Simulation Models for Decision Making
Learn to model business uncertainties through Monte Carlo and discrete event simulations using Excel to make better decisions by exploring various outcomes.
Learn to model business uncertainties through Monte Carlo and discrete event simulations using Excel to make better decisions by exploring various outcomes.
This comprehensive course teaches simulation modeling techniques to address business problems with inherent uncertainties. Students will learn to develop models that explore various possible outcomes to protect business interests against unwanted scenarios. The course begins with foundational probability concepts before introducing advanced Excel techniques for both Monte Carlo simulations (focused on overall outcomes) and discrete event simulations (emphasizing intermediate dependencies between related events). Through practical examples like a coffee shop simulation that increases in complexity, students will understand different modeling approaches and their tradeoffs. Special attention is given to real-world implementation challenges not typically covered in textbooks, including counterfactual analysis and innovative Excel solutions for discrete event simulations. By the end of the course, participants will be able to develop sophisticated simulation models applicable to a broad range of business environments.
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What you'll learn
Develop simulation models to address complex business problems with uncertainties Apply probability concepts to model business environments Create Monte Carlo simulations using Excel for decision support Analyze outcomes from different simulation approaches Design progressively complex models to answer specific business questions Implement discrete event simulations to model dependencies between related events Perform counterfactual analysis to evaluate hypothetical scenarios Use empirical and theoretical probability distributions in simulation models
Skills you'll gain
This course includes:
8 Hours PreRecorded video
12 assignments
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FullTime access
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There are 4 modules in this course
The course "Simulation Models for Decision Making" provides a thorough introduction to simulation techniques for business decision-making under uncertainty. The curriculum is structured in progressive modules that build analytical skills systematically. Students first learn essential probability concepts that serve as the foundation for simulation modeling, including axioms of probability, permutations, combinations, and joint probability. The course then explores probability distributions (uniform, normal, exponential) and introduces Monte Carlo simulation techniques with practical Excel implementations. Through increasingly complex coffee shop simulations, students learn to model various business scenarios with different levels of sophistication. The final module covers counterfactual analysis and introduces discrete event simulation for modeling dependencies between sequential events, including innovative approaches to implement these in Excel despite the platform's limitations. Throughout the course, students gain hands-on experience with Excel-based modeling techniques that have applications beyond simulation.
Week 1: Probability Concepts
Module 1 · 4 Hours to complete
Week/Module 2: Probability Distributions and Introduction to Monte Carlo Simulations
Module 2 · 4 Hours to complete
Week 3: Monte Carlo Simulations
Module 3 · 3 Hours to complete
Week 4: Counterfactual Analysis and Discrete Event Simulations
Module 4 · 4 Hours to complete
Fee Structure
Instructor
Information Systems and Decision Sciences Expert
Dr. Alok Gupta serves as Associate Dean of Faculty and Research and holds the Curtis L. Carlson Schoolwide Chair in Information Management at the University of Minnesota's Carlson School of Management. His academic credentials include a Ph.D. in Management Science and Information Systems from the University of Texas-Austin, an MS in Mine Electrical Systems from Pennsylvania State University, and a BS in Mining Engineering from Banaras Hindu University. Through his Coursera course "Simulation Models for Decision Making," he helps students understand complex decision-making processes using simulation techniques. His research focuses on economic engineering of systems, particularly examining participant incentives in various transactional contexts from internet platforms to human-AI collaboration. His work has earned numerous accolades, including three INFORMS Design Science Awards, the AIS Impact Award, and the prestigious LEO Award for lifetime achievement. As Editor-in-Chief of Information Systems Research (2017-2022) and Publisher of MIS Quarterly since 2004, he has significantly influenced the field of information systems. He teaches courses in human-AI collaboration, computer networking, electronic commerce, and IT infrastructure at undergraduate, MBA, and Ph.D. levels.
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4.5 course rating
52 ratings
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