds: Integrate political, emotional, rational inputs.
7. Leverage Data/AI: Use clean data and verified AI outputs.
8. Explore Solutions: Weigh multiple options and trade-offs.
9. Reflect & Iterate: Review and adapt based on feedback.
10. Measure Progress: Track metrics for effectiveness.
This process, grounded in evidence, ensures engineers manage complexity effectively.
3. Further Improvements Suggested by Grok (Not Started)
Add More Specific Examples

Why: Concrete examples make abstract concepts easier to grasp.

How: Include additional case studies, such as systems thinking applied to
sustainable engineering (e.g., designing renewable energy systems) or smart city
development.

Benefit: Readers can better connect theory to practice, making the report more
relatable.
2. Incorporate Interactive Elements

Why: Visual and interactive tools boost engagement and retention.

How: Add diagrams (e.g., flowcharts of the Iceberg Model) or, if possible, interactive
simulations to demonstrate systems thinking tools.

Benefit: Complex ideas become simpler and the learning experience becomes more
dynamic.
3. Expand on Emerging Trends

Why: Systems thinking evolves with technology and new methodologies.

How: Add a section on how it integrates with fields like machine learning, the Internet
of Things (IoT), or blockchain in engineering.

Benefit: Keeps the report forward-looking and relevant to modern challenges.
4. Enhance the Conclusion

Why: A strong conclusion reinforces key points and motivates action.

How: Summarize the main takeaways concisely and include a call to action,
encouraging engineers to apply systems thinking in their work.

Benefit: Leaves readers with a clear sense of purpose and next steps.
5. Include a Glossary

Why: Quick definitions improve understanding, especially for beginners.

How: Add a glossary defining terms like "feedback loops," "mental models," or
"systems thinking" at the end.

Benefit: Helps readers unfamiliar with the topic follow along more easily.
6. Add a Section on Common Pitfalls

Why: Highlighting mistakes helps readers avoid them.

How: Discuss common errors, like over-focusing on one decision-making approach
or neglecting stakeholder input.

Benefit: Makes the guide more practical and actionable.
7. Integrate Practitioner Feedback

Why: Real-world insights add credibility and depth.

How: Include quotes or tips from experienced engineers or systems thinking experts.

Benefit: Enhances authenticity and provides valuable perspectives.
8. Refine the Structure

Why: A clear layout improves readability.

How: Break long sections into shorter parts or use bullet points to highlight key ideas.

Benefit: Makes the report easier to navigate and digest.
9. Include a Checklist for the 10-Step Process

Why: A checklist turns theory into a usable tool.

How: Provide a simple list engineers can follow when applying the 10-step systems
thinking process.

Benefit: Increases practicality and user-friendliness.
10. Add a Section on Future Applications

Why: Exploring future uses shows the field’s potential.

How: Discuss how systems thinking could address challenges like climate change
engineering or space exploration.

Benefit: Inspires readers to think creatively about its long-term impact.