Project Portfolio Reliability: A Bayesian Approach for LeAgile Projects

Abstract

We propo se an applied Bayesian learning approach for continuous planning and evolution of information system projects and portfolios. Unlike traditional project management approaches for information system, the proposed approach considers the cumulative effect of all past experiences to achieve continuous performance and reliability prediction. The results of quantitative comparisons with other common estimation approaches, such as non-learning point estimates and traditional Bayesian approach, using real case data indicate that the proposed approach can generate a more realistic metric to continuously plan and measure the performance of evolving LeAgile projects or portfolios. This study can support decision makers, engineering teams, and management by supplying a practical and scalable project performance prediction tool for continuous planning and system evolution.

Keywords:

• Continuous Planning
• Continuous Learning
• Project Portfolio Management
• Reliability
• Bayesian Inference
• LeAgile

EMJ Focus Areas:

• Continuous Improvement
• New Product Development
• Program & Project Management
• Systems Engineering

Disclosure Statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Sagar Chhetri

Dr. Sagar Chhetri, PhD is a Systems Excellence Scientist, Researcher and ML Project/Portfolio Management professional. He is seasoned professional for 15+ years in applied engineering and leadership. He is a Ph.D. in the Systems and Engineering Management (SYEM) from Texas Tech University. He holds PMP certification from PMI and Data Scientist certification from Microsoft (edEx). His research interests include applied engineering, enterprise project portfolio improvement, computation, reliability, machine learning, AI, Bayesian mathematics and LeAgile.

Dongping Du

Dongping Du, PhD is an Associate Professor in the Department of Industrial, Manufacturing and Systems Engineering at Texas Tech University. She received her BS in Electrical Engineering from the China University of Mining and Technology and her MS and PhD in Industrial Engineering from University of South Florida. Her research interests are data mining and machine learning, modeling and simulation, reliability analysis and risk assessment. She is a member of Institute of Industrial and Systems Engineers (IISE), Institute for Operations Research and the Management Sciences (INFORMS), and Institute of Electrical and Electronics Engineers (IEEE). Her complete profile can be retrieved from https://www.depts.ttu.edu/imse/faculty/dongping_du/Dongping.CV.2019.pdf.

Susan Mengel

Susan Mengel, PhD is an Associate Professor in the Computer Science Department of Texas Tech University (TTU), with over 24 years of experience at TTU. She is funded by NSF, and is published in the IEEE Systems, in the Springer Soft Computing, and in the Cluster Computing Journals. She has published over 50 papers including reports, journal publications, conference papers, and chapters. Her complete profile can be retrieved from https://www.depts.ttu.edu/cs/faculty/susan_a._mengel/mengel_CV.pdf.