System Dynamics and Modelling

1. Training Introduction

The System Dynamics and modelling training equips professionals with the knowledge and skills to analyze, simulate, and manage complex systems in dynamic environments. Participants will learn to understand feedback loops, time delays, and non-linear relationships in systems, enabling them to make informed decisions, optimize performance, and predict outcomes in engineering, business, public policy, and infrastructure projects.

The program integrates theoretical concepts with practical simulations, case studies, and software tools for system modelling and analysis.

 

2. Training Objective

The program aims to enable participants to:

• Understand and apply system dynamics principles to real-world systems.
• Build, simulate, and analyze dynamic models for decision-making.
• Identify feedback loops, causal relationships, and system behaviors.
• Forecast system performance under different scenarios.
• Support policy formulation, strategic planning, and project optimization.
• Enhance problem-solving capabilities for complex, interdependent systems.

 

3. Targeted Group

• System analysts, engineers, and project managers
• Policy planners and strategic decision-makers
• Operations and process improvement managers
• Researchers and academics in engineering and management
• Professionals involved in modeling, simulation, and optimization of complex systems

 

4. Course Duration

12–16 Days

• Standard comprehensive programme: 16 days
• Condensed programme for experienced professionals: 12 days

 

5. Training Methodology

• Instructor-led lectures with interactive discussions
• Case studies and real-world system analysis
• Hands-on exercises in system dynamics modeling software (e.g., Vensim, Stella, AnyLogic)
• Group workshops for problem-solving and scenario analysis
• Simulation exercises and performance forecasting
• Capstone project integrating system modeling, analysis, and strategic recommendations
• Assessment through exercises, model deliverables, and final presentations

 

6. Course Content

Module 1: Introduction to System Dynamics

• Fundamentals of system thinking
• Understanding dynamic systems and complexity
• Key concepts: stocks, flows, feedback loops

Module 2: System Behavior and Modeling Principles

• Causal loop diagrams
• System archetypes and behavior patterns
• Non-linear dynamics in systems

Module 3: Quantitative Modelling Techniques

• Differential equations for system modeling
• Discrete vs. continuous simulation
• Parameter estimation and calibration

Module 4: Feedback Loops and Delays

• Positive and negative feedback loops
• Time delays and their impact on system behavior
• Case studies on feedback-driven systems

Module 5: Modeling Tools and Software

• Introduction to system dynamics software (Vensim, Stella, AnyLogic)
• Model building and validation
• Simulation best practices

Module 6: Data Collection and System Analysis

• Identifying system variables and data sources
• Data preprocessing for modeling
• Sensitivity analysis

Module 7: Policy Design and Scenario Analysis

• Designing interventions in dynamic systems
• Scenario planning and forecasting
• Simulation of alternative strategies

Module 8: System Optimization and Control

• Performance indicators and optimization objectives
• Control strategies for dynamic systems
• Feedback and corrective mechanisms

Module 9: Systems Thinking in Business and Operations

• Supply chain and operational systems modeling
• Demand-supply balancing and inventory management
• Process improvement using system dynamics

Module 10: Systems Dynamics in Public Policy

• Policy modeling for healthcare, environment, and infrastructure
• Dynamic impact assessment
• Resource allocation modeling

Module 11: Modeling Uncertainty and Risk

• Probabilistic modeling
• Monte Carlo simulation for risk analysis
• Managing uncertainty in dynamic systems

Module 12: Complex Systems and Emergent Behavior

• Identifying emergent patterns in systems
• Modeling interacting subsystems
• Case studies in complex socio-technical systems

Module 13: Decision Support Systems

• Using system dynamics for decision-making
• Scenario evaluation and trade-off analysis
• Reporting and visualization of results

Module 14: Integration with Other Analytical Tools

• Linking system dynamics with statistical, optimization, and AI tools
• Hybrid modeling approaches
• Software integration for advanced simulations

Module 15: Advanced System Dynamics Applications

• Modeling sustainability, energy systems, and smart infrastructure
• Urban planning and environmental modeling
• Strategic project management using dynamic models

Module 16: Capstone Project – System Modeling and Simulation

• Build a comprehensive dynamic model of a selected system
• Simulate scenarios, analyze feedback loops, and forecast performance
• Present findings, recommendations, and policy/management interventions

 

7. Expected Learning Outcomes

Participants will be able to:

• Apply system dynamics principles to analyze complex systems.
• Build, simulate, and validate dynamic models using industry-standard software.
• Identify feedback loops, delays, and non-linear relationships affecting system behavior.
• Optimize system performance and forecast outcomes under various scenarios.
• Support decision-making in engineering, business, and policy contexts.
• Integrate system dynamics with other analytical and data-driven tools.
• Develop actionable recommendations for system improvement and strategic interventions.

 

8. Certificate of Completion

Upon successful completion of all modules, practical exercises, and the capstone project, participants will receive:

Certificate of Completion

System Dynamics and Modelling

Issued by FOTADE Training, Research and Resource Development Centre

This certificate validates the participant’s competence in system modeling, dynamic simulation, and data-driven decision support for complex systems.