Simulation Models for Decision Making

Go to Course: https://www.coursera.org/learn/simulation-models-for-decision-making

Introduction

# Course Review: Simulation Models for Decision Making on Coursera As we navigate the increasingly complex landscape of business decision-making, the importance of using data-driven methodologies cannot be overstated. The **Simulation Models for Decision Making** course on Coursera emerges as a vital educational resource for third- and fourth-year undergraduate students and graduate students who seek to harness simulation techniques to address a variety of business challenges. ### Overview This course provides a comprehensive introduction to simulation modeling, focusing on its application in solving everyday and complex business problems that often lack definitive solutions due to inherent uncertainties. It guides students through the process of modeling uncertainties, helping them explore a range of possible outcomes and make informed decisions to mitigate unwanted consequences. The content is rich and tailored for those looking to deepen their understanding of practical simulation applications in business contexts. ### Course Syllabus Breakdown #### **Week 1: Probability Concepts** The course begins with a crucial foundation in probability concepts. Understanding uncertainty is key to effective decision-making, and this module equips students with basic probability theory. With insightful discussions and an introduction to Excel-based simulations, learners will gain essential analytical tools to represent uncertainty quantitatively. #### **Week 2: Probability Distributions and Introduction to Monte Carlo Simulations** As students progress, they encounter various probability distribution functions, such as Uniform, Exponential, and Normal distributions. Recognizing how natural events align with these distributions is fundamental to building robust simulation models. This week's lessons also emphasize practical tricks for working with incomplete or non-standard data, enhancing students' capabilities in real-world scenarios. #### **Week 3: Monte Carlo Simulations** Delving deeper into the flexibility of simulation, this module provides practical applications through the development of four Monte Carlo simulation models for a coffee shop. By varying technical complexity, students learn how to approach different business questions with the most appropriate modeling techniques. The comparative analysis of results further encourages critical thinking about simulation trade-offs. #### **Week 4: Counterfactual Analysis and Discrete Event Simulations** In the final week, the course integrates the knowledge accumulated thus far by exploring counterfactual analysis—assessing hypothetical scenarios and unimplemented initiatives. The introduction to Discrete Event simulation highlights how event dependencies can be modeled innovatively in Excel, despite its limitations. This original content promises insights that are rarely found in traditional learning resources. ### Course Experience The **Simulation Models for Decision Making** course adopts a user-friendly format, with a blend of video lectures, readings, and hands-on assignments that promote active learning. The instructors' expertise is evident, providing clear explanations and practical examples that resonate with both theoretical and real-world applications. Learners will appreciate the interactive elements of the course, enabling them to engage with the material and apply their knowledge effectively. ### Recommendation I highly recommend the **Simulation Models for Decision Making** course to anyone seeking to enhance their analytical toolkit. Whether you're a student preparing for a career in business analytics, finance, operations management, or any field that requires adept decision-making under uncertainty, this course will empower you with foundational knowledge and practical skills. By the end of this course, participants will not only understand the principles underlying simulation models but also be equipped to apply these techniques to real-world business problems confidently. Enroll today, and take a significant step toward mastering the art of decision-making in the face of uncertainty!

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.

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.

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.

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.