Course:FRE585
| FRE 585: Quantitative Methods for Business and Resource Management | |
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| FRE 585 | |
| Section: | |
| Instructor: | Dr. Michael Johnson |
| Email: | mjohnson@mail.ubc.ca |
| Office: | |
| Office Hours: | TBA |
| Class Schedule: | Tues and Thurs, 4:00 – 5:30pm |
| Classroom: | MCML 154 |
| Important Course Pages | |
| Syllabus | |
| Lecture Notes | |
| Assignments | |
| Course Discussion | |
COURSE DESCRIPTION
This course will provide the necessary foundation and learning experience for students to apply a variety of modeling and analytic techniques to business and resource management problems. This class will concentrate on frequently used quantitative and decision-making models that include decision analysis, resource allocation models, optimization such as linear programming (allocation and scheduling of resources), forecasting and predictive analytics, simulation modeling, sustainable operations, and supply chain management.
LEARNING OUTCOMES
Decision Analytics
- Build and evaluate decision models to determine using mathematical expectation, risk, opportunity loss and the value of perfect information. Apply sensitivity analysis, critical thinking and judgement in the context of data and analytic interpretations.
Predictive Analytics
- Use predictive analytics and forecasting tools on data that exhibits stationary, trend and seasonal characteristics. Evaluate predictions using standard forecasting metrics and cross-validation techniques.
Prescriptive Analytics
- Create conceptual formulations of linear optimization problems with continuous decision variables. Develop and solve optimization models using graphical methods and Excel’s Solver add-in. Perform sensitivity analysis and make managerial interpretations after obtaining optimal solutions.
- Model the traditional costs of managing inventory decisions under a variety of contexts (perishable food inventories) and its relationship with supply chain management.
Visual Analytics
- Build explanatory visualizations to convey an effective data story. Verbally communicate findings within individual and team-based environments using storytelling techniques.
ASSESSMENT REPORT
Case Study on Predictive Analytics and Data Visualization*. Students will be given a real-world case study that has two challenging components. One will require the use of data visualization and storytelling, and the other predictive analytics.
REAL-WORLD APPLICATIONS IN CLIMATE, FOOD & ENVIRONMENT
- This course starts with an interactive simulation where you will learn about the elements driving climate change and work collectively in teams to understand its complexity and discuss solutions. Through the remainder of the course, we will investigate how data analytic methods (decision analytics, predictive analytics, or prescriptive analytics) can be used to understand the trade-off between economic and environmental decision-making.
- How can analytical models help with the planning of food production in order to reduce food waste?
- What is supply chain sustainability and its interrelationship with the total cost of ownership and risk management?
ASSESSMENT METHODS
| Assessment Type | % |
|---|---|
| Individual Assignments (2) | 10% |
| Team-Based Assignment* | 10% |
| Participation and Team-Based Learning (TBL) Activities* | 5% |
| Midterm** | 35% |
| Final Exam** | 45% |
| Total = | 100% |
* Details to be announced
** 385 and 585 will have different exams