Project Management Analytics

Area of Interest: Capability Management

Course Summary

Project management is the key to business in many governments and non-government organisations, from construction, manufacturing, food processing, restaurant, and software industries. With the advancement of technologies, projects are becoming more complicated, and the decision-making process has become sophisticated. Modern projects are data-driven, and data is an asset for project managers since it can provide more insights into planning and controlling projects. However, to get the proper insights, project managers need to familiarise themselves with tools or techniques to analyse project data during the project lifecycle to accomplish the business requirements.

This course aims to familiarise such tools with the learners who regularly face challenges in managing projects or are interested in taking that challenge as their career. This course and accompanying short case studies will facilitate both tactical and operational level complex decision-making processes in managing projects.

No prior knowledge is assumed; however, basic skills in Microsoft Excel and knowledge of High School level maths would be an advantage.

Duration: 2 Days

Delivery Mode: Live simulcast online delivery

What you will receive

• UNSW Canberra certificate of completion/attendance

• Masters credit: UNSW Canberra allows students who have successfully completed a minimum of 12 days of approved professional education courses to use those courses as credit in eligible postgraduate programs.

Course Outline

Module 1 (Day 1)

10:00 am to 10:15 am: Housekeeping and Introduction

• Knowing each other

• How the course is structured.

10:15 am to 11:15 am: Project Management: Introduction

[Lecture 1]

• Understand why projects exist? and the importance of and growing need for a project management methodology and project managers

• Explain what a project is, and describe project attributes, constraints, and success factors

• Describe project management and key elements of the PM framework in Project Management Body of Knowledge (PMBOK) & ISO21500, including lifecycle, phases, process groups, and 10 knowledge areas

Morning Tea (11:15 am to 11:30 am)

11:30 am to 12:30 pm: Data/Decision Analytics & Project Portfolio Management: Introduction

[Lecture 2]

• Explain Data Analytics and how that is related to Project Management analytics

• Define Decision Analytics and demonstrate 'Decision Making' types

• Defining Business Analytics

• Explain the categorisation of Analytical Methods and Models

• An introductory description of PM analytics explaining the methodology's three main components

• Portfolio Optimisation?

• What are Big Data and Big Data Analytics?

• Legal and Ethical Issues in the Use of Data and Analytics

Lunch Break (12:30 pm to 1:00 pm)

1:00 pm to 2:45 pm: Analytics in Project Selection and Prioritisation

[Lecture 3]

• Understand how projects are created, including explaining how projects help organisations to achieve their strategic goals

• Learn methods for selecting feasible projects, including Weighted Scoring and Quantitative (Financial) methods

• Exercises (with Excel)

Afternoon Tea (2:45 pm to 3:00 pm)

3:00 pm to 4:15 pm: Analytics in Project Scheduling: PERT/CPM

[Lecture 4]

• Project Scheduling Based on Expected Activity Times

• Project Scheduling Considering Uncertain Activity Times

• Exercises (with Excel)

Module 2 (Day 2)

9:45 am to 11:15 am: Analytics in Project Supply Chain Management

[Lecture 5]

• Identify the role of supply chain management in project management and its importance for ensuring project success.

• Describe how to plan, conduct, & control project procurements.

• Supplier Selection by a multi-criteria decision-making technique

• Exercises (with Excel)

Morning Tea (11:15 am to 11:30 am)

12:00 pm to 12:45 pm: Prescriptive Analytics: Linear Programming and Optimisation: Part 1 [Lecture 6-a]

• How to design a mathematical formulation for a simple business problem

• How to solve a mathematical model by using a spreadsheet

• Exercises (with Excel)

Lunch Break (12:30 pm to 1:00 pm)

1:00 pm to 1:45 pm: Prescriptive Analytics: Linear Programming and Optimisation: Part 2 [Lecture 6-b]

• How to solve a mathematical model by using a spreadsheet

• Exercises (with Excel)

1:50 pm to 2:45 pm: Predicting Project Completion Time & Earned Value Management

[Lecture 7]

• Explain Earned Value Management (EVM) technique

• Demonstrating forecasting methods to predict project completion time

Afternoon Tea (2:45 pm to 3:00 pm)

3:00 pm to 4:00 pm: Artificial Intelligence-Based Approaches for Project Management: A Brief

[Lecture 8]

• How Artificial Intelligence can Contribute to Project Management with Uncertainty Considerations

• How Artificial Intelligence can Contribute to Project Risk Management (A Case Study)

• Explainable AI in Project Risk Management.

Conclusion & Course Feedback (4:00 pm to 4:15 pm)

Course Learning Outcome

• The participants will learn the fundamentals of project management and the role of data analytics in managing modern projects.

• The participants will gain skill in using decision-making tools by Excel solver for solving case studies inspired from real-life.

Affiliated Courses

• Project Schedule and Budget Control (Course Code: ZEIT8310)

• Business Analysis and Valuation (Professional Course)

• Supply Chain Analytics (Professional Course)

• Decision Making in Analytics (Course Code: ZZCA6510)

• Master of Decision Analytics (Program Code: 8634)

Who should attend

This introductory course is for anyone interested in solving decision problems in different projects for Defence, Government agencies, public and private sector organisations. This course is suitable for anyone faced with complex and dynamic decision problems in either public or private sector.

Presenter information

Dr Ripon K. Chakrabortty (Senior Member, IEEE) is a Lecturer (Assistant Professor Eqvnt.) in Systems Engineering & Decision Analytics at the School of Systems & Computing (SysCom), UNW Canberra, Australia. He is experienced in “Artificial Intelligence in Decision-Making for Complex Systems”. His research interest covers a wide range of topics in decision analytics, applied artificial intelligence, evolutionary computation, operations research, and applied optimisation in the "Project Scheduling and Supply Chain Management" domains. He is the team leader and founder of ‘The Decision Support & Analytics Research Group’ at the School of Systems & Computing, UNW Canberra, Australia. He is also the program coordinator of ‘Master of Decision Analytics’, and ‘Master of Project Management’ programs at UNSW Canberra. He has written three authored books, four book chapters, and over 160 technical journal and conference papers in prestigious venues (90%+ of them are in Q1 platforms as per WoS). He is a well-known educator in Decision Analytics and Systems Engineering discipline, where he has been teaching multiple courses with distinction. He is an Editorial Board Member of multiple well-recognised journals. Many organisations, such as the Department of Defence and the Commonwealth Government of Australia, have funded his research programs.