Program Overview
Today’s supply chains must be built for uncertainty—where decisions are no longer linear but probabilistic. This program empowers planning, operations, and finance professionals to use digital tools for structured what-if analysis, stress testing, and contingency modeling. Participants will work with a range of platforms—from Excel-based scenarios and Monte Carlo simulations in Python, to enterprise-grade tools like Anaplan and Kinaxis—building capability to simulate outcomes under varying demand, capacity, cost, and lead time conditions. The course emphasizes cross-functional alignment, agility, and decision-making under volatility, with real use cases from S&OP, working capital planning, and demand-supply balancing in pharma, auto, and CPG industries.
Features
- Develop scenario planning logic using Excel and Python (e.g., best-case, worst-case, base-case)
- Understand use cases for enterprise tools like Anaplan and Kinaxis in cross-functional planning
- Simulate impacts of variability in demand, lead time, costs, and capacity constraints
- Enable agile decision-making with structured playbooks and dynamic scenario dashboards
Target audiences
- Supply Chain Planners
- Finance Controllers
- Operations Heads
- Strategy Teams
- Digital COEs
Curriculum
- 4 Sections
- 16 Lessons
- 1 Day
- 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
Instructor
