It is widely acknowledged that the construction industry has a major impact on the environment, both in terms of resource consumption and waste production. The construction industry is responsible for producing a whole variety of different onsite wastes; the amount and type of which depends on factors such as the stage of construction, type of construction work, direct or indirect stakeholders’ design change contribution, and practices throughout the project lifecycle. A number of construction waste minimisation (CWM) techniques and tools are currently available to assist contractors to divert waste away from landfill. However, literature reveals that there are insufficient techniques and tools for reducing construction waste during the design and procurement stages. The last few years saw the emergence of Building Information Modelling (BIM) techniques, which can be adopted to improve sustainable construction performance. BIM is a maturing modelling philosophy, which has been applied to several building-related functions such as visualising designs, automating quantity takeoffs, checking compliance with regulations, and scheduling construction processes. Furthermore, BIM, as a real-time interactive and collaborative communication system, has the potential to help project stakeholders to collaboratively attain waste minimisation for sustainable construction and building throughout design, construction and throughout the lifecycle by improving building construction performance. Hence, this paper, which is part of an ongoing doctoral study, explores the potential application of BIM to design out waste. An in-depth literature review was conducted to provide a foundation for the doctoral study that aims to investigate the use of BIM as a potential platform for building design waste minimisation. The paper explores construction waste origins and causes, current waste reduction practices; examines current industry BIM practices and investigates BIM tools for sustainable project construction and management; and identifies the knowledge gaps in existing literature that pave the way for the subsequent data collection stages