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Compressing the schedule by using overlapping activities is a commonly adopted approach for accelerating projects. However, this approach might channel a variety of risks into the construction processes. Risks imply waste; still, evaluating the effects of using overlapping activities on schedule quality has been a looming gap in construction research. Therefore, this paper aims to study the quality of overlapping in terms of emerging waste and to demarcate the boundaries of the overlapping envelope.

This study presents a method for assessing the consequences of implementing overlapping activities in a schedule on two types of waste namely waiting time and variation gap. A critical path method (CPM) network including eleven activities is modeled stochastically where the durations of individual activities are sampled as beta-distributions. Using program evaluation and review technique (PERT) assumptions to calculate the schedule dates, the network is simulated for various amounts of overlapping and the corresponding waste is quantified each time.

Results show that not only the returns on overlapping are diminishing after a certain overlap percentage, but also waste in the production system increases. Particularly, results reveal that compressing the schedule leads to a decrease in variation gaps, but at the same time, it leads to a larger increase in waiting times, which creates more waste.

The presented study shows through simulation how overlapping activities affects productivity by identifying wastes. It shows that despite the apparent gains, overlaps should be used with caution, and while considering the side-effects of increased waste which introduces a need for increased managerial awareness.

This study aims to enhance productivity monitoring and control in the construction industry by integrating data-driven analytics with expert insights.

A novel framework combines expert knowledge and data analysis to identify productivity trends and devise improvement strategies. A machine learning model predicts productivity ranges using historical data and project-specific factors’ evaluated by surveys, supported by a warning dashboard for proactive decision-making.

The findings reveal that integrating expert insights with data analytics significantly enhances the ability to monitor and control productivity, leading to proactive strategies for construction stakeholders. The machine learning model demonstrates robust accuracy in forecasting productivity ranges, allowing for early identification of potential issues. The dashboard system proves invaluable, offering semi-real-time alerts and facilitating swift action to prevent productivity lapses. These results highlight the effectiveness of the proposed approach in detecting trends, predicting outcomes and enabling timely interventions, thereby contributing to the overall productivity improvement of construction projects.

There are also limitations to consider, including potential data availability, constraints in the expert pool, implementation challenges and the need for long-term evaluation; these factors should be considered when interpreting the study’s findings and applying the proposed framework to construction projects. Future research can focus on expanding the application of this framework to different types of construction projects and evaluating its scalability.

This study introduces a framework with a warning dashboard for early detection of issues, combining expert insights and data analysis for improved project outcomes. This research suggests a shift toward more expert, data-driven, insightful decision-making in construction, aiming for enhanced performance and reduced disruptions. An important implication of this research is the need to balance digital tools and expert judgment. Project managers are advised to use a holistic strategy that ensures informed and comprehensive decision-making.

This research introduces a unique methodology that blends traditional expertise with modern analytics to address construction productivity challenges. It offers a practical solution for stakeholders to enhance decision-making, resource allocation and project planning, marking a significant contribution to construction management literature.