Meta-Modeling of Complexity-Uncertainty-Performance Triad in Construction Projects

Continual learning with a Bayesian approach for evolving the baselines of a leagile project portfolio

“…Basit et al [15] looked into why projects are failing a lot more than past within recently published 33 relevant studies and found the top three reasons for in-house projects as "overrun budget & resources", "unrealistic estimated schedule," and "technical complexity". It is known that the complexity always increases with uncertainty [16] and demand for faster software development [18] are creating unrealistic schedules. These studies leave us with the conclusion that project performance measurement is changing over time [19]; the way we define and measure project success in a complex environment may be outdated [15], [12], [13] and a change is required to establish a common language for success [21], [20].…”

“…Pure success requires lean process improvement and learning. Few recent studies used computer-assisted algorithms to establish learning in a project, like learning and feedback loop system [48], work package size optimization for value improvement [49], Bayesian approach for portfolio risk identification and reduction [42], [50], Bayesian approach for traditional waterfall-type earned value planning [51] and modeling uncertainty [16]. The existing studies for success of leagile project system mainly focused on the risk factors, continuous improvement factors, complexity aspects, pros and cons, definitions, acceptance of agile or lean, and causes of failures [6], [15], [18], [27], [37], [38], [48].…”

Data-driven impact assessment of multidimensional project complexity on project performance

“…This approach has been effectively applied in several engineering domains (Fienen et al, 2016;Pousi et al, 2013). Dikmen et al (2020) adopted a similar meta-modeling approach to capture associations IJPPM 71,1 among project complexity, uncertainty and performance. However, their proposed approach does not capture multiple dimensions of performance and ignores the mapping of cause-effect relations among variables.…”

An analytic network process model for risk quantification of mega construction projects

“…As a risk source, the role of uncertainty in risk analysis has been highlighted by many researchers, such as Okudan et al (2021), whereas the efforts aimed at handling complexity within a structured risk model are scarce. The existing knowledge sources in project management literature fall short of explaining how complexity can be integrated into risk quantification models along with uncertainty (Dikmen et al, 2021;Thomé et al, 2016). For this reason, traditional approaches are often criticized for not being effective under high complexity (Baccarini, 1996;Cicmil et al, 2006;Haimes, 2018;Thamhain, 2013).…”