Operations Analytics Course Syllabus

Overview: This course provides a comprehensive introduction to operations analytics, focusing on data-driven decision-making in business operations. Over four weeks, learners will explore key analytical methods including forecasting, optimization, simulation, and decision analysis. The course is designed for beginners and requires approximately 16 hours to complete, with each module taking about 4 hours. Real-world applications such as the Newsvendor problem are used throughout to illustrate core concepts.

Module 1: Introduction, Descriptive and Predictive Analytics

Estimated time: 4 hours

• Introduction to the Newsvendor problem
• Foundations of descriptive analytics for operations
• Using historical demand data to build forecasts
• Random variables, descriptive statistics, and forecasting tools
• Measures for judging forecast quality

Module 2: Prescriptive Analytics, Low Uncertainty

Estimated time: 4 hours

• Building optimization models for business decisions
• Algebraic formulation of optimization problems
• Converting models into spreadsheet formats
• Using spreadsheet solvers to identify optimal solutions

Module 3: Predictive Analytics, Risk

Estimated time: 4 hours

• Building simulation models for uncertain settings
• Interpreting simulation results
• Common measures of risk and reward
• Using simulation to estimate outcomes

Module 4: Prescriptive Analytics, High Uncertainty

Estimated time: 4 hours

• Introduction to decision trees under uncertainty
• Evaluating decisions using decision trees
• Integrating optimization, simulation, and decision trees
• Applying the Newsvendor problem with simulation and optimization

Module 5: Final Project

Estimated time: 4 hours

• Apply analytics to a real-world operations challenge
• Develop a quantitative solution using course methods
• Submit analysis and decision recommendation

Prerequisites

• Familiarity with basic algebra
• Basic understanding of spreadsheets (e.g., Excel)
• Some exposure to statistics or probability is helpful but not required

What You'll Be Able to Do After

• Model future demand uncertainties and predict outcomes
• Develop optimization models for low-uncertainty decisions
• Apply simulation models to complex, uncertain business decisions
• Use decision trees to evaluate choices under uncertainty
• Solve real-world operations problems using quantitative methods