# Mount Carlo Simulation /GEMBA WALK-GO-SEE-THINK-DO

Course Overview:

Risk assessment and decision making are two critical skills required for each task and strategy to be executed.

Monte Carlo simulation is a mathematical computerized approach and tool that enables executives and employees in their different positions to quantitatively analyze the risks and the outcome of their decisions.

Monte Carlo recreation can be utilized to handle a scope of issues in essentially every field, for example, account, designing, flexibly chain, and science. It is likewise alluded to as a numerous probability simulation.

Who Can Enroll:

• Executives,
• Project Managers,
• Process improvement resources,
• Lean six sigma resources,
• Engineering resources,
• R&D.

Learning Outcome:

• Understand and perform defensible risk assessments using Monte Carlo Simulation.

Course Features:

• Material and templates included in course cost.
• Laptops required.
• To achieve certification, participants shall achieve 80% in CHOOLS exam for course only.
• Course could be customized for organization requirements including company LOGO.

Why Choose Chools?

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• Quiz, Exam prep, Q&As, Case-studies.

Course Outline:

Introduction to Risk analysis using software adding uncertainty to models and interpreting output.

• Introduction to probability, Monte Carlo simulation and statistics.
• Exploring the @Risk environment
• Adding uncertainty to deterministic models.
• Simulating and simulation settings
• Output and results interpretation
• Reporting simulation results in Excel
• Discrete distribution

Making defensible distribution selections, correlation, and decision modelling

• Model Logic
• Choosing the “right “distribution”
• Fitting distributions to data
• Justifying expert opinion using alternate parameters
• Policy comparison
• Correlation-its importance and implementation
• Special @ risk functions

Decision- Making and optimization using precision Tree and risk optimizer and evolver

• Modeling decisions under uncertainty using precision Tree
• Hands-on example using cumulative pay-off trees
• Sensitivity analysis
• Introduction to other aspects of Precision Tree (Use with @ Risk, non-cumulative trees, other features)
• Risk Optimizer to conduct optimizer under uncertainty
• The use of evolver to perform optimization

Model auditing, and predictive and statistical Analysis using TOPRANK, Stat tools and neural Tools.

• Use Top Rank to find model inputs and perform sensitivity analysis
• A selection of statistical procedures using Stat tools (hands on example for correlation, time series forecasting, multiple regression)
• Introduction to predictive modeling using Neural Tools (Hands-on examples with numerical and categorical variables