Course:FRE585
| FRE 585: Quantitative Methods for Business and Resource Management | |
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| FRE 585 | |
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| Instructor: | Johnson Mike |
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| Office Hours: | TBA |
| Class Schedule: | Tues and Thurs, 4:00 – 5:30pm |
| Classroom: | MCML 154 |
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| Syllabus | |
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COURSE DESCRIPTION
This course builds the foundation for applying quantitative modelling and analytical techniques to real business and resource management challenges. Students learn how to use data-driven decision tools to improve operations, allocate resources, and support strategic planning across food, environmental, and resource-based sectors.
Topics include decision analysis, resource allocation models, linear programming and optimization (for scheduling, production, and resource use), forecasting and predictive analytics, simulation modelling, sustainable operations, and supply chain analysis and 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
- How can data analytic methods (decision analytics, predictive analytics, or prescriptive analytics) be used to understand the trade-off between economic and environmental decision-making?
- How do we translate a real business or resource-management problem into a decision model, evaluate alternatives, and communicate the results so leaders can confidently act on the recommendations?
- How can analytical models help with the planning of food production in order to reduce food waste?
- How can organizations use data-driven models to make better decisions about resource use, risk, sustainability, and operational performance?
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