Introduction
Please note that this is a preliminary course description. The final version will be published in June 2027.
Why do smart strategies fail?
Business leaders and policymakers rely on data—but even the best data analysts often miss the hidden forces that derail decisions. Linear thinking leads to short-term wins and long-term disasters: cost-cutting that hinders innovation, growth strategies that exhaust resources, or policies that solve one crisis while sparking three others. Business managers and policymakers keep solving one problem only to create another. This course teaches you to see the hidden connections, model long-term impacts, and make decisions that stick—whether you’re optimizing a single business unit, a supply chain or aligning business goals with the UN’s 2030 SDGs.
The missing tool? Systems thinking and dynamics
This course teaches you to model complex systems—not as static snapshots, but as dynamic, interconnected networks where every decision ripples outward and often back inward. You’ll use system dynamics (the same methodology behind climate models and supply chain simulations) to:
1. Map the "real" structure of problems
- Spot feedback loops, delays, and leverage points that traditional analysis ignores
- Diagnose why "obvious" solutions (like pouring money into R and D or slashing prices) often backfire
2. Build simulation models
- Turn qualitative mental models into quantitative, testable systems using software like Vensim or Anylogic
- Design "what-if" scenarios (e.g., What if we onboarded AI too fast? What is the impact of a tariff war on our business?)
3. Stress-test strategies before they go live
- Run virtual experiments in management flight simulators you develop yourself—where you can crash a digital supply chain (not a real one) and learn from the wreckage
- Trade off short-term KPIs against long-term business resilience
4. Master complexity for competitive edge
- Apply models to core business challenges: pricing volatility, workforce planning, or regulatory shifts
- Present data-driven policy recommendations that account for second-order effects (the surprising side-effects of decisions most analysts miss)
Who thrives here?
- Analysts who are tired of "flat" statistical models that ignore real-world chaos (where cause/effect is anything but straightforward)
- Future consultants who want to anticipate domino effects, not just optimize snapshots