The building construction industry faces challenges, such as increasing project complexity and scope requirements, but shorter deadlines. Additionally, economic uncertainty and rising business competition with a subsequent decrease in profit margins for the industry demands the development of new approaches to construction management. However, the building construction sector relies on practices based on intuition and experience, overlooking the dynamics of its production system. Furthermore, researchers maintain that the construction industry has no history of the application of mathematical approaches to model and manage production. Much work has been carried out on how manufacturing practices apply to construction projects, mostly lean principles. Nevertheless, there has been little research to understand the fundamental mechanisms of production in construction. This study develops an in-depth literature review to examine the existing knowledge about production models and their characteristics in order to establish a foundation for dynamic production systems management in construction. As a result, a theoretical framework is proposed, which will be instrumental in the future development of mathematical production models aimed at predicting the performance and behaviour of dynamic project-based systems in construction.

Construction frequently appears at the bottom of productivity charts with decreasing indexes of productivity over the years. Lack of innovation and delayed adoption, informal processes or insufficient rigor and consistency in process execution, insufficient knowledge transfer from project to project, weak project monitoring, little crossfunctional cooperation, little collaboration with suppliers, conservative company culture, and a shortage of young talent and people development are usual issues. Whereas work has been carried out on information technology and automation in construction their application is isolated without an interconnected information flow. This paper suggests a framework to address production issues on construction by implementing an integrated automatic supervisory control and data acquisition for management and operations. The system is divided into planning, monitoring, controlling, and executing groups clustering technologies to track both the project product and production. This research stands on the four pillars of manufacturing knowledge and lean production (production processes, production management, equipment/tool design, and automated systems and control). The framework offers benefits such as increased information flow, detection and prevention of overburdening equipment or labor (Muri-無理) and production unevenness (Mura-斑), reduction of waste (Muda-無駄), evidential and continuous process standardization and improvement, reuse and abstraction of project information across endeavors.

Mathematical models have historically enabled a thorough understanding of production mechanisms in manufacturing supporting actions and laws to improve fabrication and building performance. Although much work has been done to date on modeling of production processes, more studies need to be conducted to understand the dynamics of these processes and develop mathematical relationships and laws that can reliably predict and manage project-driven production systems in construction. The purpose of this research is to explore the hypothesis that production processes in construction can be represented as dynamic systems in which a productivity function describes the time dependence of the systems’ output–input relation. By considering the transient state, productivity function models were more accurate in describing the process dynamics than first-degree polynomials and the arithmetic mean, confirming the hypothesis. The contribution of this research to the body of knowledge is a new model that considers different states of project-driven processes.

Despite being inaccurate, linear models are often used by construction managers to describe production, which limits the development and application of a production theory. The objective of this study is to formulate and test new equations comprehending the non-steady-state conditions of repetitive project-driven processes in construction to calculate cycle time and capacity based on productivity function models. The algebraic calculations were developed from Little’s Law to estimate cycle times and capacity and then tested on productivity function models of 11 construction processes (two cases arranged in a supply chain). It has been reaffirmed that the transient time negatively impacts process productivity. The transient time and theoretical average cycle time showed a proportional relationship to the average cycle times of the samples, resulting in a benchmarking ranking close to the one measured. This study contributes to the body of knowledge by introducing equations for capacity and cycle time based on a process productivity function model that can be used on processes in any state. It can be an alternative to three-point estimation, for instance.

Based on the knowledge of dynamic systems, the shorter the transient response, or the faster a system reaches the steady-state after the introduction of the change, the smaller will be the output variability. In lean manufacturing, the principle of reducing set-up times has the same purpose: reduce the transient time and improve production flow. Analogously, the analysis of the transient response of project-driven systems may provide crucial information about how fast these systems react to a change and how that change affects their production output. Although some studies have investigated flow variability in projects, few have looked at variability from the perspective that the transient state represents the changeovers on project-driven production systems and how the transient state affects the process' flow variability. The purpose of this study is to investigate the effect of changes in project-driven production systems from a conceptual point of view, furthermore, measuring and correlating the transient response of five cases to their flow variability. Results showed a proportional relationship between the percentile transient time and flow variability of a process. That means that the quicker the production system reacts to change; the less the distress in the production output, consequently, lower levels of flow variability. As practical implications, lean practices focusing on reducing set-up times (transient time) can have their effects measured on project-driven production flow.