Critical factors for successful implementation of just-in-time concept in modular integrated construction: A systematic review and meta-analysis

Abstract

Modular integrated construction (MiC) is a revolutionary construction method. However, the logistics management of MiC has always been a major barrier to the wider adoption of MiC. Nonetheless, this challenge can be tackled by the application of lean techniques, namely, just-in-time (JIT). Numerous studies have identified and evaluated the critical factors (CFs) required to implement JIT; however, there is no consensus among the previous studies on these CFs and their level of importance. Therefore, this research, for the first time, provides a systematic review and meta-analysis of these CFs. The systematic review identifies 42 CFs. To further provide a synthesis analysis of previous studies, a meta-analysis approach is used. This analysis is conducted on the identified CFs to evaluate their importance level and hence rank them. The results indicate that all the 42 CFs are important for applying JIT, of which seven are highly significant for successfully implementing JIT in MiC. Although the ranking obtained by meta-analysis is much more reliable than that provided in the individual studies, however, there is still a high heterogeneity in the results, which depicts the uncertain nature of the construction field. Therefore, sub-group analysis is conducted to investigate this heterogeneity and uncover the hidden patterns in the literature. This is achieved by studying the influence of predictive factors (moderators) on the importance level of CFs. This analysis shows that the economy of a country and the type of project executed are influential factors. The results further indicate that developing economies, in contrast to advanced economies, should pay more attention to three CFs. Also, the results show that seven CFs are much more important in MiC projects than the other project types. This research work is highly beneficial for theory development and for practitioners by identification of significant CFs that warrant management dedication to best apply JIT. Researchers, in particular, can consider the recommendations given here for implementing future meta-analysis studies.

Exploration and prioritization of just in time enablers for sustainable health care: an integrated GRA-Fuzzy TOPSIS application

“…The present work provides impetus for system wide application of JIT with through and contextual understanding among the different JIT enablers. According to a study conducted by Hussein and Zayed (2020) (Bhat et al, 2019;Vaishnavi and Suresh, 2020;Kaswan and Rathi, 2021). Naidoo and Fields (2019) also found that strategic leadership and organizational attitude and integration of Lean practices as key factors for successful deployment of Lean thinking in healthcare.…”

A critical analysis of benefits and challenges of implementing modular integrated construction

“…Also, appropriate regulations and standards are required to be in place to overcome MiC challenges. On the other hand, academics need to support practitioners by devising updated decision support systems considering complex MiC technicalities and challenges (Hussein and Zayed 2020a;Abdelmageed and Zayed 2020). In a word, there is an urgent need to identify and analyze the benefits and challenges of MiC.…”

Simulation-Optimization for the Planning of Off-Site Construction Projects: A Comparative Study of Recent Swarm Intelligence Metaheuristics

“…Off-site construction (OSC) is a sustainable construction method in which parts of the structure are produced off-site and then transported to the construction site for erection [1,2]. OSC has many advantages for the improvement of productivity [3], quality [4], and safety [5] of the built environment.…”

“…Despite the benefits of OSC, its complexity and fragmentation require extensive planning from the project managers [1,11]. The need for extensive planning is one of the key barriers that hinder the wider adoption of OSC [12].…”

“…For multi-objective TCTP, researchers used dynamic programming [19], multi-objective ACO [20], non-dominated sorting genetic algorithm II (NSGA-II), multiobjective simulated annealing, and multi-objective PSO [21]. Despite the contribution of these studies, their common limitations are: (1) The proposed models addressed the traditional cast-in-situ construction method. Hence, they do not fit with OSC projects characterized by a three-stage supply chain (i.e., production, logistics, and installation stages) [22].…”