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Arashpour, M, Wakefield, R, Abbasi, B, Arashpour, M and Hosseini, R (2018) Optimal process integration architectures in off-site construction: Theorizing the use of multi-skilled resources. Architectural Engineering and Design Management, 14(01–02), 46–59.
- Type: Journal Article
- Keywords: Fuzzy–TOPSIS theory; modular prefabrication; off-site construction; precast process optimization; planning and control;
- ISBN/ISSN: 1745-2007
- URL: https://doi.org/10.1080/17452007.2017.1302406
The architecture, engineering and construction industry has long dealt with problems such as schedule and budget overruns, quality and safety issues, and low productivity. Off-site construction, which is a hybrid of manufacturing and construction, has significant potential to address industry’s endemic problems. However, off-site construction has been criticized for replicating the traditional subcontracting approach and therefore fragmented practice in the construction industry. The current research focuses on process integration and cross-training of multi-skilled resources as a solution to the aforementioned problem. To identify optimal process integration architectures for off-site construction, production data of three off-site manufacturers were analyzed by using a hybrid of fuzzy theory and the technique for order of preference by similarity to ideal solution. Findings reveal that process integration architectures transferring excess capacity from under-utilized to over-utilized resources in direct or indirect pathways are preferable in terms of satisfying decision criteria such as time and cost of cross-training, skill transferability, compliance with network logic, and safety considerations. The study contributes to the off-site construction literature by providing insight into dynamics of using multi-skilled resources. It also contributes to practice by developing a customizable and user-friendly framework for off-site production managers in order to identify the optimal process integration architecture in their own production scenarios.