|Managing and controlling excessive dimensional and geometrical variability (i.e., tolerances) of modular components and assemblies during the fabrication, transportation, and erection phases, represents a major issue in modular construction (MC) projects. The current industry practices manage tolerance-related risks either reactively (e.g., onsite adjustment by applying forces, shimming, and replacing defected components), or proactively (e.g., 2D & 3D jigs, prototyping (mock-ups), and 3D laser scanning technology, and tolerance theory). The reactive approaches include expensive and time-consuming field rework, schedule delays, and serviceability or functional failures. On the other hand, the proactive approaches require a significant amount of investment (resources) during early project phases (design and fabrication) to produce modular systems that are compliant with design specifications. Thus, improper assessment and reactive management of excessive geometric variabilities due to out-of-tolerances can result in extensive site-fit rework, cost overruns, schedule delays, quality issues, and owner dissatisfaction.
The perceived risks and challenges will continue to fuel the reluctance of industry practitioners to apply modularization in future construction projects. Therefore, different decision support systems (DSSs), frameworks, decision matrices, models, and toolkits have been developed to evaluate modularization feasibility (benefits and challenges) for construction projects during early project phases. However, these DSSs, frameworks, and toolkits are not without their limitations. Most previously developed DSSs and toolkits focus on: 1) strategic and high-level decisions; 2) general modularization risks ; and 3) reactive solutions. Also, these DSSs and toolkits lack: 1) quantitative and probabilistic risk assessment techniques to evaluate the modularization risk impact on the overall project performance (cost, schedule, quality, etc.); 2) consideration of the impact of the unique relationships (propagation behaviour and cause-effect relationship) among risks in decision making process; and 3) integration of dynamic risk assessment and management techniques to revise the risk management plans as more accurate modularization process capability information becomes available. With this in mind, further efforts are needed to systematically evaluate tolerance-related risks and excessive geometric variability issues, and proactively manage their impact, both of which are expected to improve modularization performance and maximize its benefits in construction projects.
The goals of the research presented in this research are to develop: 1) a systematic process to identify, quantitatively evaluate, and proactively manage tolerance-related risks by identifying optimum geometric variability (using a strict or relaxed tolerance approach) that will achieve cost efficiency requirements; 2) an efficient approach to thoroughly evaluate and manage tolerance-related risks at local and global levels by incorporating the propagation behaviour and cause-effect relationships among risks in the decision making process; and 3) a dynamic methodology to continually evaluate tolerance-based risk management plans and revise risk response decisions as new information becomes available.
The results of the work conducted for this research study contribute to both knowledge and practice. On the knowledge side, the main contribution is the introduction of an efficient risk management methodology, which will support modularization decision-making process with respect to the selection of optimum approaches to proactively manage tolerance-related risks and excessive geometric variability issues in construction projects. On the practice side, this research will enhance in a quantitative and proactive manner our understanding of the unique risks and challenges associated with MC, which will help the stakeholders, including project risk managers, decision makers, and construction managers, to improve modularization performance and maximize its benefits.