Continuous Improvement of Takt Production With Data-Driven Knowledge Management Approach

Abstract

This study investigates how data-based continuous improvement could be applied in construction projects utilizing takt production. The purpose is to define a process model that will guide how such a continuous improvement system can be created in an organization utilizing takt production methods, and how the system can then be improved. This research follows design science approach to highlight the practicality of the solution. Research consists of diagnosis, process model creation, validation of the process model, discussion, and conclusion. Diagnosis is performed with a literature review and empirical research, including interviews and observations of current practices in a case company. Validation is performed by collecting external feedback and by organizing internal interviews. The findings indicate that the created process model provides a system that can be used to improve the takt production process with data, and that the process can be supported by also handling tacit knowledge. A defined learning system will help in tackling the current barriers facing the construction industry related to inefficient data processing and unclear knowledge management. As the system utilizes the terminology and theory of takt production, it is proposed that the system can be expanded to other projects and construction functions with further research.