Search results

Megaproject supply chains involve multiple layers of stakeholders, leading to complex relationships and risks. The role of social interactions within these networks is unexplored. Therefore, an analysis of construction supply chain risk management from the perspective of social networks is essential to identify related stakeholders, their relationships and the social network risk factors.

About 65 risk factors, identified from literature and interviews, informed the development of a questionnaire for the study. Online questionnaires administered in Ghana and South Africa produced 120 valid responses. Feedback from the responses was ranked and assessed to determine the overall social network risk levels using the Normalised Mean and Fuzzy synthesis analysis methods.

About 24 risk factors were identified and classified into six groups: Client/Consultant-related, Community-related, Government-related, Industry Perception-related, Supplier-related and Stakeholder Opportunism. The top five social network risks identified include bribery, supplier monopoly, incomplete design teams, poor communication and lack of collaboration.

The study provides detailed evaluations of social network risks in Africa, and the findings will help in developing strategies to mitigate supply chain disruptions caused by these challenges.

This study contributes to the literature on supply chain risk management by offering context-specific insights into the social network perspective of megaprojects in Africa, which differs from those in developed countries.

Executive sponsors play a significant role in the success of megaprojects which, in turn, affect national economies and millions of people. However, the literature on the requisite attributes of project sponsors on megaprojects is still sparse. The purpose of the paper is to provide guidelines to company boards and executives who are tasked to appoint suitable executive sponsors to megaprojects. Thus, the paper contributes to the sparse literature on megaproject sponsors.

A total of 26 senior managers, with experience in megaprojects ranging from 8 to 15 years – and who were involved in 6 recent megaprojects with a combined value of US$13.75bn – were interviewed on the attributes of megaproject sponsors. Transcriptions of semi-structured, open-ended interviews were analysed with computer-assisted qualitative data analysis software (CAQDAS).

The study identified the most essential attribute as appropriate seniority, being empowered and accountable, with appropriate seniority, being empowered and accountable, with apposite credibility and with both personal and positional power. The study also uncovered 13 attributes – all components of “competence” – which have not previously been explicitly identified in literature as elements of sponsor “competence”.

In the current study guidelines are provided for the selection and appointment of appropriate megaproject sponsors.

Construction projects generate large volumes of data which can be used for better management of projects. In this paper, key project data is manually extracted from project site meeting minutes. Knowledge discovery technologies are then used to predict the final project duration of active projects.

Project planning and effective leadership/governance were identified from literature as the most significant factors that impact the duration of projects. These factors were hence considered as the main features for a data mining process. Items supporting these factors were extracted from site meeting minutes to create a database of 27 civil engineering projects executed over the last ten years. Data mining algorithms were used to predict from this data whether or not an active project will be completed on time.

The research showed that information from project site meetings can be used to predict final project duration of active projects with accuracy of above 80% when using random forest algorithms from Orange and RapidMiner data mining applications. The value of data to predict project duration from project site meeting minutes is demonstrated but it only becomes practically useable if the format of minutes is suitably standardised.

Some of the data mining algorithms provided accuracies of above 80% in predicting final project duration and proved the value of project data from site meeting minutes. The random forest algorithms are particularly suited to this type of data. The factors with the highest impact on the prediction of the project duration are those related to the progress of the project.

This study for the first time shows that data from site meeting minutes of past and current projects can be used to make accurate predictions of final project duration of active projects and serve as a project management tool to activate remedial measures.

Due to the uniqueness of individual construction projects, identifying the dominant risk factors is needed for risk mitigation in ongoing and future projects. This study aims to identify the dominant construction supply chain risk (CSCR) factors, based on studies conducted between 2002 and 2022.

The study adopts the preferred reporting items for systematic reviews and meta-analysis (PRISMA) procedure to identify, screen and select relevant articles in order to provide a bibliography and annotation of the prevalent risks in the supply chains. A descriptive analysis of the findings then follows.

The study’s findings have highlighted the three most prevalent risks in the construction supply chain (poor communication across project teams, changes in foreign currency rate, unfavorable climate conditions) as reported in literature, that project teams need to pay closer attention to and take proactive steps to mitigate.

Due to limitations imposed by the chosen research methodology, tools, time frame and article availability, the study was unable to examine all CSCR-related papers.

The results will serve as a useful roadmap for risk/supply chain managers in the construction industry to take strategically proactive steps towards allocating resources for CSCR mitigation efforts.

Context-specific research on the impact of social and cultural risks on the construction supply chain would be beneficial, due to emerging social network risk factors and the complex socio-cultural settings.

There is presently no study that has reviewed extant studies to identify and compile the dominant risk factors (DRFs) associated with the supply chain of construction projects for ranking in the supply chain risk management process.