MGMT20005 Business Decision Analysis
Lecture 1 – Introduction and Subject Overview
Dr. Zahra HosseiniFard
Senior lecturer of Operations Management
Department of Management and Marketing
Faculty of Business and Economics
zahra.h@unimelb.edu.au
Overview of This Lecture
2
Introduction to this subject
Introduction to Decision Analysis
Decision making without probabilities
Lecturer and Coordinator
 Dr. Zahra HosseiniFard, Email: Zahra.h@unimelb.edu.au
Head tutor:
 Jackson Yuen, Email: jctyuen@unimelb.edu.au
Consultation with your tutor:
 Before/after tutorials – by email appointment
Teaching Team
3
 “A practical subject, focuses more on practice than theory”
 “I enjoyed learning about aspects and problems that we can see and apply to
situations we may experience and see happening in the world. This was in particular
with the linear programming with learning about how it can be used to solve real
problems”
 “I liked that almost everything we learnt has practical applications, such as LP and
decision trees.”
 This is a practice-oriented subject.
Feedback from Previous Students
4
Why BDA?
 Hone your analytical skills
 Improve your problem solving capabilities
 Make better decisions
 …
Importance of This Subject
Data Information Insights Decisions
5
BDA STARTS AFTER UNCOVERING INSIGHTS IN DATA…
You need to ACT on the insights and make some DECISIONS that ADD-VALUE to the bottom line!
Analytics is everywhere. It is no longer a nice-to-have.
 Marketing, finance, operations, health care, sports et al.
 We will draw heavily on examples from different disciplines including marketing, accounting,
operations and finance.
Importance of This Subject
6
Three Types of Analytics
7
Deterministic models: All relevant data are known with certainty. The decision maker needs
to identify the optimal decision within the constraints. Techniques include:
• Linear Programming (LP)
• Integer Linear Programming (ILP)
• Non-Linear Programming (NLP)
Stochastic models: Some data are uncertain. The probability concept is used. Techniques
include:
• Decision Tree Analysis and Utility Model
• Simulation-based Optimisation
• Scenario Analysis and Robust Optimisation
• Stochastic Dynamic Programming
• Stochastic Linear Programming
• Stochastic Integer Programming
• Markov Chain Process
• Queuing Models
Types of Decision Models
8
Introduction to BDA and Decision AnalysisWeek 1
Decision Making under RiskWeek 2
Value of InformationWeek 3
Multi-Criteria Decision MakingWeek 4
Deterministic Optimisation
Introduction to Linear Programming (LP)Week 5
Sensitivity Analysis in LPWeek 6
LP ApplicationsWeek 7
Network ModelsWeek 8
IP ApplicationsWeek 9
Monte Carlo SimulationWeek 10
Simulation-based OptimisationWeek 11
Simulation Modelling
Review and RevisionWeek 12
Decision Analysis
9
Subject Contents
Anderson, D.R., Sweeney, D.J., Williams, T.A., Camm, J.D., & Martin, K. (2019), An
Introduction to Management Science: Quantitative Approaches to Decision Making, 16th
edition, South-Western Cengage Learning
• You can borrow the ebook/hard copy from the library.
• You can purchase the hard copies in Co-op or from Cengage website . The
publisher also offers e-Books and e-Chapters with cheaper prices.
- Discount code: WOW10
- Previous versions also work.
10
Prescribed Textbooks
 Excel Solver: Spreadsheets are the tool of choice for today’s managers.
 Other (optional):
• SAS/OR
• LINDO
• MATLAB optimization package
• R with Gurobi package
• Gurobi
• CPLEX
• Arena Simulation Software
Software
11
 Learning materials:
• Pre-lecture study
• Lecture slides
• Text book
• Tutorial questions
 Class and tutorials
• Lecture (1 hour theory + 1 hour case study or
application/exercises)
• Tutorial session (1 hour) workshop with Excel
 Post-lecture, summary and quiz
 Peer learning
• Perusall
 Assessment
Pre-
Reading
Learning
material
CLASS
Peer
learning
Assessment
Study/Learning Format
12
 A Quantitative Subject
 Read, think, practice
 Class exercises
 Rule of thumb + Analytical mindset!!
 Assessments
 Assignment 1 (25%): An individual assignment on decision analysis
 Assignment 2 (25%): A 2500-word group assignment on optimization
 Exam (50%): An open book final examination , NO hurdle requirement
Studying for MGMT20005
13
The assignment will involve using the decision analysis models discussed in lectures to
solve decision-making problems that arise in the business world.
The assignments will involve analysis of model using computer tools, as well as drawing
on more theoretical materials from lectures.
 Materials from weeks 1-4
 Submission: Canvas assignment
 Time limit: Once you start the Assignment, you will have 140 minutes to complete and submit
it. However, you can start working on this assignment any time during the time window (e.g.,
Monday to Friday)
 Due date: Week 5, Friday 6 pm
Individual Assignment (25%)
14
The group assignment is designed to allow you to demonstrate that you can effectively formulate and
analyse business decision problems, apply mathematical modelling approaches such as LP or IP to
solve the problems.
You must master Excel Solver to obtain an optimal solution, generate a sensitivity analysis report, and
suggest courses of action for management.
You can choose the group to join (from the same tutor/tutorial group). Contribution of each team
member required to be clearly attributed.
 Materials from weeks 5-10
 Group Assignment: Form a team of up to 3 students by yourself
 Word limit: 2500
 Due date: Week 11, Friday 6pm
Group Assignment (25%)
15
 This is a two-hour open book exam covering all topics taught in the lectures/tutorials.
• You can bring 2 double-sided A4 pages (handwritten or printed) to the exam venue.
 It consists of questions like the worked examples in lectures and tutorials.
 Scientific calculators are allowed.
 During the Examination Period.
End-of-Semester Exam (50%)