A DATA-CENTRIC APPROACH FOR CONSTRUCTION PROJECTS AND ORGANIZATIONS: BARRIERS, ASSESSMENT, AND IMPLEMENTATION

Open Access
- Author:
- Karji, Ali
- Graduate Program:
- Architectural Engineering
- Degree:
- Doctor of Philosophy
- Document Type:
- Dissertation
- Date of Defense:
- June 13, 2022
- Committee Members:
- Rob Leicht, Major Field Member
Nathan Brown, Major Field Member
Chris McComb, Outside Field Member
John Messner, Chair & Dissertation Advisor
Aleksandra Radlinska, Outside Unit Member
Julian Wang, Program Head/Chair - Keywords:
- data-centric approach in construction
data-centric barriers
data-centric
construciton industry
categorical principal component analysis
digitization
digitalization
digital transformation
barriers
data-centric maturity
data-centric maturity matrix
Building Information Modeling
data-centric implementation framework
data-centric case study
Assessment
data-centric assessment
implementation - Abstract:
- Compared to other industries, the construction industry is one of the least digitalized industries, and this shortcoming has a significant impact on its life cycle management of facilities. Today, with the advancement of technology, the need to take advantage of the technology seems more than needed. To overcome this data deficit, construction organizations and project teams should expand their data-centric approaches throughout a facility's planning, design, construction, and operations phases. According to Dell’s Digital Transformation Index, 80 percent of organizations fast-tracked at least some digital transformation programs during 2020. These higher levels of digitalization can significantly impact the overall success of organizations. According to a survey by McKinsey Global Institute, data-driven organizations are more likely to be profitable and have more success acquiring and retaining customers. Another study by Franz and Messner found a significant positive relationship between digitalization practices such as BIM use adoption and the speed of delivery, perceived facility quality, and group cohesion within the project team. An essential step to the digitalization transformation of an organization is understanding and continuously improving the organization’s data-centric approaches. The data-centric approach can be defined as all practices and processes to foster digital collaboration among team members using a core data model and enable current and future technology advancement in an optimized approach throughout all phases of the project's life cycle. There are some challenges to implementing this approach for organizations and construction projects. Firstly, the barriers to a data-centric approach should be identified. Identifying the barriers to the data-centric approach is one of the first steps in its implementation. Second, as the saying goes, "what gets measured, gets managed". Therefore, there is a need to develop tools to measure the level of data-centric approach in organizations and projects. Finally, to properly implement the data-centric approach, there is a need to develop a data-centric implementation framework. Therefore, this dissertation has the following objectives: • Identify the barriers to data-centric collaboration. • Create two data-centric maturity assessment matrices – one at a project level and the other at an organizational level • Implementation guidelines to support continuous improvement and validate the data-centric maturity tools by applying them to case studies. A large portion of this research was performed as a core part of a Construction Industries Institute (CII) research project. This project included a core research team of 19 professionals, three academic faculty investigators, and myself. These individuals were Research Team 372 (RT 372). The research tasks presented throughout this dissertation were led by this researcher in close collaboration with the three faculty members, along with contributions from all members of RT 372. To achieve the first objective, an initial research effort was conducted, and 35 data-centric barriers were identified. Next, using the CII RT 372 research team, the barriers were reduced to 22. Next, a survey was distributed to industry experts to identify the most important barriers and the most difficult barriers to overcome. The following barriers were identified as the most important ones: 1. The challenges of integrating multiple data sources 2. The cost to maintain an operational model of a facility 3. The organizational culture’s resistance to change In addition, below are the most challenging barriers to overcome: 1. The financial investments needed from small business partners or project stakeholders 2. The organizational culture’s resistance to change 3. The challenges of integrating multiple data sources Finally, using Categorical Principal Components Analysis (CATPCA), the 22 barriers were grouped into four comprehensive barriers. To achieve the second objective, more than 20 existing quantitative metric systems for related technology-specific maturity tools in the industry were studied. Through a content analysis of these instruments along with lessons learned to overcome important barriers, two maturities to measure the data-centric on a project or within an organization were developed. Finally, to achieve the third goal, the important elements of the implementation framework were identified with the help of CII RT 372 and through several meetings. Once the framework elements were identified, the data-centric implementation framework was developed using the Plan-Do-Check-Act (PDCA) framework. The PDCA process identifies and tests changes and measures results before scaling up the process improvements method. A series of four case studies followed this to validate the maturity assessment matrices. Three journal papers will be submitted according to the three research objectives. The structure of this dissertation is therefore based on the three journal papers.