Advanced Complexity and Simulation Programme

Systems Thinking and Simulation for Complex Projects

Model feedback, delay, uncertainty and unintended consequences to improve decisions in complex project environments.

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Course overview

Understand why complex projects behave as they do—and test decisions before acting.

Systems Thinking and Simulation for Complex Projects develops the capability to examine project behaviour as the result of interacting feedback loops, delays, resource constraints, policies and stakeholder responses.

The programme moves from qualitative systems mapping to quantitative stock-and-flow modelling, scenario simulation, validation and decision use.

Participants build a working model, test alternative interventions and communicate both the insights and limitations of simulation evidence to decision-makers.

Learning outcomes

What participants will be able to do.

  • Frame a complex-project decision problem and define an appropriate system boundary.
  • Develop behaviour-over-time graphs and reference modes.
  • Construct and explain causal-loop diagrams with reinforcing, balancing and delayed feedback.
  • Identify recurring project structures such as rework, pressure, learning and capability erosion.
  • Translate selected causal logic into a stock-and-flow system-dynamics model.
  • Define parameters, equations, units, assumptions and initial conditions.
  • Run baseline, alternative-policy and uncertainty scenarios.
  • Perform structural, dimensional, extreme-condition, sensitivity and behavioural validation.
  • Interpret model results without presenting simulation as certain prediction.
  • Use simulation evidence to identify leverage points and support transparent executive decisions.

Who should attend

Designed for professionals working with complex, dynamic and high-consequence projects.

  • Complex-project and programme managers.
  • Project planners, controls and performance analysts.
  • Risk, assurance and decision-analysis professionals.
  • PMO and transformation leaders.
  • Systems engineers and technical managers.
  • Consultants and researchers supporting complex-project decisions.

Technical prerequisites

Analytical confidence is required; programming is not.

  • Participants should understand basic project lifecycle, schedule, cost and risk concepts.
  • Comfort with simple equations, graphs and spreadsheet relationships is recommended.
  • No prior system-dynamics or programming experience is required.
  • Participants need access to the selected modelling software and a suitable computer.
  • Corporate cohorts should confirm software installation, licensing and data arrangements before delivery.

Course structure

Five integrated modules from systems framing to working simulation and decision use.

The programme follows a complete modelling cycle: define the problem, map the structure, build the model, test confidence and use the evidence.

1

Module 1

Systems thinking for complex project environments

  • Complexity, emergence, non-linearity, feedback and delayed effects.
  • Project systems, boundaries, perspectives and levels of analysis.
  • Events, patterns, structures and mental models.
  • Behaviour-over-time graphs and reference modes.
  • Problem framing and model-purpose definition.
2

Module 2

Causal-loop diagrams and feedback structures

  • Variables, causal links, polarity and delay notation.
  • Reinforcing and balancing feedback loops.
  • Common project structures: rework, pressure, productivity, learning and erosion.
  • Boundary critique, assumptions and stakeholder perspectives.
  • Causal-loop model review and communication.
3

Module 3

Stock-and-flow system dynamics modelling

  • Stocks, flows, auxiliaries, constants and equations.
  • Units, dimensions and model consistency.
  • Translating causal logic into a quantitative model.
  • Initial conditions, time settings and integration logic.
  • Building and running a project system-dynamics model.
4

Module 4

Scenario simulation, sensitivity and model validation

  • Baseline behaviour and scenario design.
  • Sensitivity analysis and parameter uncertainty.
  • Extreme-condition, structure and dimensional-consistency tests.
  • Historical and behavioural validation.
  • Limitations, confidence, transparency and responsible interpretation.
5

Module 5

Decision use, policy testing and executive communication

  • Testing schedule, resource, quality, risk and governance policies.
  • Identifying leverage points and unintended consequences.
  • Comparing intervention timing, sequencing and intensity.
  • Translating simulation evidence into decision options.
  • Executive model briefing and implementation roadmap.

Learning experience

Frame, map, build, test, simulate and communicate.

Frame

Complex-system problem laboratory

Participants define the decision problem, system boundary, stakeholders, reference behaviour and modelling purpose.

Map

Causal-loop modelling workshop

Teams build and critique feedback structures that explain persistent project behaviour.

Build

System-dynamics modelling laboratory

Participants convert selected causal logic into a working stock-and-flow simulation model.

Test

Validation and sensitivity challenge

Teams examine dimensional consistency, extreme conditions, assumptions, uncertainty and behavioural plausibility.

Simulate

Policy and scenario experiments

Participants compare alternative interventions and examine delayed, indirect and unintended effects.

Communicate

Executive decision briefing

Teams present the model, evidence, limitations, insights and recommended actions to a simulated decision panel.

Course completion

Demonstrate transparent modelling and responsible decision support.

  • Participate actively in systems-mapping, model-building and simulation activities.
  • Complete the principal causal-loop and stock-and-flow modelling outputs.
  • Demonstrate model-validation, sensitivity and limitation awareness.
  • Present simulation evidence and a decision recommendation.
  • Prepare a practical workplace modelling or application plan.

Participant outputs

Typical course outputs include:

  • Complex-project problem statement and system boundary.
  • Behaviour-over-time graphs and reference modes.
  • Causal-loop diagram with documented feedback logic.
  • Working stock-and-flow simulation model.
  • Assumption, data and parameter register.
  • Validation and sensitivity test record.
  • Scenario comparison and executive decision briefing.

Delivery options

Choose the format that fits your modelling environment and project problem.

Instructor-led

Live Classroom

Face-to-face delivery with guided modelling, software practice, peer review, simulation experiments and executive presentations.

Instructor-led

Live Virtual Classroom

Interactive online delivery using shared modelling environments, live demonstrations, breakout reviews and remote simulation work.

Flexible

Blended Learning

A structured combination of technical preparation, live modelling laboratories, independent model development and follow-up review.

Organisation-specific

Corporate and In-Company

Tailored delivery using an approved project problem, organisational data and software environment where appropriate.

Frequently asked questions

Methods, software and participation.

What modelling methods are used?

The core methods are behaviour-over-time analysis, causal-loop diagramming and stock-and-flow system dynamics. Scenario analysis, sensitivity testing and selected spreadsheet simulation techniques support the core model.

Which software is used?

The standard learning environment uses Vensim PLE or Insight Maker for system-dynamics modelling, supported by spreadsheet analysis. Corporate delivery may use an approved equivalent where the required modelling capabilities are available.

Do participants need programming experience?

No programming experience is required. Participants should be comfortable with structured analytical thinking, basic quantitative relationships and spreadsheet use.

Is this a general project-planning software course?

No. The programme focuses on feedback, dynamic behaviour, simulation and decision support. It does not teach routine scheduling or project-management software administration.

How is model quality assessed?

Participants test structure, dimensions, extreme conditions, assumptions, sensitivity, behavioural plausibility and decision relevance. Models are treated as transparent decision-support tools, not unquestionable predictions.

What instructor competence is required?

Delivery requires demonstrated practical competence in systems thinking, causal-loop mapping, system-dynamics modelling, simulation interpretation and the selected software environment.

Take the next step

Use systems thinking and simulation to challenge intuition before committing to action.

Discuss public delivery, a specialist modelling cohort or a tailored programme built around a complex project decision problem.

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Models are simplified representations intended to support learning and decision-making. They do not eliminate uncertainty or replace accountable professional judgement. Software, datasets and exercises may be adapted to the cohort while preserving the stated modelling methods and learning outcomes.