Course Title: Intelligent Enterprise Systems

Part A: Course Overview

Course Title: Intelligent Enterprise Systems

Credit Points: 12.00

Terms

Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

ISYS2396

City Campus

Postgraduate

620H Business IT and Logistics

Face-to-Face

 Sem 2 2007,
Sem 2 2008,
Sem 2 2009,
Sem 2 2010,
Sem 2 2011,
Sem 1 2012,
Sem 1 2013,
Sem 1 2014,
Sem 1 2015,
Sem 2 2015,
Sem 1 2016,
Sem 2 2017,
Sem 1 2018,
Sem 2 2018,
Sem 1 2019,
Sem 2 2019,
Sem 1 2020,
Sem 2 2020,
Sem 1 2021

ISYS2396

City Campus

Postgraduate

665H Accounting, Information Systems and Supply Chain

Face-to-Face

 Sem 1 2022,
Sem 1 2023,
Sem 1 2024

Course Coordinator: Dr Say Yen Teoh

Course Coordinator Phone: +61 3 9925 5788

Course Coordinator Email: sayyen.teoh@rmit.edu.au


Pre-requisite Courses and Assumed Knowledge and Capabilities

None


Course Description

Intelligent enterprise systems (IE) are future-ready enterprise resource planning (ERP) software that integrate with built-in intelligent technologies, including artificial intelligence (AI), machines learning (ML), the Internet of Things (IoT) and advanced analytics. IE enables organisations to predict market trends, maximise business value, and grow more resilience under the most dynamic and demanding market.

This course explores IE and intelligent technologies from the concepts of planning, selecting, evaluating, implementing, and operating IE to make smarter and faster business decisions.


Objectives/Learning Outcomes/Capability Development

-


On successful completion of this course you will be able to:

CLO1. Evaluate the factors that lead to the need for digital transformation.

CLO2. Critically assess the advantages and disadvantages of implementing intelligent enterprise systems.

CLO3. Critically evaluate an unbiased Intelligent Enterprise Systems software selection.

CLO4. Investigate how to implement intelligent enterprise systems and transform business processes by incorporating design thinking concept.

CLO5. Utilise big data analytics, AI, ML and IoT to make smarter and faster decisions.


Overview of Learning Activities

The learning activities will provide you with the opportunity to reflect and develop your creativity and critical thinking. In order to develop the stated course capabilities you are expected to actively participate in the following learning activities:

  • Regular attendance and active participation in scheduled lectures and workshops
  • Reading of course notes and other assigned reading materials
  • Satisfactory completion of assessment tasks


Overview of Learning Resources

Various learning resources are available online through MyRMIT Studies\Canvas. The lecture notes and workshop notes are posted on Canvas.

Resources are also available online through RMIT Library databases and other facilities. Visit the RMIT library website for further details. Assistance is available online via our chat and email services, face to face at our campus libraries or via the telephone on (03) 9925 2020.

Additional resources and/or sources to assist your learning will be identified by your course coordinator and will be made available to you as required during the teaching period. 


Overview of Assessment

The assessment tasks, their weighting and the course learning outcomes to which they are aligned are as follows:

Assessment Task 1: 20%
Linked CLOs: 1, 2, 3

Assessment Task 2: 30%
Linked CLOs: 1, 3, 4

Assessment Task 3: 50%
Linked CLOs: 1, 2, 3, 4

Feedback will be provided throughout the semester in class and/or in online forums through individual and group feedback on practical exercises and by individual consultation.