Matters affecting construction project-level planning effectiveness: A literature review

Construction project success is often measured based on the adherence to time; cost and quality targets; with Clients and Contractors constantly seeking improvements across these metrics. However, the time predictability of construction projects remains poor; with annually measured 'industry-level' KPIs showing no signs of significant improvement. Access to technological advancements, such as 4D modelling; Artificial Intelligence (AI); and more recently the renewed interest in Off-Site Manufacture (OSM) has indicated opportunities to improve time-predictability; but overall 'industry-level' time performance remains unsatisfactory. As an aspect of time-predictability; insufficient attention is presently focused on exploring 'planning effectiveness'; therefore, the main aim of this work was to review time-predictability and project planning effectiveness at 'project-' and 'activity-' levels via an initial review of subject literature. Following this; a conceptual framework was developed highlighting the key aspects associated with planning effectiveness. These include project environment matters such as complexity and uncertainty; human/cognitive matters such as optimism bias and Parkinson’s Law; and the application of available planning techniques or technologies such as Critical Path Method; Last Planner System; Critical Chain Planning; AI and 4D modelling. Whilst 'alternative' planning techniques have shown the potential to improve time-performance; research identifies industry awareness and application of these techniques remains low. As a result of this work, it is considered that planning effectiveness and time predictability can be improved by increasing industry awareness of the constructs identified herein; allowing for the subsequent adoption of available and emerging planning techniques and/or technologies. Subsequent research will explore this; in practice at activity-level; with data obtained from a range of construction schemes to model improvements.