The scale and complexity of projects in the construction sector have grown significantly in recent years. Larger projects typically result in worse performance, including overspending and delays in the calendar.
This research focuses on identifying and analyzing Early Warning
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The scale and complexity of projects in the construction sector have grown significantly in recent years. Larger projects typically result in worse performance, including overspending and delays in the calendar.
This research focuses on identifying and analyzing Early Warning Signals (EWS) to mitigate schedule delays during the design phase of infrastructure projects managed by Sweco, a consultancy company in the Netherlands. The primary aim is to explore how effectively EWS can be used to identify potential issues early and prevent project delays.
The research begins by assessing the current knowledge and application of EWS among project managers, controllers and directors at Sweco. Initial findings indicate that while most participants have a basic understanding of the concept of EWS, they do not actively use or recognize it in their daily workflows. Their awareness of these signals primarily stems from gut feelings and extensive experience rather than systematic identification and application.
To identify the most relevant EWS, the study combines insights from existing literature with practical experiences shared by Sweco experts. This process involves creating a comprehensive list of potential EWS, which is then narrowed down and prioritised based on their perceived impact and likelihood. The prioritisation is similar to a risk assessment method, considering both the probability and potential impact of each signal.
The study examines how to measure data related to the signals and proposes measures on how to handle those EWS better. Some of the suggestions include establishing clear working agreements, balancing formal and informal communication, enhancing collaboration through workshops and visual tools, and leveraging advanced technologies like AI and BIM. The study further emphasises the importance of continuous client communication, internal quality checks, and ensuring appropriate expertise for project tasks.
The research provides a comprehensive framework for identifying, measuring, and managing EWS to improve project outcomes and reduce schedule delays in infrastructure projects. The findings emphasise the need for systematic approaches and technological integration to enhance early warning capabilities and project management practices. By implementing the recommended strategies, Sweco can better anticipate potential issues, make informed decisions, and ultimately deliver projects more efficiently.
Keywords: Early Warning Signals (EWS), schedule delay, design phase, infrastructure projects, project management, measurability, prioritisation