Course:FRE585

From UBC Wiki
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, 5:00 – 6:30pm
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.

BIG QUESTIONS & REAL-WORLD APPLICATIONS IN CLIMATE, FOOD AND THE ENVIRONMENT COVERED IN THE COURSE

  • 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%