# Systematic Review on the Use of CCPM in Project Management: Empirical Applications and Trends
## 4 Discussion  
The analysis of Table 2 shows a higher concentration of publications in 2019, with 11 articles reviewed. However, it is not possible to identify the reason for this peak, as it may be attributed to multiple factors, such as growing academic interest in the topic or a coincidence in the publication cycles of the selected databases. Overall, the studies are relatively well distributed between 2014 and 2025, indicating sustained academic production on CCPM over the past decade. This distribution reinforces the continued relevance of the topic, with methodological advances and new proposals emerging over time, particularly since 2019, when integrated approaches involving emerging technologies, such as artificial intelligence and advanced simulations, have become more prominent. Table 3, which analyzes the articles, reveals a significant methodological diversity, with a clear predominance of modeling and simulation approaches, applied in 41 studies (approximately 66% of the total). This prevalence indicates a technical and quantitative orientation in the recent CCPM literature, focused on evaluating model performance and algorithms in controlled environments. Case studies represent the second most frequent method, with thirteen publications (21%), followed by theoretical studies (five articles), and individual occurrences of Design Science Research, action research, and surveys (each accounting for about 2-3% of the sample). This distribution highlights a gap in more qualitative and empirically grounded investigations into the real-world applications of CCPM. Regarding application domains, there is a strong concentration in the construction industry, with 10 articles. This is followed by information technology (four articles), the energy industry (three articles), and manufacturing (five articles). However, 28 of the studies analyzed (approximately 45%) do not specify the application context, which limits the understanding of CCPM's adaptability to different organizational settings. These findings underscore the importance of research that examines the implementation of CCPM in underrepresented sectors, such as Engineer-to-Order (ETO) environments and service-based organizations. A deeper analysis of Table 4 reveals three main objectives underlying the studies assessed: reducing project time (twenty-nine studies), reducing risk and monitoring (thirty studies), and reducing costs (three studies). These goals reflect the core promises of the CCPM method: improving delivery times, mitigating uncertainties, and optimizing resource utilization. Time reduction is strongly associated with applications in construction, manufacturing, and IT projects. In these sectors, reducing project duration has a significant impact, leading to productivity gains, improved resource efficiency, and enhanced schedule reliability. Risk reduction, in turn, is a primary focus in industries characterized by high variability and uncertainty, such as the energy and large infrastructure sectors. These studies often employ buffer design, predictive algorithms, or reliability-based modeling to enhance the robustness of project schedules against unforeseen disruptions. Cost reduction, though less frequently the primary objective, appears in more sophisticated proposals that integrate CCPM with cash flow logic or multi-objective optimization algorithms, especially in engineering and construction projects. When associating objectives with domains of application, construction emerges as the most explored sector, particularly in terms of time and risk management, due to its high sensitivity to delays and sequencing issues. IT projects often emphasize flexibility and deadline compliance by integrating CCPM with agile methods. Manufacturing, though less explored, focuses primarily on improving temporal performance in complex production environments. In the group of studies labeled \"no application\", nearly half of the sample refers to generic CCPM models tested exclusively through theoretical simulations or fictitious data.