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Quantitative Methods for Business and Resource Management
FRE 585
Instructor: Dr. Michael Johnson
Office Hours: Thursdays 6:30-7:30pm
Class Schedule: Tues and Thurs, 5:00 – 6:30pm
Classroom: MCML 154
Important Course Pages
Lecture Notes
Course Discussion

Course Information

Instructor: Michael Johnson

Contact Info:


Office Hours: Thursdays 6:30-7:30pm

Lectures: Tuesdays and Thursdays, 5:00 – 6:30pm in MCML 154

Course Assistant: Xiao Han

Contact Info:


Office Hours: TBA

Course Websites: Canvas.

Course Overview

This course will provide the necessary foundation and 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, operations analysis and inventory management. Upon completing this course, students will be capable of using a powerful set of functions and tools in Microsoft Excel and R for solving a broad range of analytics problems. We will also continue exploring the use of Tableau Desktop in the application of visual analytics and storytelling.

Learning Objectives

Upon successful completion of the course, students will be able to:

  • Apply analytical techniques to develop business intelligence insights and present them in a compelling way to enable smart and sustainable business decisions.
  • Build and evaluate a decision model in Excel to determine an optimal decision alternative using mathematical expectation, risk, opportunity loss and the value of perfect information. Apply critical thinking and judgement in the context of data and analytic interpretations.
  • Evaluate multi-stage complex decision problems using decision trees. Apply Bayesian analysis to revise uncertainties to make better decisions.
  • Use predictive analytics and forecasting tools on data that exhibits stationary, trend and seasonal characteristics. Evaluate predictions using standard forecasting metrics and validity techniques. Select the appropriate predictive tool for ‘real’ forecasting.
  • Create conceptual formulations of linear optimization problems with continuous decision variables. Develop and solve optimization models using both 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.
  • Communicate findings within individual and team-based environments using visualization and storytelling techniques.  

Course Structure

For 7 weeks of classes, the instructor will deliver classes synchronously online Via Zoom in MCML 154. Lecture attendance (in MCML 154) will be mandatory, and a Course Assistant will attend the lectures in the classroom. During the last 6 weeks, the instructor will be delivering the class in person. Please see the schedule posted on page 5. An updated detailed schedule will be provided through:

To help replicate the classroom experience, make sessions more dynamic and hold each person accountable, students are asked to have their cameras on during live Zoom sessions. Students are expected to conduct themselves professionally by joining sessions on time, muting mics when not speaking, refraining from using any other technology when in-session and participating.

Learning Management System and Communication

  • Canvas ( will be used for all course content delivery.
  • For the first 2 weeks of this course, you are required to use a Zoom account during synchronous classes and office hours. If you do not have a Zoom account, you can create one here: Please use the Zoom link on the front page of Canvas to access Open Labs, Office Hours, Synchronous classes, etc.
  • Canvas Notifications and Student email addresses will be used for communicating information and disseminating class materials. It is your responsibility to check your email and the Canvas course website frequently. 
  • Students are required to have a functioning web camera and microphone while the course is being delivered remotely.

Attendance: Required

The course will be conducted using a Team-Based Learning (TBL) format, to develop both your leadership and team-building skills, while enhancing your learning beyond individual study. During Week 3, you will be asked to participate in a survey so that you can be assigned to a team that you will be working with for the entire semester. Further details will be provided in class. 


Assessment Type %
Individual Assignments (2) 10%
Team-Based Assignment* 18%
Participation and Team-Based Learning (TBL) Activities* 2% 
Midterm** 35%
Final Exam** 35%
Total = 100%

* Details to be announced

**385 and 585 will have different exams

Note: This grading scheme assumes that examinations will take place in person. If this course changes to remote delivery, the grading scheme will likely be modified, and notification will be provided before any examination.


No official textbook required. Various chapters of different textbooks will be provided under the “Fair Dealing Exception” (

Required Calculator

Any scientific calculator that can perform 2-variable statistics. The Sharp EL 531 will be used during lecture to demonstrate simple linear regression. Programmable calculators are not allowed during examinations.  

Communication and Extra Help

The best way to get extra help in this course is through Open Labs or Office hours. If you have questions regarding content (e.g., problem sets, assignments, exam prep) please use the dedicated Open Labs and Office Hours times. If you have a question regarding course administration, etc. please be sure to direct your question to Mike.


Late submissions will be accepted up to 24 hours late but will be heavily penalized. Any assignment submitted beyond that point will not be graded. Assignments must be done on an individual basis unless otherwise specified by the instructor. Discussion and collaboration among students is strongly encouraged, but on individual assignments, each student must build his or her own computer file and submit his or her own original work. Identical submissions are a form of academic dishonesty and will immediately receive a mark of zero and possibly infringe upon your academic record.

Your assignments should be presented with the same quality as you would a piece of business correspondence to your customer or your boss. The neatness and quality of your submission with contribute to your marks.

Makeup Exams

There will be NO makeup tests, exams or quizzes. If you miss an exam, you will receive zero marks. Exceptions may be considered for documented medical reasons from UBC’s Health Services or extenuating circumstances. In such a case, it is the responsibility of the student to inform the instructor immediately (not after the exam has taken place). Notification after the examination date is not acceptable and will result with a grade of zero.

All exams are “closed-book”. That is, you will NOT be allowed to use your textbook or notes. Formulas will be provided on the front page of the exam.

UBC STUDENT PHOTO ID is required in order to write any exam. Please bring your UBC student card and one other piece of photo ID to all exams.


All learning materials of this course are the intellectual property of the instructor or licensed to be used in this course by the copyright owner. Redistribution of these materials by any means without permission of the copyright holder(s) constitutes a breach of copyright and may lead to academic discipline and could be subject to legal action. Audio or video recording of classes are not permitted without the prior approval of the Instructor. Any lecture video and recordings are for the sole use of the instructor and for students currently enrolled in this class. In no case may the lecture video or recording (or other learning materials), or part of the recording be used by students or any other person for any other purpose, either personal or commercial. Further, audio or video recording of classes are not permitted without the prior consent of the instructor.

I am the exclusive owner of copyright for materials (lecture videos, course handouts, lecture slides, assessments, etc.) in this class. You may take notes and make copies of course materials for your own use. You may not and may not allow others to reproduce or distribute (or upload) lecture notes and course materials publicly whether or not a fee is charged without my express written consent. Similarly, you own copyright in your original papers and essays. If I am interested in posting your answers or papers on the course web site, I will ask for your written permission.

Academic Misconduct

Academic dishonesty and plagiarism are taken very seriously in the MFRE program and can result in a range of punitive measures, which could include failing the program. It is each student’s responsibility to review and understand what constitutes academic dishonesty and plagiarism and how to avoid them.

Academic honesty is essential to the continued functioning of UBC as an institution of higher learning and research. All UBC students are expected to behave as honest and responsible members of an academic community. Breach of those expectations or failure to follow the appropriate policies, principles, rules, and guidelines of the University with respect to academic honesty may result in disciplinary action.

Academic misconduct that is subject to disciplinary measures includes, but is not limited, to the following:

  • Plagiarism, which is intellectual theft, occurs where an individual submits or presents the oral or written work of another person as his or her own. In many UBC courses, you will be required to submit material in electronic form. The electronic material will be submitted to a service which UBC subscribes, called TurnItIn. This service checks textual material for originality. It is increasingly used in North American universities. For more information, review TurnItIn website online.
  • Cheating, which may include, but is not limited to falsification of any material subject to academic evaluation, unauthorized collaborative work; or use of unauthorized means to complete an examination.
  • Submitting others work as your own, may include but not limited to i. using, or attempting to use, another student’s answers; ii. providing answers to other students; iii.  failing to take reasonable measures to protect answers from use by other students; or iv. in the case of students who study together, submitting identical or virtually identical assignments for evaluation unless permitted by the course instructor.
  • Resubmission of Material, submitting the same, or substantially the same, essay, presentation, or assignment more than once (whether the earlier submission was at this or another institution) unless prior approval has been obtained from the instructor(s) to whom the assignment is to be submitted.
  • Use of academic ghostwriting services, including hiring of writing or research services and submitting papers or assignments as his or her own.

Student Responsibility: Students are responsible for informing themselves of the guidelines of acceptable and non-acceptable conduct for examinations and graded assignments as presented via FRE code of conduct guidelines; course syllabus and instructors; and UBC academic misconduct policies, Review the following web sites for details:

  • UBC Academic Misconduct and Discipline (,54,111,0)
  • UBC Learning Commons web-based Academic Integrity (

Penalties for Academic Dishonesty: The integrity of academic work depends on the honesty of all those who work in this environment and the observance of accepted conventions. Academic misconduct is treated as a serious offence at UBC and within the MFRE program. Penalties for academic dishonesty are applied at the discretion of the course instructor. Incidences of academic misconduct may result in a reduction of grade or a mark of zero on the assignment or examination with more serious consequences being applied if the matter is referred to the Dean’s office and/or President’s Advisory Committee on Student Discipline. Note: If a student needs to extend his/her program due to a failed course or unsatisfactory progress, they will have to pay the full MFRE tuition fees for that term/s.

Tentative Schedule

Topics (Tentative Schedule ONLY): **** please note: the schedule/content may be slightly modified based on discretion of instructor.

OL: Instructor delivers class synchronously online Via Zoom in MCML 154

IP: Instructor delivers class in-person in MCML 154

Date Topic Text Reference
Week 1

Jan 9


Introduction to the course (Model Building).  Decision Analysis:  RAT exercise;

Decision criteria; Maximax, Maximin, EMV, EOL, Minimax Regret

Ragsdale: Ch. 15:

sections 15.1 to 15.9

Week 2

Jan 16


Decision Analysis: EVPI, Decision Trees, Complex Decision Trees, Building Decision

Trees in Excel (Treeplan.xla), Sensitivity Analysis (Data Tables)

15.10 to 15.12
Week 3 

Jan 23


Decision Analysis:  Bayes Theorem and applications, Multi-criteria decision making,

Analytic Hierarchy Process, Monte Carlo Simulation

15.13, 15.14,

15.16 to 15.18

Week 4 

Jan 30


Introduction to Predictive Analytics/Forecasting: components of time series.

Forecasting Game. 

Week 5 

Feb 6


Predictive Analytics/Forecasting: SES/MA models for stationary

data; error metrics; trend models (SLR, Holts)

Week 6 

Feb 13


Predictive Analytics/Forecasting: Trend and Seasonality; Holt-

Winters; Modeling in Excel and R.

Reading Break: Feb 20-24
Week 7

Feb 27


Prescriptive Analytics (Resource Allocation Models): More advanced topics; ARIMA;

Team exercise/assignment on posted case study.

Week 8

Mar 6

No classes (work on team assignment and prepare for midterm).

Office Hours continue.


Chapt. 1 and 2

Week 9

Mar 13


Extra Class 1: Midterm Exam: Date - TBA

Tuesday: Start of Prescriptive Analytics.

Thursday: Prescriptive Analytics (Resource Allocation Models):

Introduction, Linear Programming-concepts; formulation of 2-variable

problem; graphical solution.


Chapt. 1 and 2

Week 10 

Mar 20


Extra Class 2: Prescriptive Analytics

Prescriptive Analytics: Complete introductory material (graphical

analysis). Special LP conditions; further formulation applications.

Various applications: transportation network models; food-processing

and distribution; team-based exercise on modeling coal resource


Ragsdale: Complete

Chapt. 3 and 4

Week 11

Mar 27


Prescriptive Analytics: Modeling using Excel; formulation of multivariable

LP application. Computer solutions. Interpretation of Business

results from computer output (sensitivity analysis and its relationship to

graphical solutions). Linear Programming extensions: Assignment,

network, integer models and nonlinear programming.





Week 12

Apr 3


Inventory and Supply Chain Management: Basic inventory models:

EOQ, trade-offs between costs; reorder points; quantity discount

models. Inventory and Probabilistic models; reorder point with

probabilistic demand, Newsvendor model.

Week 12

Apr 10

Simulation Game: Supply Chain Management. Course wrap up. Handout
Exam - TBA