Der-Jiun Pang
@iumw.edu.my
International University of Malaya Wales
DJ (PMP, PMI-ACP, CITPM, ITILv3, ITILv4, CLP) successfully implemented more than 100 projects in various industries in Asia Pacific Region. Met and exceeded management expectations and proven ability to attain assigned goals. Proven record of delivering projects in extremely challenging timelines and environments. Demonstrated ability to recover failing projects.
RESEARCH, TEACHING, or OTHER INTERESTS
Artificial Intelligence, Management of Technology and Innovation, Management Information Systems, Electrical and Electronic Engineering
FUTURE PROJECTS
Integrated Earned Value Method in Project Management Sustainability
Applications Invited
Scopus Publications
Scholar Citations
Scholar h-index
Scopus Publications
Der-Jiun Pang
IEEE
Traditional Earned Value Management technique in project planning for effort and duration remains low to medium accurate. This study seeks to develop a highly reliable and efficient Integrated Earned Value Method (IEVM) to improve cost and duration prediction accuracy. This experiment compared the performance of five machine learning models across three different datasets and six performance indicators and verified the models with three other types of live project data. The results indicated that IEVM is a highly reliable, effective, consistent, and accurate machine learning-based method with a significant increase in accuracy over the conventional Earned Value Management technique. The finding pointed out a potential gap in the relationship between dataset quality and the performance of the ML model.
Der-Jiun Pang, Kamran Shavarebi, and Sokchoo Ng
Politechnika Wroclawska Oficyna Wydawnicza
Despite the Information Technology (IT) sector’s continuing growth driving massive demand for IT project practitioners, the high failure rate of IT projects has caused enormous losses for many organizations. Establishing effective and proactive practice for project risk management (PRM) is imperative. Risk exposure scoring is becoming a critical risk classifier in prioritizing items in descending order, developing plans to address the most significant factors, and leaving the rest on a “watch list”. This study analyzes responses from targeted project managers (PM) in Malaysia-Singapore to a survey. The author ranked the intrinsic risk of projects and investigated the effect of a project practitioner’s level of experience in risk assessment. The results indicated that a project practitioner’s assessments of risk depend on the number of years of experience acquired.
Der-Jiun Pang, Kamran Shavarebi, and Sokchoo Ng
IEEE
Despite the impact of the COVID-19 pandemic in 2020-21, the digital economy remains solid and sustainable. This trend continues to drive massive demand for Information Technology (IT) projects. Underestimated costs and time are considered one of the most critical IT project risks that directly impact a project's success or failure. Currently, there is a lack of models, tools, and techniques capable of effectively predicting cost and duration. This study aims to find a solution to enhance prediction capability by using a machine learning (ML) model. An experiment was conducted comparing the performance of each ML model utilizing three distinct datasets and fourteen different models against six performance indicators. The results indicated the existence of a highly reliable, effective, consistent, and accurate ML model with a significant degree of augmentation compared to conventional predictive project management tools and techniques.