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Aka, A, Bamgbade, A A, Saidu, I and Balogun, O M (2019) A conceptual framework for waste identification and reduction in Nigerian sandcrete blocks production process. Construction Innovation, 19(03), 405–23.

Georgiadou, M C (2019) An overview of benefits and challenges of building information modelling (BIM) adoption in UK residential projects. Construction Innovation, 19(03), 298–320.

Le, P L, Dao, T and Chaabane, A (2019) BIM-based framework for temporary facility layout planning in construction site. Construction Innovation, 19(03), 424–64.

Liu, G, Nzige, J H and Li, K (2019) Trending topics and themes in offsite construction(OSC) research. Construction Innovation, 19(03), 343–66.

Mahami, H, Nasirzadeh, F, Hosseininaveh Ahmadabadian, A, Esmaeili, F and Nahavandi, S (2019) Imaging network design to improve the automated construction progress monitoring process. Construction Innovation, 19(03), 386–404.

Olawumi, T O and Chan, D W (2019) An empirical survey of the perceived benefits of executing BIM and sustainability practices in the built environment. Construction Innovation, 19(03), 321–42.

Saka, A B and Chan, D W (2019) A global taxonomic review and analysis of the development of BIM research between 2006 and 2017. Construction Innovation, 19(03), 465–90.

Sundling, R (2019) A development process for extending buildings vertically – based on a case study of four extended buildings. Construction Innovation, 19(03), 367–85.

Wang, Y, Gosling, J and Naim, M M (2019) Assessing supplier capabilities to exploit building information modelling. Construction Innovation, 19(03), 491–510.

  • Type: Journal Article
  • Keywords: Construction management; Clustering; Supply chain management; SME-s; BIM; Maturity levels;
  • ISBN/ISSN: 1471-4175
  • URL: https://doi.org/10.1108/CI-10-2018-0087
  • Abstract:
    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.