Purpose: The purpose of this paper is to identify how the newly emerging UK practice of “collaborative planning” (CP) for construction project delivery aligns with the advocated principles of the global last planner system (LPS) of production planning and control. Design/methodology/approach: A mixed, qualitative, exploratory approach was adopted for the study. This entailed qualitative data through three techniques, namely: semi-structured interviews, documents analysis, and structured observation. In total, 30 in-depth interviews were conducted over a 12 month period with lean construction consultants, clients, main contractors, and subcontractors drawn from the building, highways and infrastructure and rail sector. In all, 15 projects were visited where practices were observed. Findings: The study reveals that the current practice of CP in the UK partially aligns with the LPS principles. Where practitioners have heard of the LPS they believe it to be the same practice as CP. Research limitations/implications: This study is limited to 30 interviews, observation of 15 projects and document analysis. The aim of the study is not to generalise the findings, however, since the study examined top construction companies and practitioners in the UK and the findings were consistent across the sample, some conclusions could be made. The study is also limited to examining the construction phase only, future studies should incorporate the design phase. Practical implications: A clear identification of the elements of current practice compared to the components of the LPS provides a contribution to the future practice of project production planning and management in the construction industry. Social implications: The study highlights a continuing resistance to collaboration within the industry. This resistance is subtly embedded within implemented practices even though they are based on collaborative working for their success. Originality/value: This is among the first studies in the UK that comprehensively examines and reports the application of LPS/CP practice in construction across the major construction sectors. Future studies could build on the findings from this work to develop an approach/methodology to improve the current practice.

Purpose: The purpose of this paper is to present the current knowledge surrounding social value (SV) and show how lean approach supports SV realisation in the delivery of construction projects.Design/methodology/approach: A critical literature review was adopted, to gather the current knowledge surrounding SV from mainstream management sciences, construction management and lean literature. A total of 70 studies were critically reviewed.Findings: The study establishes that the current level of awareness on SV is still low and there is a dearth of scholarly publications on SV especially in the construction management literature. The investigation reveals the potentials of lean approach in supporting the delivery of SV on construction projects.Social implications: This study conceptualises the community and the physical environment around where the construction project is executed as customers using lean production approach. It shows that the transformation, flow and value view supports smooth workflow, which enhances the achievement of SV objectives. This creates a new insight into how SV can be realised in construction project delivery.Originality/value: This study extends the on-going debate around the need for SV in construction project delivery and contributes to construction management and lean construction literature on SV. Future studies could build on this to obtain empirical data and develop an approach/method that would support the evidencing of SV delivery on construction projects.

The increasing concern about the environment has led to the necessity of ensuring efficient Construction and Demolition Waste Management (C&DWM) in the built environment. Despite the extensive research on C&DWM, the industry still faces significant challenges, including inefficiencies, high costs, and environmental impacts. Meanwhile, incorporating digital technologies (DTs) has emerged as a way to eradicate the challenges of C&DW. In response to the knowledge gap, in this research, we conducted a systematic literature review (SLR), incorporating bibliometric, text-mining, and content analysis to meet the research objectives. In total, 126 papers were retrieved from the Scopus database and transferred into VOSviewer to conduct the bibliometric analysis. The findings identified seven specific DTs, namely, blockchain, Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), Robotics, Computer Vision (CV), and Building Information modeling (BIM). This study demonstrates that these technologies play a significant role in promoting efficient C&DWM in the construction industry. The study’s implication lies in its potential to guide industry stakeholders and policymakers in promoting the use of DTs and overcoming the barriers to their adoption, thereby facilitating more efficient and sustainable C&DWM practices. Finally, the findings of our research indicate possible future research directions for promoting DTs for C&DWM and eradicating the barriers to efficient implementation.

The global construction industry faces serious safety challenges, characterised by high rates of accidents and fatalities. A systematic review that analysed 95 academic articles from the Scopus and Web of Science databases investigated the current use of digital technologies (DTs) in construction safety management across developed and developing countries. The research discovered that digital technology applications in construction safety primarily focus on developing models and simulations. These technologies are making significant contributions by enhancing worker training, improving risk prediction capabilities, enabling real-time monitoring, facilitating better communication, and supporting more proactive safety interventions. The most frequently utilised digital technologies in this domain include virtual reality (VR), building information modelling (BIM), machine learning, and artificial intelligence (AI). Despite the promising potential of these technologies, their actual implementation remains somewhat limited, especially in developing countries. This study identified critical knowledge gaps, specifically the limited understanding of digital technology trends in construction safety management across different economic contexts, the insufficient research on strategies to increase digital technology adoption in the construction sector, and the need for more comprehensive investigations into how the technology adoption divide can be bridged. This research aimed to facilitate future empirical studies that can advance the understanding of digital technologies and the development of strategies to integrate them more comprehensively into construction safety practices. By providing a detailed overview of current digital technology applications, highlighting research limitations, and suggesting future research directions, this review seeks to contribute to both academic understanding and practical improvements in global construction industry safety.

Purpose: In recent years, there has been a tremendous increase in the number of applicants seeking placements in undergraduate architecture programs. It is important during the selection phase of admission at universities to identify new intakes who possess the capability to succeed. Admission variable (i.e. prior academic achievement) is one of the most important criteria considered during the selection process. This paper aims to investigates the efficacy of using data mining techniques to predict the academic performance of architecture students based on information contained in prior academic achievement.Design/methodology/approach: The input variables, i.e. prior academic achievement, were extracted from students’ academic records. Logistic regression and support vector machine (SVM) are the data mining techniques adopted in this study. The collected data were divided into two parts. The first part was used for training the model, while the other part was used to evaluate the predictive accuracy of the developed models.Findings: The results revealed that SVM model outperformed the logistic regression model in terms of accuracy. Taken together, it is evident that prior academic achievement is a good predictor of academic performance of architecture students.Research limitations/implications: Although the factors affecting academic performance of students are numerous, the present study focuses on the effect of prior academic achievement on academic performance of architecture students.Originality/value: The developed SVM model can be used as a decision-making tool for selecting new intakes into the architecture program at Nigerian universities.

Implementation of the Last Planner System (LPS) has gained prominence in the construction industry, and its influence on the production system seems to be rapid and significant. However, recent studies reveal that application of LPS principles on projects is fragmented. The aim of the current study, therefore, is to develop an approach to support construction stakeholders in implementation of the LPS. Thirty semistructured interviews and three in-depth case studies were conducted with construction stakeholders. The authors developed a nonprescriptive but all-inclusive approach for supporting construction stakeholders in implementation of the LPS on construction projects. This study contributes to knowledge in engineering management because it provides new insight into how to apply the LPS holistically in the management of engineering projects. The study further provides evidence regarding current practice and performance of the LPS in the management of civil engineering projects, as demonstrated in the case studies. Finally, identification of the three levels of support (organizational, project, and external enabler) provides a focal point for construction practitioners to focus on in implementation of the LPS in the management of civil engineering projects.