# Systematic Review on the Use of CCPM in Project Management: Empirical Applications and Trends
## 3 Results
Based on empirical data, the results optimized the total structural design time from 432 to 293 days, resulting in a 32.17% reduction in project time [8]. In the next publication [42], a gas extraction construction project, supported by modeling and simulation, applied a two-stage multi-objective approach with stochastic data to determine the size of time buffers in projects. The results indicate that the proposed robust buffer sizing method yields a more stable plan compared to traditional methods, with a shorter project lead time. The next study evaluated [41], supported by modeling and simulation, a data-driven and predictive buffer sizing approach is proposed in the project planning phase. This approach employs a full-factorial design of experiments and a Monte Carlo simulation to generate the required dataset. Then, it utilizes support vector regression to train the project buffer prediction model. The parameters of the support vector regression are adjusted using grid search and cross-validation. The experimental results show that the developed approach is competitive compared to classical project buffer sizing methods. In most cases, this data-driven approach outperforms the other four classical methods because it considers only one factor in calculating the buffer size, time uncertainty, resource constraint, and network complexity, whereas the presented approach considers additional factors related to the network topology. In the next paper analyzed [40], an attempt was made to solve problems, such as delays in project duration caused by irrational buffer zone configuration. A Critical Current damping zone configuration method is proposed based on fragility theory. With the support of modeling and simulation for application in civil construction projects, entropy models and models for modifying design buffers and feed buffers, based on the Mean Square Error Method, were used. This method can reduce the construction period and is effective and practical compared to the other three buffer calculation methods. The results offer a novel perspective on buffer configurations, drawing on existing CCPM methods. In the twenty-fourth study [39], project management practices in public institutions are suggested through a comparative analysis of the Critical Path and Critical Chain methods, supported by modeling and simulation, to critically highlight the reasons why the use of CCPM is indispensable for the scheduling and monitoring of public works by public institutions in Greece. The methodology is exemplified using empirical data from 27 infrastructure projects that delivered 300 km of highway, 46 km of road tunnels, and 20 km of bridges built for the Egnatia highway in northern Greece from 2002 to 2012. The results confirm that CCPM can outperform the traditional CPM method by achieving shorter construction times and accurately allocating resources early. Indirect costs were reduced due to a shorter project time, and the more effective allocation of these resources also reduced direct costs. In 2022, a study was published [9], and the objective was to minimize the cost and completion time of the project. Based on the problem's constraints, a non-linear mathematical model was proposed. To carry out the modeling, internal and external sourcesof uncertainty were considered simultaneously during the buffer estimation process. To identify the project's external risks, the Failure Mode and Effects Analysis (FMEA) technique and the Lognormal Distribution were used to estimate the duration of activities as internal risk resources. The results from empirical data indicate that the proposed buffer estimation method outperforms the traditional method. In the next research [45], supported by simulation and validated with empirical data, the aim was to propose a new integrated proactive-reactive solution approach, based on CCPM, to proactively generate a robust and reliable baseline schedule for the Resource-Constrained Project Scheduling Problem (RCPSP) class under uncertainty.