# Systematic Review on the Use of CCPM in Project Management: Empirical Applications and Trends
## 3 Results
The next article under review applies the Critical Chain methodology to reduce the duration of a heavy maintenance C-check on an Airbus A320. The case study, conducted in a real MRO, identified problems such as task oversizing, multitasking, and a lack of strategic buffers. By adopting CCPM, maintenance duration was reduced from 11 to 6 days (an 8.92% reduction) using buffers, reorganizing the Critical Chain, and eliminating multitasking. The model demonstrated potential to significantly improve efficiency in aircraft maintenance [58]. The study reviewed reports on the implementation of the CCPM method in a Spanish manufacturing company, with a focus on new product development projects. The introduction of CCPM has generated significant improvements, including increased resource visibility, reduced customer uncertainty, and greater strategic alignment. After implementation, a reduction of up to 20% in task duration was observed, with peaks of 50%, without changing the number of employees. The shift to integrated, rather than segmented, resource management was crucial in achieving greater reliability in meeting deadlines [59]. The following article proposes a new buffer sizing method based on resource constraints, using fuzzy theory to consider aspects such as substitutability, quality, and peak resource usage. The simulation results (500 runs per project) showed that the proposed method outperformed the C&PM and RSEM methods in terms of duration and cost, especially under high-risk conditions. The model reduced uncertainty, increased on-time completion rates, and demonstrated greater robustness for small, medium, and large projects [60]. The next paper proposes the Post Density Method (PDM) for dimensioning feeding buffers, incorporating factors such as precedence density, resource constraints, and task uncertainty. Two hundred thousand simulations were performed on 100 projects from the Kolisch-Hartman database. The PDM demonstrated a smaller average buffer size, a higher consumption rate, and a lower proportion of deadline violations in high-risk environments, outperforming the C&PM, RSEM, APRT, and APD methods. The approach demonstrated greater efficiency in protecting the Critical Chain in contexts with high variability and complexity [61]. The next study investigates the application of CCPM in product development and portfolio management at a Brazilian aircraft manufacturer. Field research revealed that the adoption of CCPM resulted in greater schedule predictability, an increase in delivery flow (from 284 to 414 aircraft between 2010 and 2012), and better utilization of engineering capacity. CCPM made significant contributions to project prioritization, reduced multitasking, and formalized commitments. Although successful, implementation remains uneven across the company [62]. The paper proposes a dynamic schedule monitoring model that allocates buffers per phase, considering the network complexity and the duration of each phase. The method defines monitoring trigger points for each phase, enabling more accurate corrective actions and reducing the likelihood of false alarms. Simulations with the \"Crystal Tools Development\" project demonstrated that the approach reduces costs and time (up to 84% of the cost and 75% of the time of traditional methods), with a 98.8% probability of on-time completion. The model overcomes \"student syndrome\" and reduces unnecessary corrective actions [63]. The next study assesses the effectiveness of CCPM in mitigating delays across various levels of schedule network complexity. Using two networks from the PSPLIB database, the model combines CPM, graph theory, buffer allocation, and Monte Carlo simulation with probabilistic durations (triangular and beta). The results show that, in more complex networks, the protection provided by buffers (PB and FB) is significantly reduced, with a high rate of deadline violations. CCPM demonstrated unsatisfactory performance in delay protection, highlighting practical limitations of the approach in complex environments [64].