Measuring Progress and Productivity in Model-Driven Engineering for Capital Project Delivery
Publication: Journal of Construction Engineering and Management
Volume 147, Issue 4
Abstract
A key underlying challenge in today’s model-driven approach to engineering is how progress and level of effort are being measured and reported. Recent definitions such as level of development enable project teams to track completeness of information in building information modeling (BIM)-driven processes; yet, there are no established processes or metrics to reliably measure progress or track productivity of a model-driven engineering process as a function of the maturity of the model and/or the data that supports the engineering process. To address this gap in knowledge, this paper presents a discrete set of model maturity index (MMI) definitions to measure progress in engineering work, per modeling discipline. Using these MMI definitions, a model maturity risk index (Model MRI) toolkit was created to track maturity of an engineering process at the granularity of work breakdown structure locations. The MMI toolkit also reports risk in achieving certain MMI levels per modeling discipline. To benchmark maturity of the modeling work per project milestone or model review session and to establish workflows for tracking progress, an addendum to the modeling execution plan (ModelXP) is presented. New productivity and project controls metrics for tracking modeling work are also introduced. The developed definitions, toolkit, and addendum to ModelXP were validated through exhaustive surveys and charrette tests with project stakeholders involved in capital projects. The role of the MMI definitions and Model MRI toolkit in providing project controls insight and proactive management opportunities in real-world projects is discussed in detail.
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Data Availability Statement
Data analyzed during the study were provided by a third party. Requests for data should be directed to the provider indicated in the Acknowledgements. Information about the Journal’s data-sharing policy can be found here: http://ascelibrary.org/doi/10.1061/(ASCE)CO.1943-7862.0001263.
Acknowledgments
The authors wish to thank CII and all RT332 member companies for their financial and technical contribution to this project. Special thanks to Derwin Cartmel from Day & Zimmerman, John Moncrief from Chevron, Jeffrey Barto from Burns & McDonnell, Richard Livingston from Wood Group, Maxwell Sehon from TechnipFMC, Victor Galotti from Georgia Pacific, Jennifer Sargianis from AVEVA, David Harrison from Zachry Group, Erick Teagarden from Fluor Corporation, Juan J. Jones from Invista, Peter A. Jackson from AstraZeneca, Dan Zaleski from Kiewit Energy, and many others who supported our research by participating in charrettes, surveys, and project meetings. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the CII or its member companies.
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Journal of Construction Engineering and Management
Volume 147 • Issue 4 • April 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Apr 5, 2020
Accepted: Aug 24, 2020
Published online: Jan 27, 2021
Published in print: Apr 1, 2021
Discussion open until: Jun 27, 2021
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