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Assessing supplier capabilities to exploit building information modelling

Wang, Yingli, Gosling, Jonathan and Naim, Mohamed 2019. Assessing supplier capabilities to exploit building information modelling. Construction Innovation: Information, Process, Management 19 (3) , pp. 491-510. 10.1108/CI-10-2018-0087

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Purpose A number of governments are making building information modeling (BIM) a mandatory requirement for all public works construction projects. While main contractors may be ready to comply with such requirements, the supply chain as whole may be vulnerable as lower-tier suppliers may not be able to adopt BIM. There is currently no objective approach to assessing BIM maturity; hence, this paper aims to develop a new approach to determine suppliers’ current vision and execution-based capabilities to exploit BIM and their capacity to reach a higher maturity level. Design/methodology/approach Based on UK Government BIM maturity levels, the authors exploit a unique data set made available by a main contractor, to determine a data-driven approach, using K-means, to assess the capabilities and vision of its supply base. Findings The authors find a direct comparison between our suggested K-means clusters and the UK Government’s BIM maturity levels. However, in interrogating specific cases, the authors find that using a subjective approach would have wrongly categorized certain companies. The authors also determine what capability and strategic developments are required for companies to move to a higher level. Research limitations/implications The method aligns with the existing UK BIM maturity model and enhances the model by determining the likelihood of a supplier in progressing to a higher level of maturity. The research was with a single case company, exploiting their existing survey instrument and data. A more comprehensive study could be adopted with a generic survey questionnaire. Practical implications The research may be exploited by companies to take a strategic approach to assess suppliers in BIM adoption and to establish supplier development mechanisms. Originality/value The data-driven approach avoids ambiguity of categories and mis-categorizing suppliers.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Business (Including Economics)
Centre for Advanced Manufacturing Systems At Cardiff (CAMSAC)
Publisher: Emerald
ISSN: 1471-4175
Funders: Highways England
Date of First Compliant Deposit: 8 April 2019
Date of Acceptance: 8 April 2019
Last Modified: 24 Nov 2020 19:27

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