Development of a framework for identifying information bottlenecks and evaluating different data capture technologies to support proactive productivity management

Construction companies need to continuously improve construction productivity to stay competitive. One of the main means to achieve construction productivity improvement is to reduce the uncertainty about, and to increase the command and control of, construction processes. Most of the current productivity management practices assess and control the productivity in a reactive manner, which take corrective actions only after the problems (such as when there are schedule delays or lower than expected production rates) are identified. Instead of relying on reactive responses to problems, we propose to proactively prevent problems by identifying and removing information bottlenecks before construction activities start. We consider assumptions as a major source to identify information bottlenecks that are associated with construction processes. In order to support proactive construction productivity management, we have developed a framework to capture assumptions made during construction processes and to reduce the uncertainty through proactive verification of the assumptions. This paper presents the vision for such a framework, and demonstrates the nature and required functionalities of the framework.