Construction planning and control are crucial for project success. The last planner system (LPS) presents a proactive approach to plan and control production, designed to increase reliability and enhance the make-ready process. Several metrics guide the planning process at the macro level (master and phase scheduling) and the micro level (lookahead and weekly work planning). However, LPS still lacks a mathematical model that can systematically and continuously analyze such metrics, especially to forecast project performance. Moreover, there are no studies on the effect of the fluctuations of lookahead-planning LPS metrics on the metrics at the weekly work plan level. This research, therefore, proposed a new mathematical model using singularity functions, which are types of range-based expressions that track the different paths that each task can follow, from lookahead planning to weekly work planning, and evaluate LPS metrics. To assess project performance, the concept of momentum was introduced as the rate of change in metrics from week to week. Momentum was applied to the Tasks Made Ready (TMR) metric to predict the Percent Plan Complete (PPC). Through machine learning models, results show that momentum can predict PPC with over 93% correlation between actual and predicted PPC values. Data from actual construction execution in the United States were used to validate the proposed model. The contribution of this research lies in (1) conceiving a mathematical model method for project control; and (2) introducing the concept of momentum, which takes the rate of change of any metric into account, incorporated into the LPS for more reliable planning. The methodology proposed in this study can help industry better plan its projects and leverage the concept of momentum to better predict PPC, which is essential for every planning and control process in construction projects.

The Last Planner System (LPS) aims to enhance planning reliability by reducing variability in construction processes. While LPS applications have been explored in construction and detailed design, its application in architectural design remains underrepresented due to its abstract nature. This study addresses this gap by proposing an LPS framework tailored for architectural design, utilizing LPS metrics to assess planning reliability. Key issues hindering formal planning methods’ implementation are identified, and relevant LPS principles are aligned with these challenges, culminating in a conceptual LPS model designed for architectural projects. Building upon the conceptual model, an implementation model was developed and put into practice within an architectural design company in the United States, resulting in measured improvements in planning reliability and responsiveness. Additionally, it unveils hidden challenges associated with emerging tasks, guiding future design process enhancements. This study demonstrates how tracking design planning performance with LPS metrics can promote LPS adoption in architectural design, offering a benchmark for necessary interventions to achieve desired performance in architectural design.

Improvisation is the decision-making process addressing unexpected obstacles in a spontaneous but rational manner. Although undesirable, as it indicates deviation from plans, improvisation is unavoidable in construction to address issues related to unforeseen uncertainties. An adaptive planning system employing improvisation to react rapidly to unplanned events may therefore boost the performance in construction projects. Accordingly, this research aims to predict the outcomes of construction planning processes from an improvisational perspective by better understanding the dynamics of improvisation. It seeks to identify how different variations of improvisational parameters influence the improvisational outcome. This objective is achieved through an agent-based model used to simulate the improvisation practices at the level of planners interacting together. Parameters relating to planners, projects, and problems influencing each planner’s improvisational means are illustrated in the model. The model’s inputs were validated through data from large-sized projects. Linear regression models that predict the results of the improvisational practices were then developed through simulation experiments. Findings regarding the impacts of different types of improvisors on the improvisational outcomes are presented. The contribution of this study lies in enhancing the overall improvisational performance in construction planning to ultimately guide decision makers and planners to better handle uncertainties in projects.

The Last Planner System (LPS) aims to enhance planning reliability by reducing variability in construction processes. While LPS applications have been explored in construction and detailed design, its application in architectural design remains underrepresented due to its abstract nature. This study addresses this gap by proposing an LPS framework tailored for architectural design, utilizing LPS metrics to assess planning reliability. Key issues hindering formal planning methods' implementation are identified, and relevant LPS principles are aligned with these challenges, culminating in a conceptual LPS model designed for architectural projects. Building upon the conceptual model, an implementation model is then developed and put into practice within an architectural design company in the United States, resulting in measured improvements in planning reliability and responsiveness. Additionally, it unveils hidden challenges associated with emerging tasks, guiding future design process enhancements. This study demonstrates how tracking design planning performance with LPS metrics can promote LPS adoption in architectural design, offering a benchmark for necessary interventions to achieve desired performance in architectural design.