Description

This curriculum engages learners in the iterative, cross-functional problem-solving required of internal strategy and transformation teams, addressing the same complexities found in multi-phase organizational change initiatives where system models must evolve amid conflicting stakeholder demands, incomplete data, and entrenched operational constraints.

Module 1: Defining System Boundaries and Stakeholder Alignment

Selecting which organizational units to include or exclude when modeling a supply chain disruption response, balancing comprehensiveness with analytical tractability.
Mapping conflicting stakeholder objectives—such as cost reduction versus service reliability—into a shared system representation without oversimplification.
Deciding whether to treat regulatory compliance as a hard constraint or a dynamic feedback loop within the system model.
Resolving disagreements among department heads about where system responsibility begins and ends during cross-functional process redesign.
Documenting assumptions about external market behaviors when those factors influence internal operations but lie outside direct control.
Establishing criteria for when to expand system boundaries mid-engagement due to emergent interdependencies.

Module 2: Causal Loop and Stock-Flow Modeling in Practice

Choosing between reinforcing and balancing loop representations when historical data shows ambiguous behavioral patterns.
Calibrating stock-flow models with incomplete time-series data by applying conservative estimation protocols and sensitivity testing.
Introducing time delays in workforce hiring models to reflect real onboarding and productivity ramp-up periods.
Validating model structure with subject matter experts who rely on intuition rather than quantitative reasoning.
Managing model complexity when adding feedback loops causes exponential growth in scenario permutations.
Translating qualitative interview insights into quantifiable variables without distorting original stakeholder intent.

Module 3: Identifying and Managing Leverage Points

Evaluating whether to target policy-level changes or shift mental models when both appear viable for reducing operational waste.
Assessing the political feasibility of intervening at high-leverage points that disrupt entrenched power structures.
Measuring the lag time between implementing a leverage point intervention and observing system-level outcomes.
Allocating budget to low-visibility leverage points with delayed returns versus high-visibility quick wins.
Monitoring unintended consequences when adjusting a leverage point such as performance incentives in a sales organization.
Documenting thresholds at which leverage point effectiveness diminishes due to system saturation or adaptation.

Module 4: Dynamic Simulation and Scenario Testing

Selecting simulation granularity—agent-based versus aggregate—based on data availability and decision-making timelines.
Designing stress test scenarios that reflect plausible but extreme conditions, such as dual supplier failure during geopolitical instability.
Integrating real-time operational data into simulations without compromising model stability or introducing noise bias.
Communicating probabilistic simulation outputs to executives accustomed to deterministic forecasts.
Version-controlling simulation models when multiple consultants modify parameters concurrently across global teams.
Establishing rollback protocols when simulation-driven decisions produce adverse real-world outcomes.

Module 5: Organizational Learning Loops and Feedback Integration

Embedding feedback collection mechanisms into existing workflows without increasing employee reporting burden.
Designing double-loop learning processes that challenge underlying assumptions in budget allocation models.
Overcoming resistance to feedback systems perceived as surveillance tools by frontline managers.
Aligning learning cycle frequency with fiscal planning cycles to ensure insights inform budget decisions.
Archiving feedback data to support longitudinal analysis while complying with data retention policies.
Balancing transparency in feedback reporting with the need to protect individual performance data.

Module 6: Navigating Systemic Inertia and Resistance to Change

Diagnosing whether resistance stems from structural misalignment or cultural norms before selecting intervention type.
Sequencing change initiatives to avoid triggering defensive coalitions across departments.
Using pilot programs to generate evidence of system improvement without full-scale commitment.
Adjusting communication strategies when early adopters misrepresent system changes to skeptical peers.
Maintaining momentum during leadership transitions that result in shifted strategic priorities.
Tracking hidden workarounds that emerge when formal system changes conflict with operational realities.

Module 7: Ethical Implications and Long-Term System Consequences

Assessing downstream equity impacts when optimizing logistics networks for speed and cost.
Disclosing model limitations to decision-makers who may treat simulations as predictive truth.
Addressing data bias in historical records that, if uncorrected, perpetuate inequitable system behaviors.
Resisting pressure to manipulate system boundaries to exclude ethically sensitive but inconvenient factors.
Planning for system decommissioning and data disposition when models are retired after strategic shifts.
Documenting ethical trade-offs made during model development for future audit and review.

Module 8: Cross-System Interdependencies and Scalability Challenges

Mapping dependencies between IT infrastructure and human decision rhythms when automating response protocols.
Adapting a successful regional model for global deployment while accounting for regulatory and cultural variation.
Managing cascading failures when a change in procurement systems impacts production scheduling and customer delivery.
Coordinating model updates across interlinked systems maintained by separate business units with misaligned timelines.
Allocating shared resources during system adaptation efforts when competing initiatives have equal strategic priority.
Designing modular system components to enable future integration with third-party platforms not yet specified.