Description

This curriculum engages learners in the iterative, politically nuanced work of building and maintaining system models across complex organizations, comparable to multi-phase advisory engagements where modeling choices must withstand scrutiny from technical, operational, and executive stakeholders.

Module 1: Defining System Boundaries and Scope

Selecting which organizational units to include in a system model when conflicting stakeholder priorities demand different scoping assumptions.
Deciding whether to treat external regulatory bodies as active system components or as environmental constraints.
Handling requests to expand system boundaries mid-analysis due to newly identified interdependencies with adjacent processes.
Determining the appropriate level of abstraction when modeling a supply chain to avoid oversimplification or excessive detail.
Resolving disagreements among leadership about whether customer behavior should be modeled as part of the internal system.
Documenting boundary decisions to ensure auditability and reproducibility during regulatory or compliance reviews.

Module 2: Identifying and Mapping Feedback Loops

Distinguishing between reinforcing and balancing loops in workforce attrition models where both retention programs and burnout coexist.
Validating suspected feedback mechanisms using historical performance data when causal relationships are not immediately evident.
Deciding whether to model delayed feedback explicitly when forecasting the impact of policy changes on employee engagement.
Addressing stakeholder resistance when feedback analysis reveals counterintuitive outcomes, such as cost-cutting leading to higher long-term expenses.
Integrating qualitative insights from interviews into causal loop diagrams without introducing subjective bias.
Managing version control when iterative refinements to feedback structures alter the interpretation of system behavior.

Module 3: Modeling Stock and Flow Dynamics

Choosing appropriate units of measure for stocks (e.g., backlog in hours vs. number of tickets) to ensure consistency across departments.
Calibrating flow rates in a production pipeline model when real-time data is incomplete or inconsistently reported.
Handling discrepancies between reported inventory levels and modeled stock due to unrecorded transfers or losses.
Designing flow rules that reflect policy constraints, such as approval gates that limit the rate of project initiation.
Deciding whether to model a resource pool as a single aggregated stock or disaggregate it by skill type or location.
Testing model sensitivity to initial stock values when historical baselines are unreliable or missing.

Module 4: Integrating Multiple Perspectives and Mental Models

Facilitating workshops where department heads attribute system failures to different root causes based on their operational focus.
Reconciling conflicting mental models of customer journey stages between marketing and customer support teams.
Deciding which stakeholder perspectives to prioritize when time and modeling resources are limited.
Documenting assumptions derived from interviews to trace how individual biases may influence system structure.
Using role-playing exercises to expose hidden assumptions in how executives perceive organizational responsiveness.
Managing power dynamics in cross-functional sessions where senior leaders dominate the definition of system behavior.

Module 5: Evaluating Leverage Points and Intervention Design

Assessing whether to target a policy rule or a performance metric when both appear to influence employee productivity.
Estimating the implementation lag for changing incentive structures in a sales organization resistant to new KPIs.
Weighing the political feasibility of altering information flows against the technical benefits of improved transparency.
Simulating unintended consequences of shortening project review cycles, such as increased rework due to rushed approvals.
Comparing the long-term impact of training investments versus hiring sprees in addressing skill gaps.
Defining success criteria for interventions that account for both quantitative outcomes and cultural acceptance.

Module 6: Validating and Stress-Testing System Models

Designing edge-case scenarios to test whether a workforce planning model breaks under extreme turnover assumptions.
Comparing model outputs against actual outcomes from past organizational changes to assess predictive accuracy.
Deciding how much historical data is sufficient to validate a model of a rapidly evolving digital transformation initiative.
Handling discrepancies between model predictions and expert judgment when both are considered credible.
Conducting blind tests where modelers are unaware of real-world outcomes to reduce confirmation bias.
Updating validation protocols when external shocks, such as market disruptions, invalidate prior behavioral assumptions.

Module 7: Communicating System Insights to Decision Makers

Selecting which model outputs to visualize when presenting to executives with limited tolerance for complexity.
Translating dynamic behavior into narrative form without oversimplifying causal mechanisms or time delays.
Anticipating and preparing responses to skepticism about counterintuitive recommendations derived from system analysis.
Choosing between static reports and interactive dashboards based on the audience’s technical fluency and decision-making cadence.
Redacting sensitive model details when sharing insights across departments with competing performance incentives.
Structuring presentations to highlight trade-offs rather than definitive answers, preserving decision-making autonomy.

Module 8: Sustaining Systems Thinking in Organizational Practice

Embedding system diagrams into standard operating procedures without creating documentation overhead that teams ignore.
Assigning ownership for maintaining and updating system models after the initial project team disbands.
Integrating system thinking checkpoints into existing governance forums, such as quarterly strategy reviews.
Measuring the adoption of systems-based reasoning through observable changes in meeting discussions or proposal structures.
Addressing turnover-related knowledge loss by standardizing model annotation and versioning practices.
Resisting pressure to revert to linear cause-effect explanations during crisis response when systemic factors are at play.