Systematic Review on the Use of CCPM in Project Management: Empirical Applications and Trends

Abstract

This study aims to critically analyze the theoretical and practical contributions of recent literature on the Critical Chain Project Management (CCPM) method in multi-project environments. To this end, a systematic literature review (SLR) was conducted based on 62 studies indexed in the Scopus and Web of Science databases between 2014 and 2025. The articles were analyzed in terms of application domains, employed methods, obtained results, and proposed integrations with other approaches. Most studies used modeling and simulation, focusing on time reduction, risk mitigation, and cost optimization. A growing trend has been identified toward integrating CCPM with methodologies, such as Scrum, BIM, Lean Construction, Fuzzy FMEA, and predictive algorithms, thereby broadening its applicability in high-complexity scenarios. However, a significant gap remains in empirical studies applied to Engineer-to-Order (ETO) systems and service-based organizations, which are characterized by high customization, variability, and interdependence of resources. The research is justified by the need to consolidate accumulated knowledge on CCPM and to guide future investigations toward underexplored sectors. The findings strengthen the theoretical robustness of the method while indicating concrete opportunities for empirical validation in real-world organizational settings.

1. Introduction

Project execution is a continuous practice in various organizations, supporting the implementation of organizational strategy as well as the development and maintenance of competitiveness. A project is a temporary endeavor designed to create a unique product, service, or outcome. Project management involves implementing knowledge, skills, tools, and techniques into project activities to satisfy requirements, and uncertainty is one reason it is distinguished as a separate field of expertise. Project duration and success are influenced by how uncertainty is managed, particularly through effective risk management. According to some studies, traditional project management methods result in only 44% of projects being completed on time, a 70% decrease in the scope of planned deliveries, and the rest ending up unrealized [1]. Traditional project management methods often lack effective control and monitoring mechanisms, which leads to project failures. A lack of real-time data visibility and reliance on manual methods can result in delays, miscommunication, and inefficiencies [2].

Several factors, including labor, materials, equipment, site characteristics, management, finances, economic conditions, and work accidents, among others, can generally cause delays in project execution [3,4]. The condition generated by delays and difficulties in managing resources generates the need for a more effective project management strategy [5]. Recent studies have highlighted the potential for integrating CCPM with emerging methodologies such as BIM, Lean Construction, and predictive tools based on artificial intelligence, thereby expanding its scope of application [6,7,8,9,10,11].

In response to such challenges, in 1997, Eliyahu M. Goldratt proposed a method based on the foundations of the Theory of Constraints (TOC), called Critical Chain Project Management (CCPM) [12]. The TOC method is used to manage project uncertainties, structure time buffers to absorb variability in task execution, and manage resources. CCPM is used in various types of industries and businesses, such as power plant projects [13], the construction industry [5,14], manufacturing [15], the oil and gas energy industry [16], and information technology projects [17], among others [18].

Given the above, the following research question arises: What are the main applications of CCPM in the literature, and what are the main results? Therefore, this article aims to critically analyze the theoretical and practical contributions of the recent literature on CCPM in multi-project environments through a systematic review. The study seeks to identify methodological trends, application gaps, and potential directions for future research. To carry out this research, a systematic literature review was carried out on this approach to managing projects that are still young (less than 30 years old) and on the application of the CCPM technique with other tools, methods, and approaches, such as the combined application of Scrum and CCPM [19], joint application of Building Information Modeling (BIM) tools and CCPM [6], organization and management of multi-project activities and resources [20], development of new products and management of their portfolio [21,22], project management, considering rework [23], demonstrating the flexibility and quality of the CCPM method. The results highlighted research gaps and opportunities for future studies, such as applications for manufacturing companies operating under the Engineer-to-Order (ETO) production system, as well as service companies. Engineer-to-Order (ETO) environments are characterized by high product customization, dynamic engineering requirements, and unpredictable workflows, making conventional project control techniques less effective. These attributes generate intense scheduling variability and resource conflicts, which CCPM may help mitigate by prioritizing constraints and buffer management [24].

This article contains the following sections: methodology, results, discussion, and final considerations.

2. Materials and Methods

The method chosen was a systematic literature review (SLR). To carry out the SRL, we opted for the method proposed by Kahn et al. [25], structured into five stages: framing the question; identifying relevant studies; assessing the quality of the studies; summarizing the evidence; and interpreting the results.

The question presented addresses the CCPM applications available in the literature and their main results. The RSL was carried out on the Scopus and Web of Science databases to identify relevant studies on the subject. To find the most up-to-date information on the subject, publications of articles, proceedings, and texts in English from 2014 to 2025 were considered, aimed at ensuring the representation of contemporary trends and avoiding the inclusion of potentially outdated studies in the current scenario of multi-project management. The following search terms were used: Critical Chain Project Management; CCPM; Critical Chain; Theory of Constraints; TOC; project management; project.

The combined search terms are shown as follows: (“Critical Chain Project Management” OR CCPM OR “Critical Chain” OR “Theory of Constraints” OR TOC) AND (“Project Management” OR Project). The following exclusion criteria were then applied:

(a)

Repeated articles from different databases.

(b)

Articles without at least one of the following terms in the title were excluded: Critical Chain Project Management; CCPM; Critical Chain; TOC; Theory of Constraints; project management; project(s); scheduling; constraints.

(c)

Articles not excluded by the previous criteria were read in full. Papers unrelated to the Critical Chain and project management were excluded.

The texts that were not excluded were read in full to understand the type of business in which the concepts were applied, the focus of the study, the research method used, the main results, and contributions. Finally, an attempt was made to identify research gaps for future applications.

3. Results

After applying the combined search terms in the Scopus and Web of Science databases, 1185 articles were retrieved (717 in Scopus and 468 in Web of Science), of which 269 were duplicated and appeared in both databases, leaving 916 articles to review titles and apply the other exclusion criteria. The results of using the exclusion criteria are shown in Figure 1.

Of the 62 texts assessed qualitatively, 50 were journal articles and 12 were conference proceedings.

Table 1. List of the authors, the research method applied, and the objective of the study.

Author and Year of Publication	Application	Method	Study Objective
Makarenko et al., 2019 [19]	IT projects	Modeling and Simulation	Risk reduction and monitoring
Husin, 2019 [6]	Construction	Case Study	Reduction in project time
Cooper Ordonez et al., 2019 [20]	Manufacturing industry	Action Research	Reduction in project time
Tenera & Rosas, 2019 [26]	No application	Theoretical Study	Risk reduction and monitoring
Luiz et al., 2019 [21]	Manufacturing industry	Survey	Reduction in project time
Moradi & Shadrokh, 2019 [27]	No application	Modeling and Simulation	Reduction in project time
Ma et al., 2019 [28]	No application	Theoretical Study	Risk reduction and monitoring
Salama & Moselhi, 2019 [29]	No application	Modeling and Simulation	Cost reduction
Huihua et al., 2019 [17]	IT projects	Modeling and Simulation	Reduction in project time
Hu et al., 2019 [30]	No application	Modeling and Simulation	Risk reduction and monitoring
Polonski, 2019 [31]	No application	Modeling and Simulation	Reduction in project time
Zarghami et al., 2020 [32]	No application	Modeling and Simulation	Risk reduction and monitoring
Sembiring et al., 2020 [33]	Construction	Modeling and Simulation	Reduction in project time
Ariyanti et al., 2021 [7]	Construction	Design Science Research	Risk reduction and monitoring
Putra et al., 2021 [34]	No application	Modeling and Simulation	Reduction in project time
Sinaga et al., 2021 [8]	No application	Modeling and Simulation	Risk reduction and monitoring
Sarkar et al., 2021 [35]	No application	Modeling and Simulation	Reduction in project time
She et al., 2021 [36]	Construction	Case Study	Cost reduction
Salama et al., 2021 [37]	Energy industry	Modeling and Simulation	Reduction in project time
Ma et al., 2021 [23]	No application	Modeling and Simulation	Reduction in project time
Kulejewski et al., 2021 [38]	Construction	Modeling and Simulation	Reduction in project time
Asgari, 2022 [9]	Construction	Modeling and Simulation	Reduction in project time
Petroutsatou, 2022 [39]	No application	Modeling and Simulation	Reduction in project time
Peng & Peng, 2022 [40]	Energy industry	Modeling and Simulation	Risk reduction and monitoring
Li et al., 2022 [41]	No application	Modeling and Simulation	Risk reduction and monitoring
Ansari et al., 2022 [42]	No application	Modeling and Simulation	Reduction in project time
Anastasiu et al., 2023 [43]	No application	Modeling and Simulation	Risk reduction and monitoring
Zhang & Han, 2023 [44]	Energy industry	Case Study	Risk reduction and monitoring
Peng et al., 2023 [45]	Construction	Modeling and Simulation	Risk reduction and monitoring
Marek & Łapuńka, 2024 [46]	Construction	Modeling and Simulation	Risk reduction and monitoring
Bahnas et al., 2024 [5]	Construction	Modeling and Simulation	Reduction in project time
Mohamed et al., 2025 [47]	Construction	Case Study	Reduction in project time
Zhang et al. [48]	No application	Modeling and Simulation	Reduction in project time
Hu et al. [49]	No application	Modeling and Simulation	Risk reduction and monitoring
Wang et al. [50]	No application	Modeling and Simulation	Reduction in project time
Amiri et al. [51]	Construction	Modeling and Simulation	Cost reduction
Ghoddousi et al. [10]	Construction	Modeling and Simulation	Risk reduction and monitoring
Hutanu et al. [52]	IT projects	Theoretical Study	Reduction in project time
Petrochenko et al. [53]	Construction	Modeling and Simulation	Risk reduction and monitoring
Ghaffari et al. [54]	No application	Modeling and Simulation	Reduction in project time
Wang et al. [55]	No application	Modeling and Simulation	Risk reduction and monitoring
Cheng and Liang [56]	IT projects	Case Study	Reduction in project time
Mansoorzadeh et al. [57]	Energy industry	Case Study	Risk reduction and monitoring
Kulkarni et al. [58]	Aircraft maintenance	Case Study	Reduction in project time
Apaolaza and Lizarralde [59]	Manufacturing industry	Case Study	Reduction in project time
Zhang et al. [60]	No application	Modeling and Simulation	Risk reduction and monitoring
Iranmanesh et al. [61]	No application	Modeling and Simulation	Risk reduction and monitoring
Souza and Moraes [62]	Aircraft industry	Case Study	Reduction in project time
Zhang et al. [63]	No application	Modeling and Simulation	Risk reduction and monitoring
Su et al. [64]	Construction	Modeling and Simulation	Risk reduction and monitoring
Yang et al. [65]	No application	Modeling and Simulation	Risk reduction and monitoring
Ma et al. [66]	Construction	Modeling and Simulation	Risk reduction and monitoring
Gwiazda et al. [67]	Manufacturing industry	Case Study	Reduction in project time
Paprocka et al. [68]	Manufacturing industry	Case Study	Reduction in project time
Hong-Yi et al. [69]	No application	Theoretical Study	Risk reduction and monitoring
Hu et al. [70]	No application	Modeling and Simulation	Risk reduction and monitoring
Salama et al. [71]	Construction	Modeling and Simulation	Reduction in project time
Ansari et al. [72]	Construction	Modeling and Simulation	Risk reduction and monitoring
Zhang and Geng [73]	No application	Theoretical Study	Risk reduction and monitoring
Ghazvini et al. [74]	Sustainable projects	Case Study	Risk reduction and monitoring
Jo et al. [16]	Oil and gas projects	Case Study	Reduction in project time
Zhang et al. [75]	No application	Modeling and Simulation	Risk reduction and monitoring

presents the list of authors, the research methods used, and the objectives of each study.

Table 2. Year of publication of the qualitatively analyzed articles.

Year of Publication	Number of Texts Evaluated
2014	5
2015	2
2016	8
2017	8
2018	7
2019	11
2020	2
2021	8
2022	5
2023	3
2024	2
2025	1

shows the number of articles published per year.

Table 3. Research methods used in the articles analyzed.

Research Method	Quantity
Modeling and simulation	41
Case study	13
Theoretical study	5
Design science research	1
Action research	1
Survey	1

presents the research methods employed in the analyzed articles.

Most of the articles analyzed present developed and simulated models, and the majority of the studies simulate the results of empirical cases generated by these models to assess their quality and robustness.

Table 4. Relationship between the application area and study objective.

Application	Study Objective
	Cost Reduction	Reduction in Project Time	Risk Reduction and Monitoring
Construction	1	6	3
Road construction	-	-	1
Energy industry	-	1	2
IT projects	-	3	1
Manufacturing industry	-	5	-
No application	1	10	17
Aircraft maintenance	-	1	-
Oil and gas projects	-	1	-
Sustainable projects	-	-	1
Aircraft manufacturing	-	1	-
Total	3	29	30

lists the number of publications, considering the application and its objective.

To facilitate the understanding of the studies analyzed, the 62 articles listed in Table 1 were described individually below in the same order. However, based on the information regarding application and research method, it is possible to classify them along two axes. Regarding the application field, most studies are concentrated in the construction sector (eighteen articles), information technology (IT) (four articles), the energy industry (three articles), and manufacturing (five articles). In contrast, 28 studies do not specify the application domain. In terms of research methods, there is a clear predominance of modeling and simulation approaches (forty-one articles), followed by case studies (thirteen articles), theoretical studies (five articles), action research (one article), surveys (one article), and Design Science Research (DSR) (one article). This distribution highlights a predominantly technical and quantitative focus in the recent CCPM literature, underscoring methodological and sectoral gaps that are further explored in the Discussion section.

In the first study, the concepts of Scrum, one of the agile project management methods, and CCPM were applied to information technology projects. The results indicate that the combination allows for effective communication between stakeholders, where Scrum generates the ability to respond quickly and CCPM manages and provides good support for meeting deadlines [19]. The next study presents a case study of structural projects for building construction. By combining Building Information Modeling (BIM) and CCPM tools in a framework, 19% of the projects were completed faster than the original schedule had predicted, indicating a reduction in project time [6]. Applying action research to a Brazilian company, where 185 people produce power and hand tools for the construction industry, aimed to identify the main performance problems of multi-project systems. After placing the opportunities, project deliveries occurred in a total time 17.1% less than planned [20]. In the fourth article analyzed, a theoretical study is carried out that seeks to relate the concepts of TOC and CCPM to project execution, demonstrating how the mix of ideas and techniques supports the identification of project bottlenecks and risks [26].

The next article examined the relationship between adherence to CCPM practices and the performance of new product development and portfolio management in 79 innovative Brazilian companies. The results indicate that managers using CCPM concepts strive for high performance in both portfolio and project management [21]. The next research, supported by modeling and simulation, proposes an algorithm for scheduling multi-project activities. For this scheduling, a dynamic buffer definition was applied according to the risk and uncertainty of the activity [27]. The next study presents a theoretical model that combines part of the CCPM and the traditional value-added technique for software development projects. The method controlled the project schedule at external and internal levels, improving the accuracy and flexibility of activity duration monitoring [28]. The eighth article evaluated suggests a method supported with simulation results, in which the design is based on the integration of six modules: uncertainty and defuzzification module, using fuzzy set theory; schedule calculations module and the integration of the linear programming method (LSM) and CCPM; cost calculations module, which considers direct and indirect costs, penalty for delay, and expense of work interruptions; multi-objective optimization module, using a genetic algorithm (GA) as software; module for identifying multiple critical sequences and schedule buffers; and reporting module. The simulation results indicate that, for duration optimization using fuzzy inputs without interruptions or the addition of buffers, the duration and cost generated by the method developed are found in 90% of other studies, and, for project lead time, they present results better than 93% of those reported in the literature [29].

The next research highlights a multi-objective optimization method for minimizing time, cost, and work interruptions in repetitive programming, while considering uncertainties associated with different input parameters, and applies it to information technology projects. Using Critical Chain and strategic buffers with the application of empirical data, the model generated a reduction in lead time from 80 to 76.5 days [17]. In the study developed with modeling and simulation support, different priority levels of activities were used to define the Critical Chain of events in projects. The main results of the computational experiments include the non-negligible impact of different Critical Chain sequences on project robustness measures; the appropriate level of feed buffer sizes; and the trade-off between different reactive responses regarding cost factors or management preferences [30]. The conference paper proposes a change in concept to fragment the buffer at the end of the project within the chain. The model was simulated with empirical data and represented a reduction in lead time from 209 to 185 days (11.5%) [31]. In the twelfth study evaluated, modeling and simulation were applied, and resource availability was treated as a random variable, incorporating reliability into resources and variability into buffers. According to the projected data and the main contributions of the work, this new concept in CCPM is called resource-based reliability, which better reflects the uncertainty in resource availability in the project schedule, and a new formulation for buffer sizing in CCPM, based on reliability theory, which improves the accuracy of buffer determination [32].

In the paper presented at the conference, a case study in the construction industry evaluated the project schedule to determine the optimum schedule for implementing the project. With the application of CCPM, a feed buffer of four days and a project duration with a total buffer of 53 days were achieved, with existing scheduling from 432 to 379 days without buffer consumption [33]. In the next research article, which employed modeling and simulation, time buffers were dimensioned in two stages using both deterministic and stochastic optimization techniques, considering the needs of construction management. The optimization criteria are based on the cash flow analysis of the wind power generator installation construction project. The results show that the approach guarantees the protection of the scheduled construction completion date and the stability of the schedule [38]. In the next study, modeling and simulation are employed to present a project management method in scenarios where rework of activities is necessary. By proposing a model that integrates CCPM, the design structure matrix method, and the max-plus method, it was possible to achieve greater accuracy of activity completion times, more reliable deliveries, and reduced project risks and uncertainties [23]. The sixteenth study presents a simulation-supported model integrating the last planner system (LPS), the linear programming method (LSM), and CCPM to develop project tracking and control procedures. The results indicate that the comparison between the schedule developed by the proposed model and the Monte Carlo simulation showed that the baseline duration generated by the simulation exceeds that produced by the developed method by 12% and 10% for schedules with a confidence level of 50% and 90%, respectively [37].

The next article proposes a new buffer sizing procedure based on network decomposition through modeling and simulation. Computational tests using both empirical and simulated data demonstrate that the proposed method provides more accurate estimates of project execution time and smaller feed buffers. Additional benefits include lower expenses, reduced parallel work, multitasking, and rework, as well as risk reduction. The next research presents an unprecedented discussion of a case study on highway construction. The work developed an improved CCPM framework for effectively implementing construction-related projects. Considering the TOC concept, a methodology was developed to analyze bottlenecks and promote improvements in highway projects in India through the formulation of the Current Reality Tree (ARA), Evaporating Clouds (EN), Future Reality Tree (ARF), Prerequisite Tree (AP), and Transition Tree (AT) of the TOC Thinking Process [36]. In a paper presented at a conference, the CPM-Project Evaluation Review Technique (PERT) and CCPM methods were proposed, supported by modeling and simulation, for developing a project schedule that reduces late deliveries and the consequent payment of fines in the construction industry. The results indicate that the probability of the project not being delayed is very low. Only 55% of projects can be completed on time, which is not an effective strategy [34]. In other research presented at a conference, the aim was to apply CCPM and a set of Lean tools to construction projects. Supported by modeling and simulation, the results indicate that by combining Lean Production methods, 5 Whys, FMEA, and CCPM, losses are reduced without the need for buffers, and the total project time decreases by more than 50% [7]. In the twentieth study evaluated, the planned time of a project was reduced using modeling and simulation, with the help of BIM and CCPM. 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 sources of 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. The results show that the approach can generate high-quality solutions efficiently and competitively when compared to reference algorithms for small- and medium-sized instances. Another study [44] uses modeling and simulation with support vector machines and empirical data to propose a dynamic buffer monitoring method that combines monitoring and forecasting. The results show a comparison between the costs and duration of projects using the traditional and proposed methods, with the latter reducing costs and total project time. In a case study [43], a construction company conducted an experimental study to assess the practical impact of CCPM on shortening the schedule of a construction project. The results indicated a significant saving of 36 days (approximately 20%) in the project’s completion time, with the project completed in 151 days using CCPM compared to the 187 days planned using CPM.

In a paper published in 2024 [5], using the Design Science Research method, a framework for implementing blockchain-based CCPM buffer monitoring was presented. The findings highlight blockchain’s potential for delivering real-time updates, early risk detection, and enhanced collaboration, ultimately benefiting project stakeholders. The research [46] aimed to propose a model with modeling and simulation support to define the buffer size and fulfill 90% of the project stages in the appropriate time. The results indicate that the model tested with empirical data achieved satisfactory results. Finally, in the last text evaluated [47], the authors integrate the Early Late Start (ELS) technique with the Critical Chain approach to manage resource allocation in construction projects, supported by modeling and simulation. The model shows a reduction in project time from 154 to 120 days and buffers from 47 to 40 days, generating relevant results and validating the model.

The next paper proposes a buffer sizing method based on comprehensive resource tightness, combining physical and informational resource constraints. The technique improves buffer accuracy using a design structure matrix (DSM) and rework logic, overcoming the shortcomings of traditional methods (such as C&PM and RSEM). Simulated design tests demonstrated a 9.9% reduction in project duration and an 8.56% reduction in project cost compared to RSEM, with a lower probability of delay (2.7%) [48]. The following study proposes an integrated schedule monitoring model that combines Buffer Management (BM) with Schedule Risk Analysis (SRA), introducing the Activity Criticality Index (CRI) as a trigger for control actions. Simulations demonstrate that the CRI-BMA model is more efficient than traditional methods (such as RBMA), generating fewer interventions and crash costs, with similar time performance and a higher probability of on-time completion. The model also evaluates dynamic sensitivity strategies, showing that adaptive limits increase tracking efficiency [49]. The following research proposes a multi-objective optimization model for multi-project scheduling based on the Critical Chain (CCPM), considering time, cost, quality, and robustness. The model uses weighted utility functions and a cloud-based genetic algorithm to generate optimized solutions. Experiments have demonstrated that the model enhances overall multi-project scheduling performance, exhibiting high robustness and utility (value of 0.893). The main limitation reported is the subjectivity of defining variable weights [50]. The following article proposes a bi-objective model for optimizing time and cost in resource-constrained multi-project environments, utilizing the Critical Chain method and the NSGA-II genetic algorithm. Several scenarios were simulated with different daily resource limits and release configurations. The model presented Pareto optimal results for time and cost, demonstrating that the release of resources K2 and K3 significantly reduces time and total cost. The analysis revealed that having more available resources leads to shorter lead times, with a corresponding reduction in total costs [51].

This paper proposes a robust multi-attribute buffer sizing model based on CCPM, incorporating metrics of complexity, flexibility, robustness, dependency, and risk. The model was tested in a real-world case study of gas infrastructure construction in Iran. Compared to the C&PM, RSEM, and ICC/PM methods, the model reduced the total buffer size by up to 15% and increased scheduling robustness, with a 97.3% on-time completion probability. The approach demonstrated greater cost-effectiveness and reliability under conditions of uncertainty [10]. The following article proposes a conceptual model that integrates the Critical Chain methodology with agile development lifecycles in the automotive industry, with a focus on embedded software projects. The proposal aims to mitigate delays caused by frequent changes in requirements, which are common in this sector. The approach combines the traditional V-model with agile methodologies, such as Adaptive Software Development (ASD), allowing greater tolerance for change without rework. The model is presented in a didactic manner, without empirical application, with a focus on reducing lead time and increasing schedule resilience [52]. The next study under review develops a probabilistic model of construction schedule reliability based on the Critical Chain, using the beta distribution to estimate the probability of meeting deadlines. Simulations demonstrate that applying the model can increase reliability from 50% to up to 90% by adopting more stringent deadlines and final buffers. The model considers the impact of management decisions and levels of control on time distribution, proposing a framework for more effective decision-making in schedule risk management in civil construction [53]. This study uses Monte Carlo simulations to identify a practical limit between “good” and “bad” multitasking in multi-project environments with different levels of resource availability. Ten portfolios with varying complexity, uncertainty, and resource constraints were simulated. The results show that multitasking of up to two activities can be beneficial when resource availability is below 170%, while multitasking above that level is detrimental. The work proposes an objective criterion to limit multitasking without compromising the principles of the Critical Chain [54].

The study proposes a robust optimization model for the resource-constrained project scheduling problem (RCPSP) based on the Critical Chain, considering uncertainties in activity durations. A genetic algorithm is used to solve the model. Tests have shown that, although the expected project time increases slightly (from 25 to 27.5 days), the duration variance is significantly reduced (from 4.1 to 3.2 days), making the schedule more robust and less susceptible to risks [55]. This article examines the scheduling management of a mobile banking payment project utilizing an enhanced version of the Critical Chain method. It integrates techniques such as the Analytical Hierarchy Process (AHP) and gray scale to define buffers based on uncertainty factors. Applied to a real-world case study at a rural credit union in China, the model reduced the project duration from 550 to 484 days, and later to 426 days with precise buffers. The approach provided better schedule control, greater predictability, and significant operational gains [56]. The following article proposes an integrated Critical Chain Risk Management (CCPM) framework to increase schedule reliability in EPC projects. It uses Fuzzy FMEA to identify critical risk events, complemented by FTA and ETA for mitigation. Applied to a real-world scenario in the electricity sector, the model demonstrated that project duration would have increased significantly (by up to 1311 days in construction) without mitigation; however, integration with CCPM enabled buffers to be dimensioned and delays to be mitigated. The approach proved effective for proactive risk control and increased delivery reliability [57]. 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]. The next article analyzed proposes the integration of fuzzy logic with QFD (Quality Function Deployment) to systematically calculate buffer time in the Critical Chain, considering six project uncertainties (time, budget, resources, etc.). The approach defines weights and fuzzy membership functions for each activity based on the degree of uncertainty associated with it. Applied to a fictitious project, the method reduced the total time to 112.68 days, compared to 117.75 days with conventional CCPM and 157 days with PERT. The solution resulted in more accurate buffers, avoiding excessive delays and optimizing the schedule [65]. The next study proposes a Scenario-Based Proactive Robust Optimization (SBPRO) method that integrates scenario analysis and fuzzy logic to increase schedule robustness in CCPM projects. Validated in three case studies with real projects, SBPRO outperformed the traditional CCPM method in terms of robustness and probability of on-time completion. The model balances project duration and schedule robustness using a genetic algorithm and dual objective functions. In all simulated cases, SBPRO demonstrated greater resilience to uncertainty and superior performance in risk and delay management [66].

This article presents the application of the CCPM method to the assembly of a coal cutter, with a focus on the introduction of resources, feed, and design buffers. Based on historical data and statistical analysis (using Statistica software), three resource buffers and one feed buffer were estimated. Implementing these buffers reduced project duration by up to 56 days and costs by 8%. The analysis revealed that the use of buffers was crucial for overcoming resource conflicts and uncertainties, increasing schedule reliability in make-to-order environments [67]. The following study applies the CCPM method to scheduling a shearer loader assembly process, focusing on human resource conflicts (welders). The analysis compares schedules developed using the ASAP and ALAP principles, demonstrating that only CCPM enables realistic resource allocation. Resolving resource conflicts required task reordering, which increased the duration from 175 to 230 days; however, it resulted in a more feasible and robust schedule. The article highlights CCPM as superior to CPM for its focus on resource constraints and mitigating multitasking and delays [68]. The next article proposes a model for the transmission and control of duration risk based on CCPM and the theory of risk element transmission in projects. The approach enables the calculation of duration risk propagation along both critical and non-Critical Chains, considering risk absorption coefficients for each activity. The model identifies bottleneck activities and redefines buffer size based on accumulated systemic risk. A numerical example illustrates how to identify the points with the greatest impact on the project’s overall risk, providing a more reliable basis for schedule planning [69]. The next article proposes an improved buffer management framework for CCPM, incorporating resource cost and schedule stability criteria. Reactive strategies, such as SOR, TSR, and ASR, are introduced to reschedule or reallocate resources in response to deviations. The planning phase considers a resource cut-off date to minimize costs for regular and emergency resources. Simulations with PSPLIB instances demonstrated that the use of the framework significantly reduces total costs and improves schedule stability, without compromising on-time delivery [70].

This paper proposes a method for integrating CCPM and LSM (Linear Scheduling Method) for repetitive projects. The model introduces a new buffer—the Resource Conflict Buffer (RCB)—and a systematic process for identifying multiple Critical Chains. The approach respects resource continuity and utilizes conservative estimates to minimize project duration. A simulated case study involving bridge construction demonstrates its effectiveness, as the project duration was reduced from 163 to 152.5 days, demonstrating gains from optimized buffers and critical sequence reconfiguration based on resource constraints [71]. The next article proposes a hybrid simulation–optimization model for buffer sizing construction projects based on CCPM. The approach combines Monte Carlo analysis, a genetic algorithm (NSGA-II), and a set of performance criteria (such as variance, flexibility, robustness, and complexity) to evaluate multiple buffer alternatives. A case study applied to the construction of a commercial building in Iran demonstrated that the model enables the selection of buffers best suited to the project profile, resulting in greater schedule robustness, more efficient risk control, and reduced schedule deviations [72]. In the next research, a conceptual schedule management model is proposed based on CCPM, incorporating factors such as human behavior, resource constraints, and uncertainty in buffer definition. The method introduces complexity, importance, and risk coefficients to calculate buffers based on triple-point estimates. The model suggests buffer adjustments based on the manager’s risk profile. Although not empirically validated, the study makes a valuable contribution by highlighting the influence of decision-maker psychology and proposing formulas for buffers that are more closely aligned with the project’s reality [73].

Another study proposes an integrated project control approach that combines the CCM/BM and EVM/ES methods, aiming to monitor time, cost, and risk simultaneously. The methodology, known as the “efficiency-risk approach,” incorporates time and cost buffers, along with new formulas for completion estimates. Application to a real project at Mobarakeh Steel Co. demonstrated that the approach allows for more accurate forecasting of completion times and costs, in addition to indicating corrective actions based on buffer consumption [74]. The next paper integrates procurement management with CCPM, introducing resource buffers for piping supplies. Applied to real-world refinery installation cases, the method reduced construction time by approximately 35% compared to traditional CPM. Furthermore, it demonstrated that ignoring supply uncertainty can increase project duration by approximately 5%. The approach provided greater predictability and schedule control, effectively aligning planning and execution [16]. In the study reviewed, an innovative buffer sizing method is proposed based on attribute optimization, simultaneously considering resource constraints and network complexity. The model introduces variables such as resource efficiency, relative cost, and activity start flexibility, applying fuzzy theory and MATLAB simulations. In 500 simulated project runs, the proposed method demonstrated shorter duration (92.73 days) and cost (USD 332,290) compared to traditional methods (C&PM and RSEM) while maintaining a low delay probability (2.6%) and high schedule reliability [75].

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. Although these models exhibit a high level of technical sophistication (e.g., fuzzy logic, genetic algorithms, machine learning, or stochastic simulations), they lack empirical validation. As such, their practical feasibility and required adaptations for real-world scenarios remain uncertain. Nonetheless, these studies are methodologically valuable, as they advance the theoretical foundation of CCPM and provide adaptable tools for future research.

The integration of CCPM with complementary methodologies, tools, and technologies has emerged as a central vector of innovation in contemporary project management applications. This trend not only enhances the classical benefits of methods, such as schedule reduction and efficient resource utilization, but also expands their adaptability to different organizational contexts, particularly in high-variability and complexity environments. One of the most evident approaches is the combination of CCPM with agile methods, especially SCRUM. This integration has proven particularly effective in software development and IT project environments, where iterative cycles and flexibility are critical. SCRUM contributes with responsiveness and continuous stakeholder interaction, while CCPM provides a solid framework for schedule control and strategic buffers, supporting on-time delivery [19,52]. The result is a balance between adaptability and reliability, essential in innovation-driven settings. In the construction industry, the integration of CCPM with Building Information Modeling (BIM)—especially in its 4D format—is gaining traction. Studies report significant reductions in project timelines, such as optimizing structural timelines from 432 to 293 days, as well as improvements in spatial coordination and scheduling [6,8]. This synergy between 3D planning and project timeline management enhances predictability and cost control. Another promising avenue is the combination of CCPM with Lean approaches, such as the 5 Whys, FMEA, and waste mapping techniques. These strategies improve operational efficiency by eliminating waste and increasing the reliability of estimates without buffer consumption, as demonstrated in dam construction projects [7]. The Theory of Constraints (TOC)—the conceptual foundation of CCPM—has been deepened in studies that employ its logical tools, such as the Current and Future Reality Trees, to identify and mitigate systemic bottlenecks in multi-project systems. Practical applications in highway construction and production systems demonstrate that these tools facilitate strategic prioritization and organizational alignment [20,26,36,69]. Integration fosters a continuous improvement environment that focuses on delivering value. It is evident that the integration between CCPM and other tools and methodologies is being applied and yields positive results for the projects.

A wide range of studies highlights the use of advanced modeling and simulation for buffer sizing and schedule optimization, employing techniques such as fuzzy logic, genetic algorithms, support vector regression, and Monte Carlo analysis, among others. These approaches allow for more accurate uncertainty representation and tailored planning according to project dependencies, variability, and resource constraints [5,9,10,29,30,31,33,36,38,40,41,42,44,45,46,60,72], and the result is more robust schedules, lower rework rates, and improved delivery reliability. Moreover, the number of studies combining CCPM with schedule monitoring and control systems is growing. These include the Last Planner System (LPS), Linear Scheduling Method (LSM), and Earned Value Management (EVM/ES). Such integrations enhance responsiveness to deviations through dynamic buffer tracking and the definition of control triggers, such as the Activity Criticality Index (CRI) [29,37,47,50,51,53,56,60,74]. Some studies on multi-objective and multi-attribute optimization have also incorporated CCPM into frameworks designed to balance time, cost, quality, and robustness. Using algorithms such as NSGA-II and utility-based evaluation methods, these solutions are tested in multi-project environments with severe resource constraints, showing significant improvements in schedule stability and decision-making support [17,45,50,51,55,66].

Regarding risk management, CCPM has been enhanced with analytical techniques such as Fuzzy FMEA, Fault Tree Analysis (FTA), Event Tree Analysis (ETA), and Schedule Risk Analysis (SRA). These models increase the capacity to anticipate failures, quantify impacts, and proactively adjust buffers—even in engineering scenarios with high structural and operational risks [9,49,57]. From a strategic perspective, studies like Sastoque-Pinilla et al. [76] emphasize that the effectiveness of models, such as CCPM, depends on a deep understanding of project success criteria, considering the expectations of multiple stakeholders. This reinforces the need to align planning tools, such as CCPM, with the qualitative dimensions of project management, including value perception, cross-functional collaboration, and strategic alignment.

An important contribution—though still concentrated in theoretical modeling and simulation—comes from generic and cross-sectoral models proposing new buffer sizing methods based on attributes such as network complexity, activity criticality, flexibility, rework propagation, and risk distribution. Although lacking empirical validation, these approaches are essential for expanding the applicability of CCPM [10,27,28,34,39,48,60,61,65,68,70,73,76]. The advancement of such generic approaches is further supported by the incorporation of real-time digital monitoring platforms, as demonstrated by Tapia et al. [11], who proposed a scalable platform for machine tool monitoring that could be integrated with CCPM. This digitalization of shop floor or construction site operations enables real-time buffer updates, improving the accuracy of consumption management and responsiveness to unexpected events.

Several studies report real-world applications of CCPM in sectors such as aircraft maintenance [58], energy infrastructure [10], and industrial product development [59,62,67]. These cases demonstrate that CCPM can be successfully adapted to environments with high resource interdependence and customization demands, underscoring the need to further investigate its potential in complex and dynamic sectors.

This article offers a comprehensive and methodologically rigorous contribution to the field of project management by mapping and critically analyzing 62 empirical and conceptual studies on the application of Critical Chain Project Management (CCPM). Through a systematic literature review, the research presents a comprehensive overview of the evolution of CCPM in both academic and practical contexts over the past decade. One of its key contributions lies in identifying trends that reflect increasing methodological sophistication and interdisciplinary integration of CCPM, particularly through combinations with agile methods, BIM, Lean tools, and advanced modeling techniques, such as fuzzy logic and genetic algorithms. The temporal scope, spanning since 2014, enables a longitudinal and in-depth analysis of the current state of CCPM research. The findings reveal a clear predominance of modeling and simulation approaches, which, although valuable, also point to a gap in qualitative and empirically grounded investigations. Furthermore, the concentration of applications in sectors such as construction, IT, and energy, and the relative scarcity of studies in Engineer-to-Order (ETO) environments or service-based organizations, emphasize the need for future research in underexplored organizational contexts.

The article also stands out for its ability to articulate theoretical contributions with practical relevance. The identification of implementation gaps, the emphasis on integration with digital technologies (e.g., real-time monitoring platforms [11]), and the recognition of project success as a multidimensional and multistakeholder phenomenon [76] demonstrate a mature and forward-looking perspective. Additionally, the synthesis of results around the main project objectives—time, risk, and cost reduction—provides project managers with evidence-based arguments for adopting CCPM strategies in diverse and dynamic environments. Finally, the study offers a solid foundation for future research agendas by highlighting opportunities for the empirical validation of generic models, investigating organizational readiness factors, and developing implementation frameworks tailored to high-variability environments. A particular emphasis should be placed on the research gap regarding ETO (Engineer-to-Order) systems, which represent firms that are continuously engaged in projects focused on developing new products and services. These environments, characterized by high customization, volatility, and resource interdependence, represent a promising field for exploring the applicability and necessary adaptations of CCPM.

Despite the rigorous methodology adopted in conducting this systematic review, the study presents some limitations that should be acknowledged. The first concerns the heterogeneity of the analyzed studies, both in terms of research methods and application contexts. While this diversity enriches the discussion, it also hinders direct comparison of the results and limits the potential for more precise generalizations. A second relevant limitation is the predominance of modeling and simulation-based approaches, which, although contributing to the technical advancement of CCPM, have limited empirical validation. Many of these studies are developed in controlled environments, with little or no documentation of practical implementation, which reduces their direct applicability to real-world project settings. Furthermore, most of the reviewed studies do not provide detailed information about their organizational context, such as sector, company size, or project management maturity level. This lack of contextual data constrains the extrapolation of findings and the development of actionable guidelines for practitioners aiming to implement CCPM in specific environments.

5. Final Considerations

This study aimed to critically analyze the theoretical and practical contributions of the recent literature on the Critical Chain Project Management (CCPM) method in multi-project environments through a systematic review. Specifically, it sought to identify methodological trends, application gaps, and potential directions for future research. Based on the results obtained, it can be stated that all proposed objectives were fully achieved.

The analysis of 62 publications revealed significant advances in CCPM research over the past decade, particularly in terms of methodological refinement and interdisciplinary integration. Modeling and simulation-based approaches were predominant, reflecting a strong technical orientation in recent studies. However, the scarcity of empirical investigations and the lack of detailed contextual data—such as organizational characteristics and project typologies—represent important gaps that limit the generalization and practical applicability of the proposed models.

The study also identified a growing trend toward integrating CCPM with emerging methodologies and technologies, such as BIM, Lean Construction, agile frameworks, predictive tools, and real-time digital monitoring platforms. These hybrid approaches demonstrate the method’s adaptability and reinforce its relevance in contexts marked by complexity, resource constraints, and high uncertainty.

Another important finding concerns the low representation of studies applied to Engineer-to-Order (ETO) systems and service-oriented organizations. These contexts—characterized by high product and process customization, variable demand, and constant project initiation—are particularly well-suited to CCPM principles. However, the lack of empirical evidence in these environments points to a promising agenda for future research.

Finally, this study contributes not only by systematizing and critically evaluating the existing body of knowledge but also by proposing a research agenda focused on empirical validation, sectoral diversification, and methodological integration. The results reinforce both the theoretical robustness and the practical potential of CCPM, while also highlighting the need for deeper, context-sensitive investigations that promote its adoption in real-world multi-project management environments.