Curriculum
- 4 Sections
- 16 Lessons
- 1 Day
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- Scenario Planning Fundamentals4
- 1.1Concepts: Scenario vs sensitivity analysis, demand-supply balancing under uncertainty
- 1.2What-If Simulation, Base Case vs Best/Worst Case, Bottleneck Modeling, Trade-off Tree
- 1.3Use Case: Simulating response to a 20% raw material price hike across 3 sourcing models
- 1.4Scenario planning framework – triggers, levers, assumptions, outputs
- Excel as a Quick Scenario Modeling Tool3
- Python for Advanced Planning Simulation4
- 3.1Libraries: Pandas, NumPy, SimPy, Plotly, SciPy Optimization
- 3.2Monte Carlo Simulation, Constraint Programming, Rolling Horizon Planning
- 3.3Case: Pharma company used Python to simulate raw material allocation under supply disruption
- 3.4Exercise: Code a basic Python scenario to optimize stock under lead-time variability
- Anaplan & Kinaxis for Enterprise-Grade Scenario Planning5
- 4.1Anaplan: Driver-based modeling, connected planning
- 4.2Kinaxis RapidResponse: Supply chain simulation grid, constrained planning
- 4.3Model Hub, Planning Cockpit, What-Next Decision Tree, Time Horizon Driver
- 4.4Case based learning: Consumer electronics company used Kinaxis to simulate demand shifts across APAC in real time
- 4.5Platform comparison matrix – flexibility, scalability, integration capabilities
What-If Simulation, Base Case vs Best/Worst Case, Bottleneck Modeling, Trade-off Tree
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