MBA520 - Digital Supply Chain Management

Credit Points: 20 credit points

Workload: 60 hours

Prerequisite: MN405 Data and Information Management

Co-requisite: MDA513 ICT Practices

Aims & Objectives

This is a core unit out of a total of 12 units in the Master of Business Analytics (MBAnalytics). This unit addresses the course learning outcomes and complements other courses in a related field by developing students’ specialised knowledge of key digital building blocks, enabling technologies, and analytical skills that are applied in the emerging field of digital supply chain management. For further course information refer to: https://www.mit.edu.au/study-with-us/programs/master-business-analytics. This unit is part of the AQF level 9 course.

This unit offers students an overview of key building blocks, ICT technologies, and data analytical techniques that drive the transformation of traditional supply chains into digital supply chains. Specifically, students will explore the role and use of digital data in supply chain management that will allow business analysts to define the kind of analytics that will be used. In addition, students will gain knowledge and skills in designing, planning, and execution of processes in managing digital supply chains. These skills include network optimisation and resource scheduling methods. Moreover, students will be exposed to how digital supply chain related strategic, tactical, and operational decisions are made by studying real-world cases. Furthermore, the unit provides students with opportunities in putting into practice the concepts, methods, and ethical principles of earlier units of this master’s course, the appropriate use of research methods and an ability to apply communication and technical research skills in a realistic situation.

This unit will cover the following topics:

  • Introduction to Supply Chain Management
  • Digital Transformation of The Supply Chain
  • Building Blocks: Smart Factories, Warehouses, and Logistics
  • Enabling Technologies I: Internet of Things and Cloud Edge Computing
  • Enabling Technologies II: Blockchain and Digital Twins
  • Enabling Technologies III: Artificial Intelligence and Data Science
  • Digital Supply Network Optimisation using Linear Programming
  • Supply Chain Scheduling using Constraint Integer Programming
  • Digital Supply Chain (DSC) Design Process, Network, and Models
  • Decision Analytics: Perfect versus Sample Information
  • Supply Chain Analytics: Descriptive, Predictive and Prescriptive
  • Barriers to Implementation and The Future of DSC Management

Learning Outcomes

4.1 Course Learning Outcomes
The Course learning outcomes applicable to this unit in respect of the course being studied are listed on the Melbourne Institute of Technology website: www.mit.edu.au 

4.2 Unit Learning Outcomes
At the completion of this unit students should be able to:

  1. Explain key building blocks and enabling ICT technologies that drive the transformation of traditional supply chains into digital supply chains.
  2. Possess practical knowledge of algorithms, analytics, and artificial intelligence for harnessing data to make supply chain decisions.
  3. Apply mathematical programming and optimisation models in designing supply chain networks and processes.
  4. Plan and execute supply chain management in different organisational settings.
  5. Critically evaluate emerging ICT technologies for adoption in digital supply chain management.

Weekly Topics

This unit will cover the content below:

Week Topics
1 Introduction to Supply Chain Management
2 Digital Transformation of The Supply Chain
3 Building Blocks: Smart Factories, Warehouses, and Logistics
4 Enabling Technologies I: Internet of Things and Cloud Edge Computing
5 Enabling Technologies II: Blockchain and Digital Twins
6 Enabling Technologies III: Artificial Intelligence and Data Science
7 Digital Supply Network Optimisation using Linear Programming
8 Supply Chain Scheduling using Constraint Integer Programming
9 Digital Supply Chain (DSC) Design Process, Network, and Models
10 Decision Analytics: Perfect versus Sample Information
11 Supply Chain Analytics: Descriptive, Predictive and Prescriptive
12 Barriers to Implementation and The Future of DSC Management

Assessment

Assessment Task Due Date Release Date A B Learning Outcomes Assessed
Assignment 1 (Individual) Week 3 Week 1 10%   a,d
In-class test Week 6     10% a
Assignment 2 (Group) Week 11 Week 7 25%   b,c,e
Laboratory and Problem Based Learning participation & submission Week 2-11 Week 2-11 10%   a-e
Final Examination (3 hours)       45% a-c
TOTALS     45% 55%  

Task Type: Type A: unsupervised, Type B: supervised.

Contribution and participation (in class) (10%)
Students are expected to attend each scheduled session, arrive on time and remain for the entire session. Adherence to this requirement will be reflected in the marks awarded for this assessment. Students are also strongly encouraged to actively participate in the class discussions and tutorial activities by answering questions, expressing their opinions, insights and their learnings from the course.

Presentations (if applicable)
For presentations conducted in class, students are required to wear business attire.

Textbook and Reference Materials

Textbook:

  • Bart L. MacCarthy and Dmitry Ivanov. The Digital Supply Chain—emergence, concepts, definitions, and technologies, Elsevier, 2022.
  • Nicoleta Tipi. Supply Chain Analytics and Modelling, Kogan Page Ltd, 2020.

Recommended Readings:

  • Alexis Bateman. Supply Chain Analytics Key Concepts, MITx MicroMasters in Supply Chain Management MIT Center for Transportation & Logistics, Cambridge, MA 02142 USA, V1 Spring 2017
  • Bob Heaney. Supply Chain Intelligence: Descriptive, Prescriptive, and Predictive Optimisation, Research Paper, Aberdeen Group, 2015
  • ANN GRACKIN, LINDA ROSENCRANCE and DAVID TURBIDE. Grow Profits with Supply Chain Analytics, TechTarget 275 Grove Street, Newton, MA
  • Bonsug Chae and David L. Olson. Business Analytics for Supply Chain: A Dynamic-Capabilities Framework, International Journal of Information Technology & Decision Making Vol. 12, No. 1, World Scientific Publishing Company, 2013
  • B.S. Sahay and Jayanthi Ranjan. Real time business intelligence in supply chain analytics, Information Management & Computer Security Vol. 16 No. 1, 2008
  • Dawei Lu. Fundamentals of Supply Chain Management, Dr Dawei Lu and Ventus Publishing ApS, 2011.
  • Genpact White Paper. Driving supply chain excellence through Data-to-Action Analytics, Genpact (NYSE: G), 2015
  • Gerhard J. Plenert. Supply Chain Optimisation through Segmentation and Analytics, CRC Press, New York, 2017.
  • Jerry O’Dwyer. Supply Chain Analytics The three-minute guide, Deloitte Development LLC, 2012
  • Mark Pearson, Frode Huse Gjendem, Pierre Kaltenbach, and Gary Hanifa. Big Data Analytics in Supply Chain: Hype or Here to Stay? Accenture Global Operations Megatrends Study, 2014
  • Nenad Stefanovic and Danijela Milosevic. Model for Big Data Analytics in Supply Chain Management, 7th International Conference on Information Society and Technology ICIST, 2017
  • Nenad Stefanovic. Proactive Supply Chain Performance Management with Predictive Analytics, Hindawi Publishing Corporation Scientific World Journal Volume, 2014

Internet references:

Adopted Reference Style: IEEE

Students are required to purchase the prescribed text and have it available each week in the class.

Graduate Attributes

MIT is committed to ensure the course is current, practical and relevant so that graduates are “work ready” and equipped for life-long learning. In order to accomplish this, the MIT Graduate Attributes identify the required knowledge, skills and attributes that prepare students for the industry.
The level to which Graduate Attributes covered in this unit are as follows:

Ability to communicate Independent and Lifelong Learning Ethics Analytical and Problem Solving Cultural and Global Awareness Team work Specialist knowledge of a field of study

Legend

Levels of attainment Extent covered
The attribute is covered by theory and practice, and addressed by assessed activities in which the students always play an active role, e.g. workshops, lab submissions, assignments, demonstrations, tests, examinations.
The attribute is covered by theory or practice, and addressed by assessed activities in which the students mostly play an active role, e.g. discussions, reading, intepreting documents, tests, examinations.
The attribute is discussed in theory or practice; it is addressed by assessed activities in which the students may play an active role, e.g. lectures and discussions, reading, interpretation, workshops, presentations.
The attribute is presented as a side issue in theory or practice; it is not specifically assessed, but it is addressed by activities such as lectures or tutorials.
The attribute is not considered, there is no theory or practice or activities associated with this attribute.