Course Title: Performance Management Foundations

Part A: Course Overview

Course Title: Performance Management Foundations

Credit Points: 12.00


Course Code




Learning Mode

Teaching Period(s)


City Campus


115H Aerospace, Mechanical & Manufacturing Engineering

Distance / Correspondence or Face-to-Face

Sem 1 2006,
Sem 2 2006,
Sem 1 2007,
Sem 2 2007,
Sem 1 2008,
Sem 2 2008,
Sem 1 2009,
Sem 2 2009,
Sem 1 2010,
Sem 2 2010,
Sem 1 2011,
Sem 2 2011,
Sem 1 2012,
Sem 1 2013,
Sem 2 2013,
Sem 1 2015,
Sem 2 2015,
Sem 1 2016,
Sem 2 2016


City Campus


115H Aerospace, Mechanical & Manufacturing Engineering

Distance / Correspondence or Face-to-Face or Internet

Sem 1 2014


City Campus


172H School of Engineering

Distance / Correspondence or Face-to-Face

Sem 1 2017,
Sem 2 2017

Course Coordinator: Dr Arun Kumar

Course Coordinator Phone: +61 3 9925 4328

Course Coordinator Email:

Pre-requisite Courses and Assumed Knowledge and Capabilities


Course Description

In this course you will study how to analyse data captured from engineering enterprises and systems.  You will be introduced to the key planning and control mechanisms for effective outcome and performance management.  You will learn the general principles and methodologies of data analysis and statistics which form the basis for modelling of engineering systems.  You will work on the sample models that will help you to realise the causal relationships of different operating parameters.  You will develop skills in data analysis fundamentals, regression analysis, data mining, forecasting, discriminant analysis, simulation and queuing analysis, project control, decision support management tools, some of which will be developed on spreadsheets.

Objectives/Learning Outcomes/Capability Development

Program Learning Outcomes (PLOs)

This course contributes to the following program learning outcomes:

1. Needs, Context and Systems

  • Describe, investigate and analyse complex engineering systems and associated issues (using systems thinking and modelling techniques)

2. Problem Solving and Design 

  • Develop creative and innovative solutions to engineering problems

3. Analysis

  • Comprehend and apply advanced theory-based understanding of engineering fundamentals and specialist bodies of knowledge in the selected discipline area to predict the effect of engineering activities
  • Apply underpinning natural, physical and engineering sciences, mathematics, statistics, computer and information sciences.

5. Research

  • Develop creative and innovative solutions to engineering challenges

Course Learning Outcomes (CLOs) 

Upon successful completion of this course you should be able to:

  1. Perform a thorough data analysis of the performance data set and summarise the findings
  2. Recognise situations and apply the appropriate forecasting models to represent the trend of the business
  3. Fit some parts of the data set to a regression analysis model and interpret the implication of the model in terms of the enterprise’s past and future performance
  4. Define and apply the Monte Carlo technique to a number of different business modelling situations
  5. Re-structure the provided data set into different interpretable modelling frameworks
  6. Apply decision making techniques to the different forms of data models and investigate what-if scenarios
  7. Create innovative solutions to solve problems recognised in the what-if scenarios
  8. Apply theories of mathematics and statistics to consolidate the provided data to an indicative decision support data structure
  9. Apply decision making techniques 

Overview of Learning Activities

Learning activities throughout the course include: Lectures, tutorials, presentations, group discussions, project work, and computer based analysis exercises

Overview of Learning Resources

All lecture Powerpoint slides, model answers of exercises in the lectures and tests in previous semesters are available from the course Blackboard (available through myRMIT).  Multimedia files related to the topics will also be uploaded to the respective weekly folders.  Except guest lectures (due to copyright restrictions), all lectures in this course will be recorded on Blackboard Collaborate.  

This course has a prescribed text. A list of journal articles will be used as reading materials.  Copies of these articles can be obtained from RMIT Library journal subscriptions.

Overview of Assessment

X This course has no hurdle requirements.

☐ All hurdle requirements for this course are indicated clearly in the assessment regime that follows, against the relevant assessment task(s) and all have been approved by the College Deputy Pro Vice-Chancellor (Learning & Teaching).


Assessment item:  Assignment 1 (individual)
Weighting of final grade:  30%     
Related course learning outcomes:  1, 2, 3, 4
Description:  Analysis of a data set related to the performance of an engineering enterprise.

Assessment item:  Assignment 2 (group)
Weighting of final grade:  30%     
Related course learning outcomes:  5, 6, 7
Description:  Analysis of a given decision problem of an enterprise using a number of decision techniques to develop new solutions.

Assessment item:  Test
Weighting of final grade:  40%    
Related course learning outcomes:  8, 9
Description:  Thoroughly conversant with some of the spreadsheet’s advanced features for performance assessment