Page 5 of 5
Assessment Details and Submission Guidelines
School
Business
Course Name
Master of Professional Accounting
Unit Code
MA609
Unit Title
Business Analytics and Data Intelligence
Assessment Autho
Dr Ken Mardaneh
Assessment Type
Project [Group]
Assessment Title
Project [Group]
Unit Learning Outcomes covered in this assessment
a. Demonstrate advanced and integrated understanding of business and data Intelligence for organisational decision-making.
. Analyse critically, reflect on and synthesise techniques of data visualisation and data mining.
c. Demonstrate advanced and integrated understanding of business analytical models.
d. Critically analyse, synthesise and reflect on decision analysis techniques to develop optimal strategy.
Weight
30%
Total Marks
100 Marks (this will be scaled down to 30%)
Word limit
Not applicable
Release Date
Week 5
Due Date
Week 11
Submission Guidelines
ï‚· All work must be submitted on Moodle by the due date along with a completed
Assignment Cover Sheet.
ï‚· For Melbourne students: use Melbourne Campus Submission link.
ï‚· For Sydney students: use Sydney Campus Submissions link.
ï€ ï€ ï€ ï€ ï€ ï€ ï€ ï€ ï€ ï€ ï‚· Reference sources must be listed appropriately at the end in a reference list using APA referencing style.
Extension
Â· If an extension of time to submit work is required, a Special Consideration Application must be submitted directly to the School's Administration Officer, in Melbourne on Level 6 or in Sydney on Level 7. You must submit this application three working days prior to the due date of the assignment. Further information is available at:
http:
www.mit.edu.au/about-mit/institute-publications/policies-procedures-and-guidelines/specialconsiderationdeferment
Academic Misconduct
Â· Academic Misconduct is a serious offence. Depending on the seriousness of the case, penalties can vary from a written warning or zero marks to exclusion from the course or rescinding the degree. Students should make themselves familiar with the full policy and procedure available at: http:
www.mit.edu.au/about-mit/institute-publications/policies-procedures-and-guidelines/Plagiarism-Academic-Misconduct-Policy-Procedure. For further information, please refer to the Academic Integrity Section in your Unit Description.
Assessment Cover Sheet
Student ID Numbe
s:
Student Surname/s:
Given name/s:
Course:
School:
Unit code:
Unit title:
Due date:
Date submitted:
Campus:
Lecturer:
Tutor:
Student Declaration
I/We declare that:
1. the work contained in this assignment is my/our own work/group work, except where acknowledgement of sources is made;
1. certify that this assessment has not been submitted previously for academic credit in this or any other course;
1. I/we have read the MITâ€™s Plagiarism and Academic Misconduct Policy Procedure, and I/we understand the consequences of engaging in plagiarism;
1. a copy of the original assignment is retained by me/us and that I/we may be required to submit the original assignment to the Lecturer and/or Unit Co-ordinator upon request;
I/we have not plagiarised the work of others or participated in unauthorised collaboration when preparing this assignment.
MIT ID
Signature
Date
For Assessor Use Only (if not marked on Moodle)
Name:
Position
Date:
Signature:
Marks/Grades:
Assignment Description:
The assignment is designed to allow you to demonstrate effective business analytics skills using optimisation methods. You will need to use linear programming to formulate and model a problem, and Excel solver will be used to conduct the analytics and obtain the solutions. For the assignment, you are required to form a group of 3 students.
Each group needs to select a company from the list below and use the companyâ€™s website to collect some background information and any type of preliminary information that could help in formulating the optimisation problem of the company. For example you may find some information about a given companyâ€™s operations, their
anches/wholesales, level of production, etc., that could help you with formulating the problem.
You will realise that there is no direct information about optimisation problems of the company of your choice, therefore you will need to use the companyâ€™s background information along with business analytics knowledge to formulate the problem. For instance for an airline the problem could be the assignment problem with assigning of terminals, airplanes, flights and pilots. Accordingly and based on the preliminary information that you collect, you need to make assumptions and build up a model for the company and solve the problem.
The assignment should be formulated based on any of Transportation, Transshipment, or Assignment problems (you just need to do one out of these three options). You can choose any company from the below list in airline or Metro train industries (for assignment problem), in manufacturing or port shipment industries (for transhipment or transportation problem). Below is a list of companies/organisations and possible approaches to problem solving:
Transportation problem by manufacturing and mining companies:
ADANI Mining (Transportation of minerals from origin to destination)
Honda (Transportation of parts from manufacturers to dealers)
Nissan (Transportation of parts from a manufacturer to dealer)
Transshipment problem by manufacturing companies:
Audi (Transportation of parts from manufacturers to dealers)
Mercedes (Transportation of parts from manufacturers to dealers)
BMW (Transportation of parts from manufacturers to dealers)
Assignment problem by companies/organisations:
Air Asia (assignment of terminals, airplanes, flights)
V-line (assignment of trains to different lines)
Melbourne royal hospital (assignment of nurses to different shifts)
St. Vincent hospital (assignment of surgeons, operating theatres based on availability)
Assignment requirement:
Â· choose a company from the list or assume your own company
Â· make assumptions about the problem before formulating the problem and modelling (assumptions such as the type of operation, amount of manufacturing and shipment, cost, etc.)
Â· Formulate a Linear Programming problem (LP)
Â· After formulating the problem define the following:
Â· Define decision variables
Â· Define objective functions
Â· Define constraints
Â· Implement the model
Â· Run the analysis using Excel solver
Â· Prepare Excel output of the model using excel solve
Â· Run sensitivity analysis using excel solver and interpret the results
Assignment structure:
Â· Introduction to the company
Â· Selection of the company
Â· assumptions about the problem before formulating the problem and modelling
Â· Problem definition
Â· Identification and description of the problem to be analysed
Â· Model design:
Â· Formulation of Linear Programming problem (LP)
Â· Defining decision variables
Â· Defining objective functions
Â· Defining constraints
Â· Model implementation:
Â· Run the analysis using Excel solver
Â· Prepare Excel output of the model using excel solve
Â· Run sensitivity analysis using excel solver and interpret the results
Â· Indication of optimal solution to the problem:
Â· Present solved problem using excel solver
Â· Recommendation and conclusion
Â· Structure and presentation of the assignment
Â· Appendix (should include both assignment word document and Excel solver file)
Note: Students need to submit the assignment as a word document along with the Excel solver file used for the analyses.
MA609 Business analytics and data Intelligence Project [Group] Marking Guide (30 Marks)
Criteria
Possible Marks
Marks Allocated
Introduction to the company:
Â· A
ief information around the company
usiness
Comment:
5%
Selection of the company
Â· assumptions about the problem before formulating the problem and modelling
Comment:
5%
Problem Definition:
Â· Identification and description of the problem to be analysed
Comment:
10%
Model design:
Â· Formulation of Linear Programming (LP) problem OR
Â· Defining decision variables
Â· Defining objective functions
Â· Defining constraints
Comment:
30%
Model implementation:
Â· Run the analysis using Excel solver
Â· Prepare Excel output of the model using excel solve
Â· Run sensitivity analysis using excel solver and interpret the results
Comment:
30%
Indication of optimal solution to the problem:
Â· Present solved problem using excel solver
Comment:
10%
Recommendation and conclusion:
Comment:
5%
Structure and Presentation of the assignment:
Comment:
5%
Total
100%
Overall Comments:
Assessor Name:
Assessor Signature:
= _____/30__ Marks
Date:
Page 2 of 3
Assessment Details and Submission Guidelines
School
Business
Course Name
Master of Professional Accounting
Unit Code
MA609
Unit Title
Business Analytics and Data Intelligence
Assessment Autho
Dr Ken Mardaneh
Assessment Type
Presentation[Group]
Assessment Title
Presentation[Group]
Unit Learning Outcomes covered in this assessment
b. Analyse critically, reflect on and synthesise techniques of data visualisation and data mining.
c. Demonstrate advanced and integrated understanding of business analytical models.
Weight
10%
Total Marks
100 Marks (this will be scaled down to 10%)
Word limit
Not applicable
Release Date
Week 5
Due Date
Week 11
Submission Guidelines
ï‚· All work must be submitted on Moodle by the due date along with a completed
Assignment Cover Sheet.
ï‚· For Melbourne students: use Melbourne Campus Submission link.
ï‚· For Sydney students: use Sydney Campus Submissions link.
ï€ ï€ ï€ ï€ ï€ ï€ ï€ ï€ ï€ ï€ ï‚· Reference sources must be listed appropriately at the end in a reference list using APA or IEEE referencing style.
Extension
Â· If an extension of time to submit work is required, a Special Consideration Application must be submitted directly to the School's Administration Officer, in Melbourne on Level 6 or in Sydney on Level 7. You must submit this application three working days prior to the due date of the assignment. Further information is available at:
http:
www.mit.edu.au/about-mit/institute-publications/policies-procedures-and-guidelines/specialconsiderationdeferment
Academic Misconduct
Â· Academic Misconduct is a serious offence. Depending on the seriousness of the case, penalties can vary from a written warning or zero marks to exclusion from the course or rescinding the degree. Students should make themselves familiar with the full policy and procedure available at: http:
www.mit.edu.au/about-mit/institute-publications/policies-procedures-and-guidelines/Plagiarism-Academic-Misconduct-Policy-Procedure. For further information, please refer to the Academic Integrity Section in your Unit Description.
Assessment Cover Sheet
Student ID Numbe
s:
Student Surname/s:
Given name/s:
Course:
School:
Unit code:
Unit title:
Due date:
Date submitted:
Campus:
Lecturer:
Tutor:
Student Declaration
I/We declare that:
1. the work contained in this assignment is my/our own work/group work, except where acknowledgement of sources is made;
1. certify that this assessment has not been submitted previously for academic credit in this or any other course;
1. I/we have read the MITâ€™s Plagiarism and Academic Misconduct Policy Procedure, and I/we understand the consequences of engaging in plagiarism;
1. a copy of the original assignment is retained by me/us and that I/we may be required to submit the original assignment to the Lecturer and/or Unit Co-ordinator upon request;
I/we have not plagiarised the work of others or participated in unauthorised collaboration when preparing this assignment.
MIT ID
Signature
Date
For Assessor Use Only (if not marked on Moodle)
Name:
Position
Date:
Signature:
Marks/Grades:
Presentation
Each group needs to present the findings of the group project. Visual representation takes the form of power point slides and includes one or more of the following: charts, graphs, statistics, pictures, photos or other graphics are used in the power point slides.You must cover relevant items on any required project task and make reference to them. This must include indicating steps for each analytics task. Presentation should also be professional.
Note: Presentation is an assessment task and PPT file need to be submitted for marking.
MA609 PRESENTATION MARKING GUIDE [100 Marks = 10%]
Criteria
Possible
Marks
Comments
Marks
Allocated
Fulfilment of the requirements of the task:
Accuracy of problem identification
Content
Â· Relevance- based on task