Simulation Models for Decision Making (Coursera)

We can model uncertainties by using the concepts of probability and stepwise thinking. Stepwise thinking allows us to break down the problem in smaller components, explore dependencies between related events and allows us to focus on aspects of problem that are prone to changes due to future uncertainties.
The course will introduce you to advanced Excel techniques to model and execute simulation models. Many of the Excel techniques learned in the course will be useful beyond simulation modeling. We will learn both Monte Carlo simulation techniques where overall outcome is of primary interest and discrete event simulation where intermediate dependencies between related events might be of interest. The course will introduce you to several practical issues in simulation modeling that are normally not covered in textbooks. The course uses a few running examples throughout the course to demonstrate concepts and provide concrete modeling examples.
After taking the course a student will be able to develop fairly advanced simulation models to explore fairly broad range of business environments and outcomes.
Course 4 of 4 in the Analytics for Decision Making Specialization.

Syllabus

WEEK 1
Probability Concepts
Uncertainty leads to challenges in decision making. Mathematically, we represent uncertainty by defining probabilities when several of the outcomes are possible in the future. This modules provides an overview of probability concepts that are essential to lay a good foundation for simulation modeling. We will also get our first exposure to Excel based simulations.

WEEK 2
Probability Distributions and Introduction to Monte Carlo Simulations
While being able to estimate probabilities using mathematical relationships is important, a lot of natural events follow or approximate some nicely defined probability distribution functions such as Uniform, Exponential and Normal Distributions. To effectively build simulation models, it is important to understand how to use these distributions. Further, we may need to find what distribution does our observed data follow. This module introduces the finer details of working with probability distribution functions and introduces the types of simulation models as well as some practice based tricks to work with real-world data that may not be complete or may not fit a given distribution exactly.

WEEK 3
Monte Carlo Simulations
We started by stating that simulation is one of the most flexible modeling approaches. This module demonstrates that flexibility. In this module, four Monte Carlo simulation models are built for a coffee shop. The models increase in technical complexity and sophistication to demonstrate various issues that modelers have to consider in building these models depending upon the type of questions that need to be answered. The lessons explain which models can answer certain type of questions and what questions may not be answered by a certain type of model. The results obtained from various models are then compared and discussed to understand the tradeoffs in choice of a particular model choice.

WEEK 4
Counterfactual Analysis and Discrete Event Simulations
In this module we wrap up the Monte Carlo Simulation modeling by looking at modeling special cases and doing counterfactual analysis (examining scenarios that may not have existed or initiatives that have not actually been implemented). We then examine the power of Discrete Event simulation. The goal of Discrete Event simulation modeling discussion is to introduce you to examine the dependencies in events and how these dependencies can be modeled in Excel with some innovative thinking, even though Excel does not natively support any functionality to support Discrete Event simulation. The material in this part is completely original and is designed for this course and will not be found in any books.