Course:FRE585/Syllabus

COURSE INFORMATION

Instructor: Michael Johnson

Contact Info: Email: mjohnson@mail.ubc.ca

Office Hours: 1 hour following class on Thursdays (MCML 352)

Lectures: Tuesdays and Thursdays, 5:00 – 6:30pm in MacLeod 2002

FRE 585 TA: Xiao Han

Email: xiao.han@ubc.ca

Office Hours: TBA

Course Website: 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 OUTCOMES

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

Understand the casual links of climate change and its importance to individual and collective decision-making.
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

In-person instruction except for the possibility of one asynchronous class during Week 7. Please see the schedule posted on page 5.

Learning Management System and Communication

Canvas (http://canvas.ubc.ca) will be used for all course content delivery
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.

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 Weeks 2-3, 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.

ASSESSMENTS


Individual Assignments (2)	Throughout the term	10%
Participation and TBL Activities		5%
Team-based Assignment*		10%
Midterm**		35%
Final Exam**		40%
TOTAL		100%

* Details to be announced

** 385 and 585 will have different exams

Textbook:

No official textbook required. Various chapters of different textbooks will be provided under the “Fair Dealing Exception” (https://copyright.ubc.ca/fair-dealing/)

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 attending class and Office hours. If you have questions regarding content (e.g., problem sets, assignments, exam prep) please use the dedicated Office Hours times (Mike and Xiao). If you have a question regarding course administration, etc. please be sure to direct your question to Mike.

Assignments:

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 exams. 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.

COPYRIGHT

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 HONESTY POLICIES

Plagiarism and Academic Dishonesty

Academic dishonesty and plagiarism are taken very seriously in the MFRE program. All incidences of plagiarism will be escalated to the MFRE Academic Director with penalties ranging from a mark of zero on the assignment, exam or course to being required to withdraw from the program. 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.

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.
Using Generative Artificial Intelligence (AI) tools like ChatGPT, Bard, or other Generative AI models to generate content or conduct analysis for evaluations, without proper citation and or if asked not to use AI, is considered plagiarism and academic misconduct. If students use AI in their submissions, they must cite the AI generator using citations consistent with the UBC Academic Honesty Standards.
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.
Working with Others on an Assignment: You are encouraged to work with other students, but you must turn in your own individual assignment. If you have an answer that is too close to another student’s answer, this will be considered academic dishonesty and this will be handled according to the MFRE and UBC policies.
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 MFRE Code of Conduct; MFRE Turn it in, Course Syllabus, MFRE Instructors; Canvas and UBC academic misconduct policies.

Penalties for Academic Dishonesty: Penalties for academic dishonesty are applied at the discretion of the MFRE program. Incidences of academic misconduct may result in a mark of zero on the assignment, examination, or course, required withdrawal from the program, and/or the matter being is referred to UBC Graduate Studies.

COURSE SCHEDULE

Weeks	Topic	Text Reference
1	Introduction to the course (Model Building). Decision Analytics: Decision criteria; Maximax, Maximin, EMV, EOL, Minimax Regret	Ragsdale: Ch. 15.1 to 15.9
2	Climate Change Workshop. Decision Analytics: EVPI, Decision Trees, Complex Decision Trees.	15.10 to 15.12
3	Decision Analytics: Building Decision Trees in Excel (Treeplan.xla), Sensitivity Analysis (Data Tables). Bayes Theorem and applications.	15.13, 15.14, 15.16 to 15.18.
4	Introduction to Predictive Analytics/Forecasting: Components of time series analysis. Forecasting Game.	Handout
5	Predictive Analytics/Forecasting: SES/MA models for stationary data; error metrics; trend models (SLR, Holts)	Handout
6	Predictive Analytics/Forecasting: Trend and Seasonality; Holt- Winters; Modeling in Excel and R. Makeup Class: Fri Feb. 16th (12:30-2:00pm)	#3 – FX & Options (Apr 8)
	Reading Break: Feb 19-23
7	Tues. Feb. 27 – No class (makeup class on Fri. Feb. 16th). Predictive Analytics/Forecasting: More advanced topics; ARIMA; Team exercise/assignment posted.	Handout
8	Midterm Exam: Thurs. Mar 7th during class (2 HOURS).	Chapt. 1 and 2
9	Prescriptive Analytics (Resource Allocation Models): Introduction, Linear Programming-concepts; formulation of 2-variable problem; graphical solution.	Chapt. 1 and 2
10	Prescriptive Analytics: Complete introductory material (graphical analysis). Special LP conditions. Various applications: transportation network models; food-processing and distribution; team-based exercise on modeling coal resource allocation.	Complete Chapt. 3 and 4.
11	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.	Handout: Inventory Control Models.pdf”
12	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.	Handout
13	Simulation Game: Supply Chain Management Course wrap-up.	Handout
	Final Exam - TBA

MFRE PROGRAM - COURSE PROTOCOL POLICIES

Recordings

There is no required distribution of recordings of class. Recording will be provided based upon on the decision of the course instructor. Classes are designed as and are intended to be in‐person.

Copyright

All materials of this course (course handouts, lecture slides, assessments, course readings, etc.) 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. Further, audio or video recording of classes are not permitted without the prior consent of the instructor.

Missing Classes/Labs

Students are expected to attend all classes, labs, or workshops. If you cannot make it to a class, lab, or workshop due to a medical or personal emergency, please email your instructor, your course assistant, and Olivier Ntwali, MFRE Program Coordinator ahead of time to let them know.

Respectfulness in the Classroom

Students are expected to be respectful of their colleagues at all times, including faculty, staff and peers. This means being attentive and conscious of words and actions and their impact on others, listening to people with an open mind, treating all MFRE community members equally and understanding diversity.

Respect for Equity, Diversity, and Inclusion

The MFRE Program strives to promote an intellectual community that is enhanced by diversity along various dimensions including status as a First Nation, Métis, Inuit, or Indigenous person, race, ethnicity, gender identity, sexual orientation, religion, political beliefs, social class, and/or disability. It is expected that all students and members of our community conduct themselves with empathy and respect for others.

Centre for Accessibility

The Centre for Accessibility (CfA) facilitates disability‐related accommodations and programming initiatives designed to remove barriers for students with disabilities and ongoing medical conditions. If you are registered with the CfA and are eligible for exam accommodations, it is your responsibility to let Olivier Ntwali, Academic Program Coordinator, and each of your Course Instructors know. You should book your exam writing with the CFA using its exam reservation system: for midterm exams or quizzes, at least 7 days in advance; and final exams, 7 days before the start of the formal exam period.