MGMT90280: pass the exams, not just read the notes
Your complete guide to University of Melbourne's managerial decision analytics unit. See where the marks are, work real practice questions, and study with an AI tutor that knows MGMT90280.
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Worked example
A workshop makes two products, S (standard) and D (deluxe), with profit contribution $10 per S and $9 per D. Two resources bind: a cutting stage needs 1 hour per S and 2 hours per D with 40 hours available (1S + 2D <= 40), and a finishing stage needs 3 hours per S and 2 hours per D with 60 hours available (3S + 2D <= 60), with S, D >= 0. Formulating this as a linear program and solving it (as you would with Excel Solver), what is the maximum total profit contribution?
The optimum of a linear program lies at a corner (extreme point) of the feasible region, so evaluate the objective at each corner.
List the feasible corners and their profit 10S + 9D: (0, 0) gives 0; (20, 0) gives 200 (binding on finishing); (0, 20) gives 180 (binding on cutting); (10, 15) gives 100 + 135 = 235.
The largest value is at (S, D) = (10, 15), so the maximum total profit contribution is $235 (option index 1).
The trap: Stopping at a single-constraint corner. Producing only the high-margin product S up to the finishing limit gives (20, 0) and $200, and producing only D up to the cutting limit gives (0, 20) and $180. The mix where both constraints bind, (10, 15), beats both: that is exactly why you solve the LP rather than greedily maximising one product. classic slip!
One exam decides 50% of your grade. Students must be available for the entire examination period; supplementary exams require an approved Special Consideration application. This whole page is built around that.
Overview
What MGMT90280 is, and where it sits
MGMT90280 Managerial Decision Analytics is the University of Melbourne's postgraduate business-analytics subject, run by the Department of Management and Marketing in the Faculty of Business and Economics. It builds one decision-analytics toolkit across the three layers of analytics: prescriptive (linear, integer and nonlinear optimisation, and Monte Carlo simulation), descriptive (probability distributions and descriptive data mining such as clustering and association rules), and predictive (regression modelling, time series analysis and forecasting, and classification trees). The framing is managerial throughout, applying each technique to business problems in international business, marketing, supply chain and strategy.
The subject is taught in weekly 3-hour seminars (in person, also recorded) and is built around Excel-based tooling: the Excel Solver Add-in, the Excel Analysis ToolPak, and Analytic Solver Basic for Excel. Students are expected to review the seminar slides and attempt the in-slide questions before each seminar so that class time is spent solving practice problems. The prescribed text is Camm, Cochran, Fry and Ohlmann, Business Analytics, 5th edition (Cengage), with earlier editions acceptable but chapter mappings varying by week.
Assessment is split between technical skill and applied judgement: a 20% timed individual quiz (sat in Respondus LockDown Browser, semi-open-book), a 25% group Assignment 2 with a 5% group presentation, and a 50% individual 2-hour exam. The group project asks teams to take a real organisation's business problem and apply descriptive, predictive and prescriptive techniques end to end, so the subject rewards both the ability to run the models and the ability to frame a problem and interpret results for a non-expert manager.
Official outline: handbook.unimelb.edu.au · MGMT90280 outline. Always treat the official outline and the exam timetable as authoritative.
Difficulty & time commitment
Is MGMT90280 hard, and how much time does it take?
MGMT90280 is manageable if you keep a weekly rhythm and treat the back half as the main event. Across student reviews the pattern is consistent: it starts gently and steepens, and the heaviest assessment is the part that separates grades.
The difficulty curve and the assessment weighting point the same way: the back half is harder and worth more. Front-loading effort there is the highest-return decision in the unit.
Is this unit for you
Who tends to do well, and who tends to struggle
You will likely do well if
- You are comfortable building and reading spreadsheet models and will put in the hours to get fluent with the Excel Solver Add-in, the Analysis ToolPak and Analytic Solver Basic.
- You keep up weekly: review the seminar slides and attempt the in-slide questions before each 3-hour seminar so class time reinforces rather than introduces the technique.
- You can connect a technique to a managerial decision, not just run it, because Assignment 2 and the exam reward interpreting results for a non-expert and recommending an action.
- You start the group project early, divide the descriptive, predictive and prescriptive analysis across the team and leave time to integrate and rehearse the presentation.
You may struggle if
- You let the breadth pile up and try to learn optimisation, probability, simulation, regression, forecasting and data mining in the final weeks before the 50% exam.
- You treat the subject as theory only and never get hands-on with Solver and the Analysis ToolPak, so the applied quiz and group implementation catch you out.
- You can compute a result but cannot interpret it (read a Solver sensitivity report, explain an R-squared or a lift value, or justify a forecast) for the managerial marks.
- You leave the group report and presentation late, since there are no extensions (only Special Consideration) and the report and live presentation together are 30% of the grade.
- Build a one-page map of when to use each technique (optimise with LP/ILP/NLP, quantify uncertainty with probability and Monte Carlo, find patterns with clustering and association rules, predict with regression, forecasting and classification trees) so you pick the right tool under exam pressure.
- Rehearse the full Solver workflow until it is automatic: define decision variables, set the objective, add constraints, choose the Simplex LP or GRG Nonlinear engine, and read the answer and sensitivity reports.
- Work the real past papers timed, writing both the model and the managerial interpretation, because the exam uses a few multi-part scenario questions rather than many short ones.
- On Assignment 2, make the link between techniques explicit (how the descriptive, predictive and prescriptive steps feed each other), reference in APA, and rehearse a tight 10-minute presentation with a short live implementation demo.
Syllabus
The 12 topics, week by week
The exam-weight marker on each topic shows where the marks concentrate. The amber topics carry the highest exam weight.
T1 · Introduction to MDA and Linear Programming
Camm et al. Ch 14The three layers of analytics (descriptive, predictive, prescriptive); formulating a linear program (decision variables, objective function, constraints) and solving it with the Excel Solver Add-in; sensitivity analysis.
T2 · Integer Linear Programming
Camm et al. Ch 14 & 15Adding integer and binary decision variables to LP, modelling all-or-nothing and either-or decisions, and why the LP relaxation gives a bound on the integer optimum.
T3 · Nonlinear Programming
Camm et al. Ch 16Optimisation with nonlinear objectives or constraints, local versus global optima, and solving with the GRG Nonlinear engine in Solver.
T4 · Probability Distributions
Camm et al. Ch 5Discrete and continuous distributions, expected value and variance, and the binomial, Poisson and normal distributions as inputs to managerial models. Assignment 1 due in this block.
T5 · Monte Carlo Simulation
Camm et al. Ch 13Modelling uncertainty by sampling from input distributions, running many trials in a spreadsheet, and reading the output distribution to support risk-aware decisions.
T6 · Descriptive Data Mining
Camm et al. Ch 6Unsupervised methods: hierarchical and k-means clustering, similarity and distance measures, and association rules (support, confidence and lift) for market-basket analysis.
T7 · Regression Modelling
Camm et al. Ch 8Simple and multiple linear regression, interpreting coefficients, R-squared and significance, dummy (indicator) variables, and using regression output for prediction.
T8 · Time Series Analysis and Forecasting
Camm et al. Ch 9Trend, seasonality and cyclical patterns, moving averages and exponential smoothing, and evaluating forecast accuracy with measures such as MAE and MSE.
T9 · Predictive Data Mining: Classification Trees
Camm et al. Ch 11Supervised classification and regression trees, splitting rules, overfitting and pruning, and evaluating a classifier (accuracy, the confusion matrix and logistic regression as a comparator).
T10 · Practice full session
All chaptersA full practice session integrating the prescriptive, descriptive and predictive techniques on combined business problems in preparation for the exam.
T11 · Exam review (Assignment 2 due)
All chaptersExam review across the full toolkit; the group Assignment 2 report is due in this week.
T12 · Oral presentations
Assignment 2Groups present their Assignment 2 analysis live in the final seminar, introducing the organisation and business problem, the techniques used and a brief implementation demo, and the managerial contributions.
How it's assessed
Assessment structure
| Component | Weight | Format & timing |
|---|---|---|
| Assignment 1 (individual timed quiz) | 20% | A timed quiz sat in Respondus LockDown Browser, semi-open-book: only hard copies of subject materials posted on the LMS (including the textbook and seminar slides) are permitted, plus a calculator. Internet searches and generative AI tools are strictly prohibited. Around Week 4 (due dates subject to change; confirm on the LMS). Covers the prescriptive and probability material from the first weeks; a laptop is strongly recommended over a tablet. |
| Assignment 2 (group report) | 25% | A group report taking a real organisation's business problem and applying descriptive, predictive and prescriptive techniques (formulated and implemented with Excel Solver, the Analysis ToolPak and/or Analytic Solver Basic), with a literature review, methodology, results, recommendations and a critical evaluation. APA referencing. Due Week 11 (subject to change). No extensions are permitted; apply for Special Consideration if eligible. Word-limit overruns attract marking penalties. |
| Class presentation of Assignment 2 | 5% | A live group oral presentation (about 10 minutes, plus or minus 1 minute) in the final seminar, introducing the organisation and business problem, summarising the literature review, presenting the analytics approaches with a brief implementation demo, and discussing the managerial contributions. No file submission required. Final seminar, Week 12. All group members receive the same mark unless a non-contribution issue is formally raised before the Assignment 2 due date. |
| Final exam | 50% | A 2-hour individual exam under closed conditions, covering the full subject toolkit (optimisation, probability, simulation, data mining, regression and forecasting). The only approved calculator is the Casio FX82 (any suffix). Formal examination period. Students must be available for the entire examination period; supplementary exams require an approved Special Consideration application. |
- Pass on a weighted average of at least 50% across the four components. No single-component hurdle is stated in the subject materials reviewed; confirm against the current handbook and LMS.
- Based on past papers, the exam presents a small number of multi-part, scenario-driven questions (for example, formulate and interpret an optimisation model from a production-planning case, read a Solver sensitivity report, or interpret regression and forecasting output) rather than many short questions, so showing the full working and the managerial interpretation matters.
- Calculator policy: Exam: the only approved calculator is the Casio FX82 (any suffix); no equivalent models are permitted. A calculator is also allowed in the Assignment 1 timed quiz.
This is an exam-cram unit. With the exams at 50% of the grade and the final exam alone at 50%, your result is overwhelmingly decided by how well you perform under time pressure. Students must be available for the entire examination period; supplementary exams require an approved Special Consideration application.
Final exam timing: approx mid-November 2026 (S2 offering, confirm against the official exam timetable). Confirm the exact date and venue on the official exam timetable.
How to actually pass it
A weekly rhythm, two checklists, and the traps to avoid
The unit rewards consistency over cramming, and practice over re-reading. Here is the loop that works, then what to have nailed before each exam.
The weekly loop
Before the mid-semester checklist
- Get fluent with the Excel Solver Add-in and the Analysis ToolPak early, before they are needed under quiz time pressure.
- Drill the prescriptive block (formulate and solve linear, integer and nonlinear programs; read a sensitivity report) and the probability and Monte Carlo material that the individual quiz covers.
- Practise in Respondus LockDown Browser using the provided sample quiz so the lockdown environment and the semi-open-book rules are not a surprise.
- Prepare your permitted printed materials (textbook and seminar slides) and a Casio FX82 calculator for the semi-open-book quiz.
Before the final heaviest topics
- Work all the available past exams timed, writing both the full model and the managerial interpretation, since the exam uses a few multi-part scenario questions.
- Be able to formulate an optimisation model from a worded case and read a Solver sensitivity report (shadow prices and allowable ranges).
- Rehearse interpreting regression and forecasting output: coefficients, R-squared, significance, dummy variables, and forecast-accuracy measures.
- Review the data-mining methods: clustering and association rules (support, confidence, lift) and classification trees with the confusion matrix.
- Finish and rehearse Assignment 2 and the presentation in good time, because there are no extensions and the report and presentation are 30% of the grade.
The mistakes that cost marks
Greedily maximising one product instead of solving the LP. In an optimisation problem the best mix often sits where two constraints bind, not at a single-constraint corner. Producing only the high-margin product can leave money on the table. Formulate the model and let Solver find the corner of the feasible region, then check it against the constraints.
Learning theory but never touching the Excel tools. Solver, the Analysis ToolPak and Analytic Solver Basic are central to the quiz, the group project and the applied exam questions. Reading about a technique is not the same as being able to set it up and read its output in a spreadsheet under time pressure.
Computing a result without interpreting it. Marks come from the managerial reading: what a shadow price, an R-squared, a lift value or a forecast error means for the decision. A correct number with no interpretation loses the points the subject is designed to award.
Underestimating the group project and presentation. Assignment 2 plus its presentation are 30% of the grade, there are no extensions, and the work spans all three analytics layers end to end. Starting late, splitting the work poorly or under-rehearsing the 10-minute presentation is a common avoidable loss.
Teaching team
Who teaches MGMT90280
The bios below are factual. The star ratings are not ours: they are impressions from students who have taken the unit, so you can hear from people who sat in the lectures.
Dr. Medo Pournader
Senior Lecturer and Program Director of the Master of Supply Chain Management in the Department of Management and Marketing, University of Melbourne, with research at the intersection of behavioural operations, supply chain, risk, sustainability and technology management. Staff profile
Jackson Yuen
Teaches in the Department of Management and Marketing across undergraduate and postgraduate programs, mainly Business Analysis and Decision Making and Managerial Decision Analytics.
Misagh Alidost
Teaches in the Department of Management and Marketing since 2018, including Managing Processes and Projects, Managing Supply Chain Networks, Business Analysis and Decision Making, and Managerial Decision Analytics.
Teaching team as listed in the unit materials reviewed. AskSia does not rate lecturers; star ratings are submitted by students who have taken MGMT90280.
Where it fits
Prerequisites, related units & why it matters
A postgraduate (Level 9) Faculty of Business and Economics subject worth 12.5 credit points, typically taken within a master's program. Check the handbook for the current prerequisite, corequisite and enrolment rules for your course.
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FAQ
Frequently asked questions
Is MGMT90280 hard?
It is moderate-to-hard for a master-level subject. The challenge is breadth rather than depth: you move quickly across optimisation, probability, simulation, regression, forecasting and data mining, all implemented in Excel tools (Solver, the Analysis ToolPak and Analytic Solver Basic). With consistent weekly practice on the in-seminar questions and the spreadsheet workflow it is very manageable, especially if you are comfortable with basic statistics.
How is MGMT90280 assessed?
There are four components: a 20% individual timed quiz (Assignment 1, sat in Respondus LockDown Browser and semi-open-book), a 25% group report (Assignment 2), a 5% group presentation of that report, and a 50% individual 2-hour exam. You pass on a weighted average of at least 50%, with no single-component hurdle stated in the materials reviewed.
What software do I need?
Microsoft Excel with the Solver Add-in and the Analysis ToolPak, plus Analytic Solver Basic for Excel. The seminars and Assignment 2 are built around these tools, so the practical skill of setting up and reading a Solver model or a regression and forecasting output matters as much as the theory.
What is the Assignment 1 quiz like?
It is a timed quiz taken in Respondus LockDown Browser, which locks down your laptop so you cannot open other applications or browse the internet. It is semi-open-book: you may use printed hard copies of the LMS materials (including the textbook and seminar slides) and a calculator, but internet searches and generative AI tools are strictly prohibited. A practice sample quiz is provided so you know the format.
What is the group assignment about?
Assignment 2 is a group consulting-style report: you pick a real organisation and business problem, do a short literature review, then apply descriptive, predictive and prescriptive analytics techniques (implemented in the Excel tools), interpret the results, recommend a course of action and critically evaluate your approach. Groups also present the work live in the final seminar for a separate 5%.
Do I need to buy the textbook?
The prescribed text is Business Analytics, 5th edition, by Camm, Cochran, Fry and Ohlmann (Cengage). Earlier editions are acceptable, although the equivalent chapter for each week can differ. The seminar slides are the primary study material, and the 5th-edition e-book can be borrowed through the University of Melbourne library.
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