Course:FRE385
| Quantitative Methods for Business and Resource Management | |
|---|---|
 | |
| FRE 385/585 | |
| Section: | |
| Instructor: | TBA |
| Email: | |
| Office: | MCML 321 |
| Office Hours: | Thurs following class for one hour |
| Class Schedule: | Lecture: Tuesdays and Thursdays, 5:00 – 6:30 pm,
Lab: Tuesdays and Thursdays 4:00 – 5:00 pm |
| Classroom: | Lecture: FNH 60
Lab: MCML 194 |
| Important Course Pages | |
| Syllabus | |
| Lecture Notes | |
| Assignments | |
| Course Discussion | |
Course Overview
This course will provide the necessary foundation and experience for students to apply a variety of modeling and quantitative 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), an introduction to forecasting and predictive analytics, data mining, 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 for solving a broad range of quantitative problems. The instructor will also provide some instruction for the use of Tableau – software for visual analytics for exploratory data analysis.
Lectures: Tuesdays and Thursdays, 5:00 – 6:30pm, FNH 60
Labs: Tuesdays and Thursdays 4:00 – 5:00pm Room 194 MacMillan
Labs are completely optional and are available to provide extra help in this course. The computer lab will be attended by our teaching assistant for this course.
Teaching Assistants: Xiao Han
Course Websites: Canvas
Textbook
Spreadsheet Modeling and Decision Analysis; Author: Cliff Ragsdale.
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.
Attendance: Required
Attendance is mandatory at ALL classes. 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. Missing class will be detrimental to your grade.
Grading
| Individual Assignments (2) | 10% |
| TBL: Individual and Team Tests
TBL: Weekly Team Activities |
5% |
| Team-based Assignment* | 10% |
| Midterm** | 35% |
| Final Exam** | 40% |
| Total = | 100% |
*Details to be announced
** 385 and 585 will have different exams
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. All assignments must be submitted using the posted title page that can be downloaded from Connect in the “Assignment” folder.
Makeup Exams or Tests
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 by phone only (not after the exam has taken place). Notification after the examination date is not acceptable and will result with a grade of zero.
All TBL tests (both Individual and team-based tests) and Midterm and Final 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.
Topics
(Tentative Schedule ONLY): **** please note: the schedule/content may be slightly modified based on discretion of instructor.
| Date | Topic | Text Reference |
|---|---|---|
| Week 1
Jan 6 |
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 13 |
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 20 |
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 27 |
Introduction to Predictive Analytics/Forecasting: SES/MA models for stationary data; components of time series. | Handout |
| Week 5
Feb 3 |
Predictive Analytics/Forecasting: An introduction to trend models; SLR; Holts; error metrics. Modeling in Excel. | Handout |
| Week 6
Feb 10 |
Predictive Analytics/Forecasting: Team exercise/assignment on posted case study. | |
| Feb 17 | Reading Break: Feb 17-22 | |
| Week 8
Feb 24 |
Prescriptive Analytics (Resource Allocation Models): Introduction, Linear Programming-concepts; formulation of 2-variable problem; graphical solution.
Midterm Exam (2 Hours): Tuesday Feb 25th (5:00 to 7:00pm) |
Ragsdale: Chapt.
1 and 2 |
| Week 9
Mar 2 |
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 allocation. | Ragsdale: Chapt.
1 and 2 |
| Week 10
Mar 9 |
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. | Ragsdale:
Complete Chapt. 3 and 4 |
| Week 11
Mar 16 |
Inventory and Supply Chain Management: Basic inventory models: EOQ, trade-offs between costs; reorder points; quantity discount models. | Handout:
Inventory Control Models.pdf |
| Week 12
Mar 23 |
Simulation Game: Supply Chain Management
Inventory and Probabilistic models; reorder point with probabilistic demand, Newsvendor model. |
Handout |
| Week 13
Mar 30 |
Simulation Game: Debrief. |