|Quantitative Methods for Business and Resource Management|
|Instructor:||Dr. Michael Johnson|
|Office Hours:||TBA, TA: Juan Fercovic (email@example.com) and TBA|
|Class Schedule:||Tuesdays and Thursdays, 5:00 – 6:30pm|
|Classroom:||Orchard Commons 3047|
|Important Course Pages|
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 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. Student will also be introduced to a Visual Analytics tool called Tableau and will have several assignments that will utilize this tool.
Teaching Assistants: Juan Fercovic (firstname.lastname@example.org) and tba
Course Websites: Canvas
Thursdays 4:00 – 5:00pm Room 192 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.
TBA but most likely: Spreadsheet Modeling and Decision Analysis 7th edition; ISBN: 9780176673953 Author: Cliff Ragsdale, 2015.
Required Calculator: Any scientific calculator. Programmable calculators are not allowed during examinations.
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.
|FRE 385||FRE 585|
|Individual Assignments (3)||14%||14%|
|TBL: Individual and Team Tests
TBL: Weekly Team Activities
* Details to be announced (Case Study – Due at end of course)
Late submissions will be accepted up to 24 hours late but will be penalized 50%. 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): **** please note: subject to change at the discretion of instructor!
|Week 1/2 Jan 8||Introduction to the course (Model Building). Resource Allocation Models: Introduction, Linear Programming-concepts; formulation of 2-variable problem; graphical solution.||Ragsdale: Chapt. 1 and 2|
|Week 3 Jan 15||Resource Allocation Models: RAT exercise; Special LP conditions; further formulation applications. Various applications: transportation network models; food-processing and distribution; team-based exercise on modeling coal resource allocation.||Ragsdale: Complete Chapt. 2.|
|Week 4 Jan 22||Optimization Techniques: Modeling using Excel; formulation of multi-variable 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.||Ragsdale: Chapt. 3 and 4|
|Week 5 Jan 29||Introduction to Predictive Analytics/Forecasting: SES/MA models for stationary data; An introduction to trend models; components of time series.||Ragsdale: Chapt. 4|
|Week 6 Feb 5||Data Mining: Data mining using classification trees (Knime).||Handout|
|Week 7 Feb 12||Midterm Exam (2 hours): TBA|
|Feb 19||Reading Break: Feb 19-23|
|Week 8 Feb 26||Decision Analysis: RAT exercise; Decision criteria; Maximax, Maximin, EMV, EOL, Minimax Regret, EVPI, Decision Trees.||Ragsdale: Ch. 14: sections 14.1 to 14.9|
|Week 9 Mar 5||Decision Analysis: Complex Decision Trees, Building Decision Trees in Excel (Treeplan.xla), Sensitivity Analysis (Data Tables)||14.10 to 14.12|
|Week 10 Mar 12||Decision Analysis: Bayes Theorem and applications, Multi-criteria decision making, Analytic Hierarchy Process, Monte Carlo Simulation||14.13, 14.14, 14.16 to 14.18. Chapt 12|
|Week 11 Mar 19||Inventory and Supply Chain Management: Basic inventory models: EOQ, trade-offs between costs; reorder points; quantity discount models.||Handout: “Chapter 13 - Inventory Control Models.pdf”|
|Week 12 Mar 26||Inventory and Probabilistic models; reorder point with probabilistic demand, Newsvendor model. Simulation Game: Supply Chain Management||Handout|
|Week 13 April 2||Case Study - Poster Presentations (Graduate Students Only)|