Delay is the leading challenge in completing Engineering, Procurement, and Construction (EPC) projects. Delay can cause excess costs, which reduces company profits. The relationship between subcontractors and the main contractor is a critical factor that can support the success of an EPC project. The problematic financial condition of the main contractor can cause delay in payments to subcontractors. This research will set a model that combines the system dynamics and earned value method to describe the impact of subcontractor advance payments on project performance. The system dynamics method is used to model and analyze the impact of interactions between variables affecting project performance, while the earned value method is applied to quantitatively evaluate project performance and forecast schedule and cost outcomes. These two methods are used complementarily to achieve a holistic understanding of project dynamics and to optimize decision-making. The designed model selects the optimum scenario for project time and costs. The developed model comprises project performance, costs, cash flow, and performance forecasting sub-models. The novelty in this research is a new model for optimizing project implementation time and costs, adding payment rate variables to subcontractors and subcontractor performance rates. The designed model can provide additional information to assist project managers in making decisions.

The projects of the IT industry are considered successful when they are completed within the timeline, budget, and client satisfaction on a specific project. Although client relationship is not given much importance in the delay of a project, through several studies it has been seen that the project is delayed in the IT industry due to a lack of awareness about the project to the client. The objective of this study is to inspect the impact of client relationships on project delay. Drawing on stakeholder theory and agency theory, this study investigates how client relationship influences project delay through project awareness and the role of project governance as moderator. A deductive approach of reasoning was used to test the hypotheses formulated under the current research work and proceed by using the quantitative method. This study employed a cross-sectional research design, where data was collected at a specific point in time through a survey strategy. Data was collected from the sample of 288 respondents from the IT companies of Rawalpindi and Islamabad. The data was collected using a convenience sampling technique. The demographics of the respondents were analyzed through the IBM-SPSS software program. The assumptions and the reliability of the model were also tested in SPSS. In this study, it was discovered that effective management of client relationships significantly reduces project delays, with project awareness being a crucial factor in this mitigation process. The results revealed that client relationship was negatively associated with project delay and project awareness. Whereas this linkage was mediated by project awareness. This study concludes that adequate project awareness and fruitful project governance reduce project delays and lead to positive client relationships.

This study aims to identify the causes of delays in public construction projects in Thailand, a developing country. Increasing construction durations lead to higher costs, making it essential to pinpoint the causes of these delays. The research analyzed 30 public construction projects that encountered delays. Delay causes were categorized into four groups: contractor-related, client-related, supervisor-related, and external factors. A questionnaire was used to survey these causes, and the Relative Importance Index (RII) method was employed to prioritize them. The findings revealed that the primary cause of delays was contractor-related financial issues, such as cash flow problems, with an RII of 0.777 and a weighted value of 84.44%. The second most significant cause was labor issues, such as a shortage of workers during the harvest season or festivals, with an RII of 0.773. Additionally, various algorithms were used to compare the Relative Importance Index (RII) and four machine learning methods: Decision Tree (DT), Deep Learning, Neural Network, and Naïve Bayes. The Deep Learning model proved to be the most effective baseline model, achieving a 90.79% accuracy rate in identifying contractor-related financial issues as a cause of construction delays. This was followed by the Neural Network model, which had an accuracy rate of 90.26%. The Decision Tree model had an accuracy rate of 85.26%. The RII values ranged from 68.68% for the Naïve Bayes model to 77.70% for the highest RII model. The research results indicate that contractor financial liquidity and costs significantly impact construction operations, which public agencies must consider. Additionally, the availability of contractor labor is crucial for the continuity of projects. The accuracy and reliability of the data obtained using advanced data mining techniques demonstrate the effectiveness of these results. This can be efficiently utilized by stakeholders involved in construction projects in Thailand to enhance construction project management.