The role of the rework cycle from system dynamics modelling for project management: challenges and future research agenda

Abstract

Purpose

This paper aims to contribute to a better understanding of the relationship between the rework cycle with system dynamics (SD) models and the Project Management Institute (PMI) process group.

Design/methodology/approach

To achieve the aim of this paper, 84 articles that blended SD models and project management (PM) were analysed to identify key variables used in PM modelling. The key variables were utilised to build an extended SD model with multiple rework cycles to explain the link between the rework cycle SD model and PMI process group.

Findings

The results show that SD might be a favourable approach to capture the reality of the project life cycle when it is extended to represent front-ending, delivery and back-ending. In fact, SD models could potentially be extended to the agile and hybrid methodologies for improving the PM.

Research limitations/implications

Although this paper provides a better understanding about the extended project life cycle by SD modelling, the results reported herein should be considered in future research that comprises the design of a SD model considering the agile and hybrid methodologies for PM.

Practical implications

This paper shows how the rework cycle can be applied to the extended project life cycle and the PMI process groups. Additionally, it highlights why SD modelling is a crucial tool for assisting managers with long-term decision-making in PM.

Originality/value

This study is among the first to explore the integration of rework cycle SD models within the PMI process groups. Specifically, it may prove valuable in supporting decision-making for project managers at each stage of a project’s extended life cycle. As a result, the research also contributes to the ongoing discussion on integrating PM with sustainability and innovation considerations.

1. Introduction

As a field, project management (PM) has multiple challenges prompted by delays in the execution that practitioners in this arena worry about (Morris, 2017; Abbasi and Jaafari, 2018; Davies et al., 2018; Pinto, 2022). Projects execution often does not meet time expectations, occasioning in activities or duration overrun, resulting in rework cycles (Elia et al., 2020; Morris, 2022). In such projects, decision-makers face the complex task of reconciling project activities to achieve objectives. To assist with tackling this concern, the project life cycle has been extended to capture front-ending, delivery and back-ending (Artto et al., 2016; APM, 2019; Williams et al., 2019). Nevertheless, the complexity and uncertainty in the PM caused by this situation have not been deep studied from formal models. Therefore, this scenario has brought new challenges and opportunities for modelling within the PM discipline, which has not yet been examined (Sabini et al., 2019; Magano et al., 2021; Brunet, 2022).

Previous studies show that the complexity, uncertainty and nonlinearity are critical aspects in the control and planning for project delivery on time (Wang et al., 2017; Bing et al., 2022; Pinto, 2022). The simulation approaches, such as system dynamics (SD) can contribute to better understanding about these concerns. The existing literature shows several SD applications in the subject of the PM for claim analysis (Nasirzadeh et al., 2019), decision-making processes (Sadabadi and Kama, 2014; Lopes et al., 2015), policy design (Ogano and Pretorius, 2017) and human resources analysis (Garcia-Alvarez et al., 2016). From the SD methodology, different studies have utilised a rework cycle structure to simulate the project back-ending stage of the extended life cycle. Nevertheless, there are few studies about how the SD approach may be useful in evaluating the project front-ending decision-making.

The rework cycles as part of the extended project life cycle should be measured with project front-ending, project delivery and project back-ending. In fact, most SD models have been produced within the boundary of traditional PM practice (i.e. project delivery), without considering the extended project life cycle. Therefore, there is a need to rethink the boundaries of traditional PM to assist from the simulation with new emerging aspects of decision-making. This study aims to bridge the gap by identifying rework cycle variables in SD models based on existing literature, offering new insights into the PMI process group (i.e. extended project life cycle). New insights in this issue contribute to improve both the modelling process and decision-making from the PM perspective. Developing countries, especially in Ibero-America, could benefit from enhanced comprehension of the PM, considering the rise in global projects.

In this context, the paper addresses the following three research questions: (1) What are the benefits of connecting the rework cycle SD model with Project Management Institute (PMI) process group? (2) What are the advantages and limitations of using SD modelling for PM? and (3) What directions might inform future SD modelling in PM? To give a response to these questions, the paper discusses which key relationships reported in the literature connected to rework cycle SD models with the PMI process group. In the same line, the aim of this paper is discussed, the future directions of the rework cycle SD models in monitoring and controlling for executing a project.

This study makes some noteworthy contributions to the field of PM by illustrating the application of rework cycle SD models within the PMI process groups. By doing so, it extends beyond the conventional understanding typically held by traditional project managers. The paper also proposes new directions for PM practice, advocating for the integration of rework cycle SD modelling across various stages of the project life cycle. This approach offers a more dynamic and adaptive perspective, enabling project managers to better anticipate and manage rework during project execution. In this context, the paper updates and enhances the findings of a research project previously conducted by the authors (Calderon-Tellez et al., 2021).

The remainder of this paper is organised as follows. Section 2 presents a description of the research problem and an overview of SD modelling. Section 3 shows an outline of the methodology used for this research. Section 4 summarises a descriptive analysis of the obtained findings. Section 5 discusses three main challenges and opportunities identified from the findings. Section 6 concludes the paper, provides theoretical and practical implications and future research directions.

2. Research framework

2.1 System dynamics and project management

The technological changes that alter the way society functions are known as socio-technical transitions. In fact, these changes are often linked to the development of projects. Silvius (2017) declares that PM plays a pivotal role in the realisation of socially sustainable business. Nowadays, the PM faces emerging challenges associated with socio-technical transitions (Magano et al., 2021; Daniel, 2022). These transitions involve developing new ways to understand the PM processes, providing the context for the evolution of the discipline. As socio-technical transition challenge comprehends both theoretical and practical gaps within the discipline, a new understanding of the future directions for the PM needs to be addressed (Sovacool and Geels, 2021).

PM discipline may be conceptualised as an open system that is influenced by its environment. In this direction, SD modelling allows to explain the relationships between PM processes and its environment. SD is a powerful approach to understand, analyse, simulate, and predict complex and dynamic business process (Noto and Cosenz, 2021; Ammirato et al., 2022). Previous studies have applied SD models or system thinking approach for achieving sustainability in project-based organisations (Scales, 2020; Singh et al., 2023). Cosenz et al. (2019) conceptualised a dynamic business modelling for sustainability supported by a SD methodology approach. Singh et al. (2023) propose an integrated system thinking approach to attain sustainability in project-based organisations. These studies help project professionals in assessing the best organisational designs to achieve sustainability.

Other studies also have shown how an SD methodology approach assists the decision-making for the development of projects (Calderon-Tellez et al., 2024). Indeed, Papachristos et al. (2020) point up PM research is one of the most successful SD application areas. These studies have become novel ways for modelling the complexity of the decision-making in the PM (Kapsali, 2013; Bell et al., 2019).

Sterman (2000) established five iterative steps for SD modelling process, as shown in Table 1. First step, problem articulation or boundary selection, relates to theme selection, key variables, time horizon and dynamic problem definition. Second step is the formulation of dynamic behaviour which can include initial hypothesis generation, endogenous focus, and tools for mapping such as causal loop diagrams (CLDs) and subsystem diagrams. Third step is concerning to the establishment of a simulation model by specifying the structure and estimating initial conditions such as stock-and-flow diagram (SFD). Fourth step, testing, develops model tests through sensitivity and robustness under extreme conditions. The next step, policy formulation and evaluation, is where the process is iterative within the process by defying “what happens if …” scenarios, design policy or analyse sensitivity. Note that this SD modelling process is characterised by a continuous cycle.

Many studies highlight project failure regarding PM success (short-term objectives) (i.e. cost, time, quality) and project success (long-term objectives) (Morris, 2013; Gupta et al., 2019; Ayat et al., 2021). The analysis of failure causes associated with traditional PM led to the inception of the Management of Projects (MoP) (Sage et al., 2014; Gupta et al., 2019). MoP has three distinctive levels: technical core, strategic envelope and institutional context (Morris, 2013). These levels started an evolution that encouraged a more holistic approach linked with systems thinking to achieving project and project success (Kapsali, 2013; Ika and Pinto, 2022). The MoP and work of Morris have influenced the APM [1] body of knowledge, extended life cycle, which aligns with project front-ending, project delivery and project back-ending (Artto et al., 2016; Williams et al., 2019; Morris, 2022), which suggests increased complexity, uncertainty and non-linearity. The research of Morris and co-workers has fundamentally re-conceptualised the boundaries of the theory and practice of PM.

In this context, there is a need to identify key SD structures associated with traditional PM practice, which will contribute to future modelling directions. This paper presents potential structural enhancements which could inform and offer further explanatory insights into traditional project practice. Therefore, it shows beyond the boundaries of traditional PM, getting a significant implication for the PM profession.

2.2 SD modelling for system explanation

There is a need to highlight the connectivity between the systems movement, systems thinking and SD. Jay Forrester developed the SD modelling technique in the 1950s to simulate system behaviour over time that includes nonlinear dynamics, complex system, feedback loops and delays (Forrester, 1961). Barry Richmond contrasted systems thinking and SD definition developed by Forrester. Richmond considers systems thinking a broader idea than SD, instead of considering systems thinking, as Forrester did “a small subset of system dynamics” (Richmond, 1994, p. 136). Despite the connectivity between SD and systems thinking was established in the 1990s (Richardson et al., 1994), sometimes SD has been disconnected from systems thinking.

Systems thinking is a key idea of the system’s movement. Checkland (1981b) asserts that the systems movement is a meta-discipline and establishes general systems theory and cybernetics as part of the intellectual foundations. Also, other studies argue that open-and-closed systems theories are an integral part of the systems movement (Kapsali, 2011). Systems movement explores the consequences of holistic rather reductionist thinking (Checkland, 1981a). In this sense, systems movement shows how different aspect such as sociocultural, human resources and political concerns should be considered within the broader context of PM. Jackson (2019) argues that systems thinking can deal with complexity. Checkland (1981a) presents a supporting map which delineates between the study of systems and the application of systems ideas in different disciplines. The study of systems is divided into theoretical developments and practical real-world problem solving. In sum, the systems movement aims to tackle problems of organised complexity.

SD is rooted in servomechanism (Forrester, 1961; Richardson, 1999). Thus, SD is linked with communication and control, and therefore it is an integral part of the systems movement. Also, SD can highlight the structural complexity of real-world problems, which contributes to understand the delay effects on projects in the long-term (Jackson, 2019).

Thinking in wholes assists with recognising boundaries of our modelling rationality. In this sense, this paper has connectivity with the work of Herbert Simon and bounded rationality. The principle of bounded rationality is defined as “the capacity of the human mind for formulating and solving complex problems is very small compared with the size of the problems whose solution is required for objectively rational behaviour in the real world or even for a reasonable approximation to such objective rationality” (Simon, 1957, p. 198). Although the existing literature has addressed the boundaries of traditional PM practice, this paper identifies important structures that explain relevant dynamic behaviour. These structures will be integrated into future SD models of the extended boundaries of new PM practices (Winter et al., 2006).

2.3 Dynamics hypothesis

The dynamic hypothesis is a conceptual structure that represents the behaviour of a system because of decision-making for the actors. It theorises about system structure, its relationship and the consequential behavioural dynamics that might be involved. In this way, the CLD reinforces the dynamic behaviour hypothesis step which is used for qualitative analysis system behaviour. CLDs structure causal links among variables of a system. CLDs link cause to an effect by using arrows. The arrows are the causal links explaining in Table 2. In addition, Table 2 explains causal link polarity, positive (+) or negative (−), to establish the changes from dependent variable to independent variable, namely, causal positive link or casual negative link. When causal links make up a closed loop, it is positive loop (reinforcing) or negative loop (negative), as shown in Table 2.

Figure 1 represents a dynamic hypothesis of the rework cycle for a project. The balancing loop is denoted as B1, which establishes the relationship between work to be done and work done correctly. The balancing loop (B1) is the hypothesis that means, if work to be done increases, then work done correctly increases above what it would have been.

Exploring PM through SD modelling reinforces project decision-making at long-term. The most used structure to simulate within SD modelling is the rework cycle. However, the rework cycle has limitation such as changing staff, activities and delays per phase. To enhance the simulation model, this paper proposes a rework cycle per phase or process group. As a result, consecutive or series of the rework cycles offer a better understanding of project behaviour.

2.4 Stock and flow diagram

The SFD is a tool to formulate and represent the processes of PM through a simulation model. The SFD underpins the formulation step of the modelling process, which is used as quantitative analysis. The SFD highlights the physical structure through elements such as stocks, flows, converters and connectors, as described in Table 3. The interactions between these elements form an SFD. Stocks are used to state the system by generating the information of the variables. Stocks relate to flows, which regulate its input or output as a rate of change. This rate of change can be connected by using connectors.

3. Methodology description

This section shows the methodology of research used to address the posed questions above. After a robust systematic literature review (SLR), this paper adopted the SD methodology for identifying advantage and limitations of rework cycle in the PM. Figure 2 presents the description of each stage used in the study. First, we developed an SLR for better understanding the relationships between PM and SD modelling. Second, a casual loop diagram (CLD), and stock and flow diagram (SFD) were developed based on the identified variables in the existing literature. Third, we employed model validation using Theil inequality statistics and correlation coefficient. In relation to our study, this methodology allows us to undertake an in-depth analysis of how the rework cycle deployed by SD methodology can help to gain insights to reduce the uncertainty in executing a project.

3.1 Systematic literature review (SLR)

This study identified several useful aspects for modelling the project life cycle from a systematic literature review (SLR). An SLR is stated as a complete and well-organised literature study, backed by a method that is led by a research question (Nisa et al., 2022). Thus, the research questions mentioned above in the introduction section were addressed. Specific literature review studies about the rework cycle SD models to the PM have not yet been reported. Calderon-Tellez et al. (2024) conducted a comprehensive literature study on the link between SD modelling and PM. However, this study does not discuss the usefulness of rework cycle models for managerial decision-making throughout the project life cycle.

The first step of planning the review for the systematic search developed in this study was based on the methodology proposed by Tranfield et al. (2003). The search strategy stages for our research are presented in Table 4. The first stage seeks to collect the articles aimed on SD approach used to model PM issues. We incorporated studies with simulation models or CLDs. Non-English articles were excluded from the dataset identified. For the first step, the dataset included articles from Web of Science and Scopus as search engines with a time span between 1980 and 2023. In both databases, we searched “project management” and “system dynamics” in the title and abstract (i.e. Web of Science: TS= (“System Dynamics” AND “system dynamics” AND “project management” AND “Project Management”). We excluded simulation approaches such as agent-based modelling (ABM) and discrete simulation.

In the second stage, we refined our search by keeping articles in categories that applied to the business, management, and accounting (miscellaneous), management, operation research, decision sciences and social sciences for both databases. That is, we incorporated all articles published in journals ranking in the categories of business within Journal Citation Reports (JCR) for Web of Science and Scimago Journal Ranking (SJR) for Scopus. After refining the articles in categories, 62 and 130 articles in each database remained. Following for the third step, we combined articles found from the databases and removed duplicates, which resulted in 137 articles. In the last stage, we read the abstract of the articles and screened the full article to identify if a study considered the rework cycle model. After reading the abstract of the articles and screened the full paper, we identify 84 articles that included the rework cycle model. A sample of articles included in this review was organised in an Excel spreadsheet in which descriptive key information for each article which can be revised in Appendix 1.

The descriptive analysis of the dataset includes distribution of publications per year from 1980 to 2023, fields where SD modelling and PM have been blended. We also conducted a descriptive analysis by displaying information on the development of articles by examining the triple constraint (cost, time and quality), the extended project life cycle in the front-end, project execution and back-end domains, and process group in the PM according to the PMI (PMBOK, 2017). In the last stage, we performed a CLD for the rework cycle model, considering the variables identified from the SLR. Then, general SFD structure for linking multiple rework cycles was performed employing the Stella software. Finally, we conducted a discussion by examining the simulation results by linking multiple rework cycles.

3.2 Modelling description

Our CLD and SFD structure were formalised using a set of variables from simulation structures, which we identified from existing literature (see Appendix 2). Four experts in the PM field validated the CLD structure at the University of Sussex, UK. We conducted two meeting with the experts. We conducted the first meeting in November 2021 to discuss the initial results regarding the variables identified in the literature. We carried the validation process out through a brief description of the principal activities of the rework cycle in the extended project life cycle. Then, we discussed the draft of a CLD to identify the connection of variables with the existing literature. In December 2021, we held the second meeting to capture the experts' perspectives on the developed CLD and improve our understanding of the emerging themes in PM. Furthermore, we validated the usefulness of the rework cycle in decision-making. In this sense, the fundamental assumptions considered designing of the CLD are as follows.

Assumption 1. The rework cycle structure’s principal variables, including “work to be done”, “work actually done”, “rework discovery”, “productivity”, “people” and “quality”, were based on Cooper et al. (2002).

Assumption 2. There is a relationship in the project execution between “work to be done” and “work actually done”. That is, if work to be done increases, then work done correctly increases above what it would have been (Cooper, 1980).

Assumption 3. Given the project’s execution faces delays, there is a fraction really complete in the project (Williams et al., 1995). That is, if work done correctly increases, then work actually done decreases fraction really complete.

Assumption 4. The need for extra activities in the projects because of delays involves adding additional work (Williams et al., 1995). Therefore, the workflows might be affecting the productivity and staff in the projects (Garcia-Alvarez et al., 2016).

Starting from the CLD, the SFD was developed. The SFD represents the feedback structures and variables associated with the rework cycle SD model. For instance, we represent the closing process Cl at time t as the initial value of the closing process Cl0 plus the integral of monitoring and controlling rate MCi(t) minus closing rate Ci(t) as follows:(1)Cl (t)=Cl0(t−dt)+∫060MCi(t)−Ci(t)dt

Appendix 3 presents all equations used in the simulation model to represent the rework cycle SD model.

3.3 Model validation

A structural and behavioural validation process was conducted on the SFD and simulation results, following the validation tests outlined in the SD literature (Barlas, 1996; Oliva, 2003; Qudrat-Ullah and Seong, 2010). In this regard, the results of the behaviour reproduction test for project tasks (actual) and completed work (simulation) are shown in Figure 3. These results show that it highly correlated the simulation and actual data. The data for validating the simulation model were obtained from an energy project executed by the Ministry of Mines and Energy of Colombia, carried out between 10 November 2017 and 30 April 2019.

In addition to this, Theil’s inequality statistics and the correlation coefficient were used for the historical fit validation. Theil’s inequality statistics indicate that unequal covariation (UC) and unequal variation (US) account for 73.2% and 16.8% of the mean squared error (MSE), respectively. The R-squared correlation values for each project stage – initiating (0.91), planning (0.89), executing (0.92), monitoring and controlling (0.80) and closing (0.85) – demonstrate a strong correlation with the historical data.

4. Results

This research examines 84 articles of PM and SD approach between 1980 and 2023, as shown in Figure 4. Although the use of the SD models for PM started in the 1980s with the rework cycle (Cooper, 1980), the rework concept only was contemplated in the PM discipline began in the 1990s. The rework cycle models play an essential role to analyse the key factors of disruption in the projects. The figure highlights the use of the rework cycle models in the last two decades. Since 1996, SD has used the rework cycle to model PM for infrastructure projects, achieving a 48% growth rate. The existing literature in SD applied to the PM field provides evidence of the concern about sustainability (de Toledo et al., 2019; Goel et al., 2019; Silvius and Marnewick, 2022). Most of the SD models assess how the productivity loss and increased rework amplify the impact of environmental, social and economic aspects. Nevertheless, there are no studies that explicitly addressed a combination of the rework cycle models with traditional PM methods. Therefore, there is a future field of research regarding the integration of recent PM methods, such as agile techniques with SD models, for addressing sustainability problems.

Figure 5 shows the number of papers in various fields of PM using SD modelling, as obtained from the literature review: 40 in infrastructure construction, 17 in product development, 15 in research and development (R&D), 6 in software development, 4 in aeronautical and aerospace, and 2 in shipbuilding. Although the existing literature shows several applications on cases of projects implemented in developed countries, the findings show that there are few studies of PM and SD applications reported in developing countries (Torres, 2019). Therefore, the results from the SLR suggest expanding the number of studies which allow understanding the dynamics of the projects because of the constraint of resources over time, particularly in Latin America. The insertion of new PM methods also can be an opportunity to use the simulation as a way for planning the efficient use of existing resources.

Figure 6 shows the number of articles and identified variables in the SD models for each project performance. From the existing literature for PM and SD modelling, this review shows that 100% of articles considering the time performance in the projects, 78% considering the quality performance and 63% including the cost performance. These findings evidence the need to consider integrating the project performance indicators because of only 52% of the articles, including whole project performance. In other words, the results of the review show only 44 articles addressed simultaneously the cost, time and quality as a project performance indicator.

Figure 7 shows the identified variables in the SD models for the extended project life cycle: 11% included front-end of projects (Barbosa and Azevedo, 2018; Zhong et al., 2018), and 9% included back-end of projects (Cui et al., 2010; Yaghootkar and Gil, 2012). This study identified that most studies have been focused on evaluating the project delivery stage. The findings also disclosed that the back end of the project needs to be studied in more detail to understand post-learning about the project delivery. This issue should be seen as a challenge and opportunity because some organisations do not maintain an up-to-date record of post-project (Von Zedtwitz, 2002). Thus, more research on extended project life cycle might contribute to comprehend the complexity and uncertainty relationships which face the projects, particularly in Latin America. Besides, the PMBOK 7th edition could benefit from the addition of SD modelling to the PM to aid in decisions on adaptability and resilience aspects (Project Management Institute, 2021).

The PMI process group addressed by the SD models was reviewed in this paper based on the findings of the extended project life cycle – initiating, planning, executing, monitoring and controlling, and closing (PMBOK, 2017). As the simulation models are a valuable means to illustrate the behaviour of a PM process since scenario generation for the projects to be developed (Dugarte-Peña et al., 2021), this paper disaggregates the PMI process to identify the principal variables used by the SD models. Figure 8 highlighted the variables in the SD models identifying from the reviewed papers that use the PMI process group: 100% on the execution, 89% on the monitoring and controlling, 81% on the closing, 69% on planning and only 43% on the initiating. The systematic review shows that the SD models have a focus on the last PMI processes – executing, monitoring and controlling, and closing. Besides, the results identified the principal variables most often to model the rework cycle structure are “work to be done” for executing process and “work done” for closing process. A detailed summary of the most relevant variables identified in this review can be seen in Appendix 2.

This study identified that SD models for PM are focused on project delivery. Inside the project delivery, the modelling is referring to executing, monitoring and controlling, and closing process group focusing lowly on initiating and planning process group. In this sense, this research discussed three aspects: First, the link of the rework cycle SD model with PMI process group. To do this, we developed a CLD and then a SFD based on the SLR and assumptions proposed in Section 3.2. Second, we discussed the advantages and limitations using SD models for PM, and third, we proposed the future lines of research on the rework cycle SD models incorporated into the PM framework.

4.1 Linking the rework cycle SD model to the PMI process group

The CLD rework cycle model is showed in Figure 9 which has two reinforcing (R) loops and five balancing (B) loops. Cooper et al. (2002) established principal variables for the rework cycle structure such as “work to be done”, “work actually done”, “rework discovery”, “productivity”, “people” and “quality”. B1 established the relationship between work to be done and work done correctly. However, the activities to be done are specific for one process group, limiting the number of works to another process group. B2 sought the relationship between work actually done and work done correctly. Due to some work need to be added, B3, B4 and B5 are connected to the rework.

Reinforcing loop (R1) represents the relationship between the work to be done, the productivity and staff. Nevertheless, if staff are different from one process group to another, this model is limited. Last loop is R2, this loop relates R1 and B3 loop, in other words, rework to productivity and staff. In sum up, the rework cycle model is limited by adding work and changing staff in a process group. To solve these limitations, linking rework cycles is proposed to include PMI process group for project time span. This analysis is very important to decide on the project planning and development. That is, it recognises the needs to include additional resources along projects to adjust each project stage. It also is essential to recognise that strategic learning in each stage of projects helps build organisation-wide knowledge capabilities (Wiewiora, 2023). Thus, this context should be considered in the project planning because of limited resources, particularly in developing countries.

Linking rework cycles by using the process groups sets up the project effectively, as shown in Figure 10. A rework cycle represented each process group. The idea of using a rework cycle for each process group was because of the use of different staff, activities, costs and duration within each process group. Using detailed data for each process group represented a better approach for the project forecast span simulation, where the interaction of the phases (in this case, the process groups) simulates a better approximation of the project performance based on the triple constraint. Focusing on just carrying out the work (PMBOK, 2017), deployment (APM, 2019) or building (Morris and Geraldi, 2011) from the project life cycle does not have a representation of when the project is duly completed. In other words, the project managers should link multiple rework cycle along project life cycle that reduces the uncertainty for project completed. Besides, a quantitative approach allows analysing the measurable and patterns of the emerging approaches for the PM. That is, it helps the emerging approaches such as agile or agility approach (Pinho et al., 2022), considered as a new way of working more productively, and one in which the project team has greater control (Burga et al., 2022).

Figure 11 shows the interaction of linking the rework cycles to simulate the entire project. It shows how project phases and overall project success can be associated. These findings may align with the previous study by Ben Abdallah et al. (2022), which examined the relationship between the development phase and project success. Therefore, the simultaneity and sequence of project stages bring challenges to the management of resources, due to different delays that may occur. Particularly, the available resources are of utmost importance both in developing and developed countries.

5. Discussion

This section discusses the stated research questions in this paper for understanding the useful of SD modelling for PM. Section 5.1 shows a brief discussion about the benefits of connecting the rework cycle SD model with PMI process group. Section 5.2 explains the advantages and limitations of using SD modelling for PM. Section 5.3 proposes four potential research directions for future work.

5.1 Benefits of rework cycle SD model for the PMI process group

Despite there are several applications with a rework cycle, it has not been sufficiently studied. From the SLR, it was not possible to identify any studies in Latin America which use SD modelling as a tool for assessing triple constraints. Traditional PM focuses on the triple constraint or project performance: time, cost and quality (Svejvig et al., 2019; Zerjav et al., 2021). Nevertheless, the findings of our study indicate that the rework cycle SD models could evaluate the project performance from a dynamics and systemic perspective which incorporate the uncertainty and complexity of the projects (Nasirzadeh and Nojedehi, 2013; Ozcan-Deniz and Zhu, 2016; Zhong et al., 2018; Wu et al., 2019). In fact, one can use an SFD to represent the project performance and evaluate and control it in the short and long term.

Several studies have used SD simulations to assess and improve project performance through a rework cycle perspective (Lyneis et al., 2001; Lyneis and Ford, 2007; Cui et al., 2010; Godlewski et al., 2012; Ozcan-Deniz and Zhu, 2016; van Oorschot et al., 2018; Yan et al., 2019). Nevertheless, these studies have not deepened in modelling each PMI process group. Indeed, the stages of projects are all connected to each other. It should be noted that simulating only the executing process group suppresses the effects of other process groups and this impacts the project over time. The utilisation of rework cycle SD models can enhance understanding during the project life cycle. These models help depict the interaction between PMI process groups and offer insights into project process delays.

Delays can be a major concern for the project managers in project delivery. Thus, the qualitative modelling of information flows about the project life cycle helps to identify new challenges and opportunities for project delivery in the long run. A systemic intervention might create new insights into the project planning process. Nowadays, there is a lack of studies to address the complexity and uncertainty of the projects from a systemic intervention, considering the long-term effects.

5.2 Advantages and limitations of using SD modelling for PM

Although SD can explore complexity problems in a project, there are some advantages and limitations. This paper identifies advantages and limitations of using SD modelling for PM, as shown in Table 5. SD is usually used to understand project behaviour in the long-term as well as to analyse socio-technical complexity of projects. Nevertheless, the outputs from an SD model may suffer from the lack of accuracy due to missing data after finishing a project. Besides, SD is often criticised as it ignores the relationship between the macro level and micro-level in the system. It cannot give a deep understanding of the operational processes of the project.

SD focuses on the flows and feedback that allow simulating project’s dynamic behaviour. This is adequate to conceptualise the cumulative effects on the resources or capacities (stocks) used in the projects. In fact, SD models allow visualising behaviour in the long term of the interactions between different variables and decision-making from actors of a project. Nevertheless, the process of modelling requires expertise about the system’s dynamic behaviour, which limits the ability to identify operational issues that influence on performance PM. In this sense, the intellectual capital plays an essential role to model the projects and their relationships with sustainable economic performance (Yusliza et al., 2020; Jordão et al., 2022; Cheng et al., 2023). Thus, the spending time to build an SD model could be significantly higher according to ability of the modeller.

5.3 Future research directions for SD modelling simulation in PM

This paper identifies four distinctive future research lines that may assist PM to achieve both PM success (e.g. cost, time and quality) and project success (e.g. value and benefits). These research lines could help integrate rework cycle SD models into a formal evaluation of sustainability and innovation for PM.

First research line suggests the integration of PM with sustainability and innovation to contribute to interdisciplinary thought, which links respectively with project front-ending and back-ending. In other words, to extend project back-ending, which integrates the systems impact concept linked to sustainable project success (Calderon-Tellez et al., 2024). Future works in this field must incorporate a systemic approach to seamlessly blend diverse sustainability aspects into innovation systems. Project managers should use formal tools such as the simulation to improve the decision processes, considering connection between sustainability and innovation.

As other studies also have pointed up, it is possible to explain the role of innovation strategy on economic sustainability (Njoroge et al., 2020; Alonso Dos Santos et al., 2022). Herrera and Trujillo-Díaz (2022) show how a strategic innovation framework can integrate the concepts of innovation functions, dynamic performance management (DPM) and SD modelling. Likewise, Quintella et al. (2017) measured the financial risk effect on returns from innovation projects. In fact, recent studies show the importance of sustainability and innovation for PM (Martens and Carvalho, 2017; Davies et al., 2018; Stanitsas et al., 2021), even though these studies do not consider the modelling of complexity of project through the SD approach.

Second research line advocates qualitative systems modelling (i.e. CLDs) on the information flows for project decision-making. This is the development of simple dynamic hypothesis for future projects, which offers explanations by mental models for long range thinking and project risk planning (Ayala-Cruz, 2016; Vera et al., 2019). As the role of management information flow plays a crucial part for new product development or innovation (Mutum et al., 2019; Jordão, 2022; Castaneda et al., 2023), the qualitative modelling of information flows contributes to integrate sustainability and innovation (Singh et al., 2023). Integrating SD facilitates a systemic view that supports in the planning, executing and controlling agile projects. As agile project methodology is based on continuous delivery and integrating customer feedback, the SD methodology allows an ongoing monitoring of the performance of the projects, considering the cause-effect sequence.

Third research line asserts the use of quantitative SD modelling (e.g. SFDs as a hard systems methodology) for exploring sustainable project success. This enables long range environmental evaluation of aspects such as carbon dioxide (CO₂) emissions produced by projects (Calderon-Tellez and Herrera, 2023). Thus, a simulation model can improve the understanding of system and variables associated to the PM. Indeed, SD has contributed with a better understanding about the boundaries of a system because of that considers the dynamic and complex of the projects. In this line, project managers can extend your strategies by the simulation scenarios that allow to identify alternatives of decision.

Fourth research line suggests transdisciplinary thought which is guided by ideas associated with the systems movement. Uncertainty and complexity are inherent in PM, often leading to bounded rationality in decision-making (Daniel and Daniel, 2018). To mitigate this risk, developing SD models that integrate elements from various fields can enhance decision-making capabilities and improve project outcomes. Over time, SD models have evolved to include perspectives from diverse disciplines. For instance, some SD models incorporate health and epidemiological concepts to better simulate real-world dynamics. Others blend different methodological approaches, such as hybrid and adaptive methods, creating a more comprehensive toolset for PM (Gemino et al., 2020; Papadakis and Tsironis, 2022). This multidisciplinary integration allows for a more holistic understanding of complex project environments. Besides, this situation can prompt PM practitioners to utilise SD methodology approach to back current hybrid and adaptive methods.

There are multiple challenges and opportunities for Latin America countries regarding the PM. For instance, renewable energy projects in Latin America have surged exponentially in recent years, driven by their economic attractiveness and favourable financial conditions (Zapata et al., 2023). Nevertheless, the rework cycles caused by different drawbacks have brought delivery delays in the energy infrastructure projects (Leusin et al., 2024). In this context, our results demonstrate that simulation tools can assist project managers in identifying scenarios that minimise rework cycles. Besides, this issue can be a contemporary way of analysis the sustainability and innovation in Latin America.

6. Conclusion

This paper has discussed the role of the rework cycle SD model as a fundamental structure to be to assess PM by using CLD and SFD. The paper has given a response to the research questions: What are the benefits of connecting the rework cycle SD model with PMI process group? What are the advantages and limitations of using SD modelling for PM? What directions might inform future SD modelling in PM? To address these questions, the paper has presented a SLR and discussed the future lines of research for SD modelling simulation in PM. Hence, the paper offers new insights about the needs of modelling each PM process using the SD approach in rework cycle. In this sense, the paper has aimed that linking rework cycles represents a better approach to forecast project behaviour.

Although several papers have discussed the extended project life cycle and its performance (Jensen Oellgaard, 2013; Kivilä et al., 2017; Koke and Moehler, 2019), this paper shows how the simulation may be a better approximation of the project performance based on the triple constraint. Simulation serves as a valuable management tool for representing the PMI process groups (PMBOK, 2017), aiding in the assessment of cost reduction and the monitoring of schedule overruns. Given the current focus on project delivery, this paper shows that integrating the rework cycle SD model with the PMI process can provide significant benefits in evaluating the extended project life cycle (see Figure 9). Our results indicate that rework cycle simulations can help identify key elements of the PMI process groups that had previously remained unrecognised. Additionally, this could be a first step in evaluating the potential for incorporating agile or hybrid methodologies into the simulation models.

The number of rework cycles relies on the phases or process groups employed in project development. When there are changes in staff, activities or delays within a process group, it is advised to initiate another rework cycle. In simpler terms, when the volume of tasks grows, the amount of work done correctly also increases. Consequently, simulations could provide a superior method for evaluating new rework cycles compared to reality. Other studies have showed the useful of the SD models in assessing the rework cycle. For instance, Pargar et al. (2019) identified through an SD model that the rework process significantly influences the value created in a project. Since agility refers to the ability to respond quickly to changes and create value in uncertain environments (Sithambaram et al., 2021; Papadakis and Tsironis, 2022), the SD approach can help assign value to each project task and track value creation over time.

Using SD modelling has multiple advantages and benefits for PM. For instance, CLDs are used to understand project behaviour and conceptualisation of the project problem. Meanwhile, SFDs are used to understand complex systems, formulate project problem and simulate scenarios. Soft and hard variables are used in SD modelling for forecast and visualise project behaviour at long term. Besides, the SD simulation might be used to evaluate the agile and hybrid process within the PM ecosystem. The insertion of a simulation approach might help to evaluate the project from a systemic view, considering the managerial effects in the long-term (Sithambaram et al., 2021), given the agile management methods have improved the success rates of projects.

This paper focuses on showing how SD modelling contributes to a better understanding on rework cycle for traditional PM. Although the SD model discussed in this paper is not representing explicitly to agile and hybrid methodologies, the SD methodology approach has contributed to analyse varying cycle time, underestimation of tasks and requirements volatility as an essential aspect of applying the agile and hybrid methodologies. As future work, the agile and hybrid methodologies could potentially be extended to SD models.

In the Ibero-American context, the PM plays a significant role in the development and social gaps reduction. The planning of limited resources involves best management practices at the front-end project stage. A systemic perspective allows for a better understanding of the efficient use of resources. That is, the SD simulation can help the project managers with planning when the project has a limited resource as case of developing countries. In this way, our results show a connection between the rework cycle SD model and the PMI process group, providing a potential means of assessing options for enhancing project execution and delivery over the long haul (see, Figure 10). Nevertheless, there are challenges to the future research on modelling of the PM, which should consider the new project delivery principles, such as adaptability and resilience proposed by new PMBOK Seventh edition (Project Management Institute, 2021).

Informed by systems thinking, this paper outlines four key research directions for advancing PM using SD techniques:

(1)Enhancing interdisciplinary integration: emphasizing the need to incorporate interdisciplinary elements, particularly by integrating innovation and sustainability into PM models.

(2)Qualitative SD modelling for decision-making: advocating for the use of qualitative SD models, such as CLDs, as a “soft” methodology to support early-stage project decision-making.

(3)Quantitative SD modelling for sustainability: leveraging quantitative SD models, such as stock and flow diagrams (SFDs), as a “hard” systems approach to assess the long-term environmental impacts and sustainability of project outcomes.

(4)Transdisciplinary approaches for SD modelling: encouraging the adoption of transdisciplinary thinking in quantitative SD modelling, inspired by systems movement concepts (Checkland, 1981a) and the principles of bounded rationality (Morecroft, 1983; Simon, 1990).

Author Affiliation

Figure 1

Dynamic behaviour hypothesis for the rework cycle for a project

Figure 2

Methodology description

Figure 3

Simulated and historical behaviour reproduction test

Figure 4

Distribution of publications per year

Figure 5

Fields where SD modelling and PM have been blended

Figure 6

Identified variables in the SD models for project performance as triple constraint (cost, time and quality)

Figure 7

Identified variables in the SD models for the extended project life cycle in the front-end, project execution and back-end domains

Figure 8

Identified variables in the SD models for the project management process group

Figure 9

Causal loop diagram for the rework cycle model

Figure 10

General SFD structure of linking multiple rework cycles

Figure 11

Simulation results by linking multiple rework cycles

Table 1

SD modelling process

Table 2

Causal loop diagram notation

Table 3

Stock and flow symbols

Table 4

Systematic literature review planning

Steps	Search strategy	No. of articles	Search engine
1	Articles using search keywords queries: “project management” and “system dynamics”; Time Span: 1980–2023	120	Web of Science
		220	Scopus
2	Refine by categories: management (49), operations research management science (18), business (10)	62	Web of Science
	Filter by subject area: business, management, and accounting (99), decision sciences (34), social sciences (29)	130	Scopus
3	Merge articles from Web of Science and Scopus	137
4	Articles that use the rework cycle model	84

Source(s): Prepared by the authors

Table 5

Advantages and limitations of using system dynamics modelling for project management

Advantages	Limitations
Understand project behaviour and complex project	Lack of accuracy
Conceptualisation and formulation of project problem	Need to use tests for confidence in the model
Forecast and visualise behaviour at long term	SD modelling requires expertise
Capable of simulate “what happen if …” scenarios	Time-consuming for SD model generation

Source(s): Prepared by the authors

The Colombian Ministry of Science, Technology and Innovation Colciencias Scholarship Program No. 756 and the Colombian Army Resolution No. 3330 supported this work. We would also like to acknowledge Universidad Militar Nueva Granada (Grant, IMP-ECO-3402) for the support in carrying out this work.

Notes

1.The APM Body of knowledge is a collection of knowledge for the Association of Project Management.

References

Abbasi, A. and Jaafari, A. (2018), “Evolution of project management as a scientific discipline”, Data and Information Management, Vol. 2 No. 2, pp. 91-102, doi: 10.2478/dim-2018-0010.

Alonso Dos Santos, M., Huertas González-Serrano, M. and Staniewski, M.W. (2022), “Analytical editorial: ensuring the future of our world: innovation, management and governance for sustainable growth”, Academia. Revista Latinoamericana de Administración, Vol. 35 No. 2, pp. 117-130, doi: 10.1108/ARLA-07-2022-368.

Ammirato, S., Linzalone, R. and Felicetti, A.M. (2022), “The value of system dynamics' diagrams for business model innovation”, Management Decision, Vol. 60 No. 4, pp. 1056-1075, doi: 10.1108/MD-02-2021-0172.

APM (2019), APM Body of Knowledge, 7th ed., Princes Risborough, Buckinghamshire: Association for Project Management, Buckinghamshire.

Artto, K., Ahola, T. and Vartiainen, V. (2016), “From the front end of projects to the back end of operations: managing projects for value creation throughout the system lifecycle”, International Journal of Project Management, Vol. 34 No. 2, pp. 258-270, doi: 10.1016/j.ijproman.2015.05.003.

Ayala-Cruz, J. (2016), “Project risk planning in high-tech new product development”, Academia. Revista Latinoamericana de Administración, Vol. 29 No. 2, pp. 110-124, doi: 10.1108/ARLA-11-2015-0297.

Ayat, M., Imran, M., Ullah, A. and Kang, C.W. (2021), “Current trends analysis and prioritization of success factors: a systematic literature review of ICT projects”, International Journal of Managing Projects in Business, Vol. 14 No. 3, pp. 652-679, doi: 10.1108/IJMPB-02-2020-0075.

Barbosa, C. and Azevedo, A. (2018), “Hybrid modelling of MTO/ETO manufacturing environments for performance assessment”, International Journal of Production Research, Vol. 56 No. 15, pp. 5147-5171, doi: 10.1080/00207543.2017.1421788.

Barlas, Y. (1996), “Formal aspects of model validity and validation in system dynamics”, System Dynamics Review, Vol. 12 No. 3, pp. 183-210, doi: 10.1002/(SICI)1099-1727(199623)12:3<183::AID-SDR103>3.0.CO;2-4.

Bell, G., Pagano, R., Warwick, J. and Sato, C. (2019), Problem Structuring Approaches for Management of Projects: Demonstrating Successful Practice, Palgrave Macmillian, Gewerbestrasse, Switzerland.

Ben Abdallah, S., El–Boukri, S., Floricel, S., Hudon, P., Brunet, M., Petit, M. and Aubry, M. (2022), “A process–oriented framework to measure development performance and success of megaprojects”, International Journal of Project Management, Vol. 40 No. 6, pp. 685-702, doi: 10.1016/j.ijproman.2022.06.005.

Bing, L., Haojun, X., Yeting, H., Zhe, L., Tianhong, L. and Zhuqing, W. (2022), “Impact of local environmental standards on water environmental carrying capacity in large water diversion project: a system dynamics analysis in Shandong, China”, Journal of Cleaner Production, Vol. 362, 132423, doi: 10.1016/j.jclepro.2022.132423.

Brunet, M. (2022), “On the relevance of theory and practice in project studies”, International Journal of Project Management, Vol. 40 No. 1, pp. 22-24, doi: 10.1016/j.ijproman.2021.11.006.

Burga, R., Spraakman, C., Balestreri, C. and Rezania, D. (2022), “Examining the transition to agile practices with information technology projects: agile teams and their experience of accountability”, International Journal of Project Management, Vol. 40 No. 1, pp. 76-87, doi: 10.1016/j.ijproman.2021.10.004.

Calderon-Tellez, J., Bell, G., Sato, C. and Herrera, M.M. (2021), “Exploring project management through system dynamics modelling: revisiting the importance of the rework cycle”, in Memorias LVI Congreso Internacional CLADEA 2022, Sede Ejecutiva del Consejo Latinoamericano de Escuelas de Administración - CLADEA.

Calderon-Tellez, J.A. and Herrera, M.M. (2023), “Assessing renewable energy projects: a sustainability and innovation perspective”, in Herrera, M.M. (Ed.), Business Model Innovation for Energy Transition: A Path Forward towards Sustainability, Springer International Publishing, Cham, pp. 61-82, doi: 10.1007/978-3-031-34793-1_4.

Calderon-Tellez, J.A., Bell, G., Herrera, M.M. and Sato, C. (2024), “Project management and system dynamics modelling : time to connect with innovation and sustainability”, Systems Research and Behavioral Science, Vol. 41 No. 1, pp. 3-29, doi: 10.1002/sres.2926.

Castaneda, M., Herrera, M.M. and Méndez-Morales, A. (2023), “A simulation-based approach for assessing the innovation barriers in the manufacturing firms”, Technology in Society, Vol. 75, 102391, doi: 10.1016/j.techsoc.2023.102391.

Checkland, P.B. (1981a), “Science and the systems movement”, in Systems Behaviour, 3rd ed., The Open University, London, England, pp. 26-43.

Checkland, P.B. (1981b), Systems Thinking, Systems Practice, John Wiley & Sons, Chichester, UK.

Cheng, K.-T., Hsu, J.S.C., Li, Y. and Brading, R. (2023), “Intellectual capital and team resilience capability of information system development project teams”, Information & Management, Vol. 60 No. 1, 103722, doi: 10.1016/j.im.2022.103722.

Cooper, K.G. (1980), “Naval ship production–a claim settled and a framework built”, Interfaces, Vol. 10 No. 6, pp. 20-36, doi: 10.1287/inte.10.6.20.

Cooper, K.G., Lyneis, J.M. and Bryant, B.J. (2002), “Learning to learn, from past to future”, International Journal of Project Management, Vol. 20 No. 3, pp. 213-219, doi: 10.1016/S0263-7863(01)00071-0.

Cosenz, F., Rodrigues, V.P. and Rosati, F. (2019), “Dynamic business modeling for sustainability: exploring a system dynamics perspective to develop sustainable business models”, Business Strategy and the Environment, Vol. 29 April, pp. 1-14, doi: 10.1002/bse.2395.

Cui, Q., Hastak, M. and Halpin, D. (2010), “Systems analysis of project cash flow management strategies”, Construction Management & Economics, Vol. 28 No. 4, pp. 361-376, doi: 10.1080/01446191003702484.

Daniel, P.A. (2022), “Multi-level perspective framework in macro project studies: towards a complex project organizing approach to sustainability transitions”, International Journal of Project Management, Vol. 40 No. 8, pp. 865-870, doi: 10.1016/j.ijproman.2022.10.001.

Daniel, P.A. and Daniel, C. (2018), “Complexity, uncertainty and mental models: from a paradigm of regulation to a paradigm of emergence in project management”, International Journal of Project Management, Vol. 36 No. 1, pp. 184-197, doi: 10.1016/j.ijproman.2017.07.004.

Davies, A., Manning, S. and Söderlund, J. (2018), “When neighboring disciplines fail to learn from each other: the case of innovation and project management research”, Research Policy, Vol. 47 No. 5, pp. 965-979, doi: 10.1016/j.respol.2018.03.002.

de Toledo, R.F., Miranda Junior, H., Farias Filho, J. and Costa, H.G. (2019), “A scientometric review of global research on sustainability and project management dataset”, Data in Brief, Vol. 25, 104312, doi: 10.1016/j.dib.2019.104312.

Dugarte-Peña, G.-L., Sánchez‐Segura, M., de Amescua, A., Medina‐Domínguez, F. and Armenia, S. (2021), “Using system dynamics to teach about dependencies, correlation and systemic thinking on the software process workflows”, IET Software, Vol. 15 No. 6, pp. 351-364, doi: 10.1049/sfw2.12031.

Elia, G., Margherita, A. and Secundo, G. (2020), “Project management canvas: a systems thinking framework to address project complexity”, International Journal of Managing Projects in Business, Vol. 14 No. 4, pp. 809-835, doi: 10.1108/IJMPB-04-2020-0128.

Forrester, J.W. (1961), Industrial Dynamics, M.I.T. Press, Cambridge, MA, p. 197.

Garcia-Alvarez, M.T., Varela-Candamio, L. and Morollon, F.R. (2016), “Simulation in software engineering education: a system dynamics proposal”, International Journal of Engineering Education, Vol. 32 No. 4, pp. 1611-1617.

Gemino, A., Horner Reich, B. and Serrador, P.M. (2020), “Agile, traditional, and hybrid approaches to project success: is hybrid a poor second choice?”, Project Management Journal, Vol. 52 No. 2, pp. 161-175, doi: 10.1177/8756972820973082.

Godlewski, E., Lee, G. and Cooper, K.G. (2012), “System dynamics Transforms fluor project and change management”, Interfaces, Vol. 42 No. 1, pp. 17-32, doi: 10.1287/inte.1110.0595.

Goel, A., Ganesh, L.S. and Kaur, A. (2019), “Sustainability integration in the management of construction projects: a morphological analysis of over two decades' research literature”, Journal of Cleaner Production, Vol. 236, 117676, doi: 10.1016/j.jclepro.2019.117676.

Gupta, S.K., Gunasekaran, A., Antony, J., Bag, S. and Roubaud, D. (2019), “Systematic literature review of project failures: current trends and scope for future research”, Computers & Industrial Engineering, Vol. 127, pp. 274-285, doi: 10.1016/j.cie.2018.12.002.

Herrera, M.M. and Trujillo-Díaz, J. (2022), “Towards a strategic innovation framework to support supply chain performance”, International Journal of Productivity and Performance Management, Vol. 71 No. 5, pp. 1872-1894, doi: 10.1108/IJPPM-03-2020-0131.

Ika, L.A. and Pinto, J.K. (2022), “The ‘re-meaning’ of project success: updating and recalibrating for a modern project management”, International Journal of Project Management, Vol. 40 No. 7, pp. 835-848, doi: 10.1016/j.ijproman.2022.08.001.

Jackson, M.C. (2019), Critical Systems Thinking and the Management Complexity, John Wiley & Sons, Oxford.

Jensen Oellgaard, M. (2013), “The performance of a project life cycle methodology in practice”, Project Management Journal, Vol. 44 No. 4, pp. 65-83, doi: 10.1002/pmj.21357.

Jordão, R.V.D. (2022), “Editorial: information, knowledge, and technology in developing economies in times of crises”, The Bottom Line, Vol. 35 Nos 2/3, pp. 25-32, doi: 10.1108/BL-09-2022-134.

Jordão, R.V.D., Almeida, V.R.D and Novas, J. (2022), “Intellectual capital, sustainable economic and financial performance and value creation in emerging markets: the case of Brazil”, The Bottom Line, Vol. 35 No. 1, pp. 1-22, doi: 10.1108/BL-11-2021-0103.

Kapsali, M. (2011), “Systems thinking in innovation project management: a match that works”, International Journal of Project Management, Vol. 29 No. 4, pp. 396-407, doi: 10.1016/j.ijproman.2011.01.003.

Kapsali, M. (2013), “Equifinality in project management exploring causal complexity in projects”, Systems Research and Behavioral Science, Vol. 30 No. 1, pp. 2-14, doi: 10.1002/sres.2128.

Kivilä, J., Martinsuo, M. and Vuorinen, L. (2017), “Sustainable project management through project control in infrastructure projects”, International Journal of Project Management, Vol. 35 No. 6, pp. 1167-1183, doi: 10.1016/j.ijproman.2017.02.009.

Koke, B. and Moehler, R.C. (2019), “Earned Green Value management for project management: a systematic review”, Journal of Cleaner Production, Vol. 230, pp. 180-197, doi: 10.1016/j.jclepro.2019.05.079.

Leusin, M.E., Uriona Maldonado, M. and Herrera, M.M. (2024), “Exploring the influence of Brazilian project cancellation mechanisms on new wind power generation”, Renewable Energy, Vol. 221, 119755, doi: 10.1016/j.renene.2023.119755.

Lopes, J.D., Braga, J.L. and Resende, M.A. (2015), “Systems dynamics model for decision support in risk assessment in software projects”, Journal of Software-Evolution and Process, Vol. 27 No. 12, pp. 976-989, doi: 10.1002/smr.1754.

Lyneis, J.M., Cooper, K.G. and Els, S.A. (2001), “Strategic management of complex projects: a case study using system dynamics”, System Dynamics Review, Vol. 17 No. 3, pp. 237-260, doi: 10.1002/sdr.213.

Lyneis, J.M. and Ford, D.N. (2007), “System dynamics applied to project management: a survey, assessment, and directions for future research”, System Dynamics Review, Vol. 23 Nos 2-3, pp. 157-189, doi: 10.1002/sdr.377.

Magano, J., Silvius, G., Silva, C.S.e. and Leite, Â. (2021), “The contribution of project management to a more sustainable society: exploring the perception of project managers”, Project Leadership and Society, Vol. 2, 100020, doi: 10.1016/j.plas.2021.100020.

Martens, M.L. and Carvalho, M.M. (2017), “Key factors of sustainability in project management context: a survey exploring the project managers' perspective”, International Journal of Project Management, Vol. 35 No. 6, pp. 1084-1102, doi: 10.1016/j.ijproman.2016.04.004.

Morecroft, J.D.W. (1983), “System dynamics: portraying bounded rationality”, Omega, Vol. 11 No. 2, pp. 131-142, doi: 10.1016/0305-0483(83)90002-6.

Morris, P.W.G. (2013), Reconstructing Project Management, Wiley and Sons, Oxford, UK.

Morris, P.W.G. (2017), “Climate change and what the project management profession should be doing about it – a UK perspective”, Association for Project Management, Princes Risborough, available at: https://www.apm.org.uk/v2/media/jhhlitx0/climate-change-report.pdf (accessed 12 January 2024).

Morris, P.W.G. (2022), “A working account of the rise of project management”, International Journal of Project Management, Vol. 40 No. 2, pp. 91-94, doi: 10.1016/j.ijproman.2022.01.005.

Morris, P.W.G. and Geraldi, J. (2011), “Managing the institutional context for projects”, Project Management Journal, Vol. 42 No. 6, pp. 20-32, doi: 10.1002/pmj.20271.

Mutum, D.S., Ghazali, E.M. and Putit, L. (2019), “Guest editorial”, Bottom Line, Vol. 32 No. 4, pp. 249-252, doi: 10.1108/BL-12-2019-116.

Nasirzadeh, F. and Nojedehi, P. (2013), “Dynamic modeling of labor productivity in construction projects”, International Journal of Project Management, Vol. 31 No. 6, pp. 903-911, doi: 10.1016/j.ijproman.2012.11.003.

Nasirzadeh, F., Carmichael, D.G., Jarban, M.J. and Rostamnezhad, M. (2019), “Hybrid fuzzy-system dynamics approach for quantification of the impacts of construction claims”, Engineering Construction and Architectural Management, Vol. 26 No. 7, pp. 1261-1276, doi: 10.1108/ecam-08-2017-0150.

Nisa, C., Rita, V. and Chalid, D.A. (2022), “Impact of competition on microfinance institutions: bibliometric analysis and systematic literature review”, Heliyon, Vol. 8 No. 10, e10749, doi: 10.1016/j.heliyon.2022.e10749.

Njoroge, M., Anderson, W. and Mbura, O. (2020), “Innovation strategy and economic sustainability in the hospitality industry”, The Bottom Line, Vol. 32 No. 4, pp. 253-268, doi: 10.1108/BL-03-2019-0080.

Noto, G. and Cosenz, F. (2021), “Introducing a strategic perspective in lean thinking applications through system dynamics modelling: the dynamic value stream map”, Business Process Management Journal, Vol. 27 No. 1, pp. 306-327, doi: 10.1108/BPMJ-03-2020-0104.

Ogano, N.O. and Pretorius, L. (2017), “Analysis of policy options for projects in the electricity sector in sub-saharan africa: a system dynamics approach”, South African Journal of Industrial Engineering, Vol. 28 No. 1, pp. 20-35, doi: 10.7166/28-1-1607.

Oliva, R. (2003), “Model calibration as a testing strategy for system dynamics models”, European Journal of Operational Research, Vol. 151 No. 3, pp. 552-568, doi: 10.1016/S0377-2217(02)00622-7.

Ozcan-Deniz, G. and Zhu, Y.M. (2016), “A system dynamics model for construction method selection with sustainability considerations”, Journal of Cleaner Production, Vol. 121, pp. 33-44, doi: 10.1016/j.jclepro.2016.01.089.

Papachristos, G., Jain, N., Burman, E., Zimmermann, N., Mumovic, D., Davies, M. and Edkins, A. (2020), “Low carbon building performance in the construction industry: a multi-method approach of project management operations and building energy use applied in a UK public office building”, Energy and Buildings, Vol. 206, 109609, doi: 10.1016/j.enbuild.2019.109609.

Papadakis, E. and Tsironis, L. (2022), “Towards a hybrid project management framework: a systematic literature review on traditional, agile and hybrid techniques”, The Journal of Modern Project Management, Vol. 8 No. 2, JMPM02410, doi: 10.19255/JMPM02410.

Pargar, F., Kujala, J., Aaltonen, K. and Ruutu, S. (2019), “Value creation dynamics in a project alliance”, International Journal of Project Management, Vol. 37 No. 5, pp. 716-730, doi: 10.1016/j.ijproman.2018.12.006.

Pinho, C.R.A., Pinho, M.L.C., Deligonul, S.Z. and Tamer Cavusgil, S. (2022), “The agility construct in the literature: conceptualization and bibliometric assessment”, Journal of Business Research, Vol. 153, pp. 517-532, doi: 10.1016/j.jbusres.2022.08.011.

Pinto, J.K. (2022), “Avoiding the inflection point: project management theory and research after 40 years”, International Journal of Project Management, Vol. 40 No. 1, pp. 4-8, doi: 10.1016/j.ijproman.2021.11.002.

PMBOK (2017), A Guide to the Project Management Body of Knowledge, 6th ed., Project Management Institute, Newtown Square, PA.

Project Management Institute (2021), A Guide to the Project Management Body (PMBOK Guide), 7th ed., Project Management Institute, Newtown Square, PA.

Qudrat-Ullah, H. and Seong, B.S. (2010), “How to do structural validity of a system dynamics type simulation model: the case of an energy policy model”, Energy Policy, Vol. 38 No. 5, pp. 2216-2224, doi: 10.1016/j.enpol.2009.12.009.

Quintella, V.da M., Silva Jr, A.F.d.A.d., Almeida, J.R.U.C. and Embiruçu, M. (2017), “Financial exposure and technology innovation investment”, Academia. Revista Latinoamericana de Administración, Vol. 30 No. 4, pp. 547-564, doi: 10.1108/ARLA-06-2016-0165.

Richardson, G.P., Wolstenholme, E. and Morecroft, J.D.W. (1994), Systems Thinkers, Systems Thinking, John Wiley & Sons, Chichester.

Richardson, G.P. (1999), Feedback Thought in Social Science and Systems Theory, Pegasus Communications, Waltham, MA, available at: https://systemdynamics.org/shop/books/feedback-thought-in-social-science-and-systems-theory-1999/ (accessed10 January 2024).

Richmond, B. (1994), “Systems thinking/system dynamics: let's just get on with it”, System Dynamics Review, Vol. 10 Nos 2-3, pp. 135-157, doi: 10.1002/sdr.4260100204.

Sabini, L., Muzio, D. and Alderman, N. (2019), “25 years of ‘sustainable projects’. What we know and what the literature says”, International Journal of Project Management, Vol. 37 No. 6, pp. 820-838, doi: 10.1016/j.ijproman.2019.05.002.

Sadabadi, A.T. and Kama, N. (2014), “A conceptual-operative framework for in-process decision support of software project management practice”, International Journal of Software Engineering and its Applications, Vol. 8 No. 1, pp. 287-302, doi: 10.14257/ijseia.2014.8.1.25.

Sage, D., Dainty, A. and Brookes, N. (2014), “A critical argument in favor of theoretical pluralism: project failure and the many and varied limitations of project management”, International Journal of Project Management, Vol. 32 No. 4, pp. 544-555, doi: 10.1016/j.ijproman.2013.08.005.

Scales, J. (2020), “A design science research approach to closing the gap between the research and practice of project scheduling”, Systems Research and Behavioral Science, Vol. 37 No. 5, pp. 804-812, doi: 10.1002/sres.2743.

Silvius, A.J.G. (2017), “Sustainability as a new school of thought in project management”, Journal of Cleaner Production, Vol. 166, pp. 1479-1493, doi: 10.1016/j.jclepro.2017.08.121.

Silvius, G. and Marnewick, C. (2022), “Interlinking sustainability in organizational strategy, project portfolio management and project management A conceptual framework”, Procedia Computer Science, Vol. 196, pp. 938-947, doi: 10.1016/j.procs.2021.12.095.

Simon, H.A. (1957), Models of Man, Social and Rational: Mathematical Essays on Rational Human Behavior in a Social Setting, John Wiley and Sons, New York.

Simon, H.A. (1990), “Bounded rationality”, in Eatwell, J., Milgate, M. and Newman, P. (Eds), Utility and Probability, Palgrave Macmillan UK, London, pp. 15-18, doi: 10.1007/978-1-349-20568-4_5.

Singh, A.K., Pathak, D.K. and Patra, S. (2023), “An integrated systems thinking approach for achieving sustainability in project-based organizations”, Systems Research and Behavioral Science, Vol. 40 No. 3, pp. 501-535, doi: 10.1002/sres.2892.

Sithambaram, J., Nasir, M.H.N.B.M. and Ahmad, R. (2021), “Issues and challenges impacting the successful management of agile-hybrid projects: a grounded theory approach”, International Journal of Project Management, Vol. 39 No. 5, pp. 474-495, doi: 10.1016/j.ijproman.2021.03.002.

Sovacool, B.K. and Geels, F.W. (2021), “Megaprojects: examining their governance and sociotechnical transitions dynamics”, Environmental Innovation and Societal Transitions, Vol. 41, pp. 89-92, doi: 10.1016/j.eist.2021.10.002.

Stanitsas, M., Kirytopoulos, K. and Leopoulos, V. (2021), “Integrating sustainability indicators into project management: the case of construction industry”, Journal of Cleaner Production, Vol. 279, 123774, doi: 10.1016/j.jclepro.2020.123774.

Sterman, J.D. (2000), Business Dynamics: Systems Thinking and Modeling for a Complex World, McGraw-Hill, New York, NY.

Svejvig, P., Geraldi, J. and Grex, S. (2019), “Accelerating time to impact: deconstructing practices to achieve project value”, International Journal of Project Management, Vol. 37 No. 5, pp. 784-801, doi: 10.1016/j.ijproman.2018.12.003.

Torres, J.P. (2019), “System Dynamics Review and publications 1985-2017: analysis, synthesis and contributions”, System Dynamics Review, Vol. 35 No. 2, pp. 160-176, doi: 10.1002/sdr.1628.

Tranfield, D., Denyer, D. and Smart, P. (2003), “Towards a methodology for developing evidence-informed management knowledge by means of systematic review”, British Journal of Management, Vol. 14 No. 3, pp. 207-222, doi: 10.1111/1467-8551.00375.

van Oorschot, K.E., Sengupta, K. and Van Wassenhove, L.N. (2018), “Under pressure: the effects of iteration Lengths on agile software development performance”, Project Management Journal, Vol. 49 No. 6, pp. 78-102, doi: 10.1177/8756972818802714.

Vera, P., Nikulin, C., Lopez-Campos, M. and Gonzalez Ramirez, R.G.G. (2019), “Prospective study using archetypes and system dynamics”, Academia. Revista Latinoamericana de Administración, Vol. 32 No. 2, pp. 181-202, doi: 10.1108/ARLA-05-2017-0151.

Von Zedtwitz, M. (2002), “Organizational learning through post–project reviews in R&D”, R&D Management, Vol. 32 No. 3, pp. 255-268, doi: 10.1111/1467-9310.00258.

Wang, L., Kunc, M. and Bai, S.J. (2017), “Realizing value from project implementation under uncertainty: an exploratory study using system dynamics”, International Journal of Project Management, Vol. 35 No. 3, pp. 341-352, doi: 10.1016/j.ijproman.2017.01.009.

Wiewiora, A. (2023), “Identifying and managing persisting tensions affecting strategic learning from projects”, Long Range Planning, Vol. 56 No. 1, 102267, doi: 10.1016/j.lrp.2022.102267.

Williams, T.M., Eden, C., Ackermann, F. and Tait, A. (1995), “The effects of design changes and delays on project costs”, Journal of the Operational Research Society, Vol. 46 No. 7, pp. 809-818, doi: 10.1038/sj/jors/0460702.

Williams, T., Vo, H., Samset, K. and Edkins, A. (2019), “The front-end of projects: a systematic literature review and structuring”, Production Planning & Control, Vol. 30 No. 14, pp. 1137-1169, doi: 10.1080/09537287.2019.1594429.

Winter, M., Smith, C., Morris, P. and Cicmil, S. (2006), “Directions for future research in project management: the main findings of a UK government-funded research network”, International Journal of Project Management, Vol. 24 No. 8, pp. 638-649, doi: 10.1016/j.ijproman.2006.08.009.

Wu, C., Chen, C., Jiang, R., Wu, P., Xu, B. and Wang, J. (2019), “Understanding laborers' behavioral diversities in multinational construction projects using integrated simulation approach”, Engineering Construction and Architectural Management, Vol. 26 No. 9, pp. 2120-2146, doi: 10.1108/ECAM-07-2018-0281.

Yaghootkar, K. and Gil, N. (2012), “The effects of schedule-driven project management in multi-project environments”, International Journal of Project Management, Vol. 30 No. 1, pp. 127-140, doi: 10.1016/j.ijproman.2011.02.005.

Yan, M.R., Nhan, T.D. and Hong, L.Y. (2019), “Knowledge-based decision support system for improving e-business innovations and dynamic capability of IT project management”, Knowledge Management Research and Practice, Vol. 17 No. 2, pp. 125-136, doi: 10.1080/14778238.2019.1601507.

Yusliza, M.-Y., Yong, J.Y., Tanveer, M.I., Ramayah, T., Noor Faezah, J. and Muhammad, Z. (2020), “A structural model of the impact of green intellectual capital on sustainable performance”, Journal of Cleaner Production, Vol. 249, 119334, doi: 10.1016/j.jclepro.2019.119334.

Zapata, S., Castaneda, M., Herrera, M.M. and Dyner, I. (2023), “Investigating the concurrence of transmission grid expansion and the dissemination of renewables”, Energy, Vol. 276, 127571, doi: 10.1016/j.energy.2023.127571.

Zerjav, V., McArthur, J. and Edkins, A. (2021), “The multiplicity of value in the front-end of projects: the case of London transportation infrastructure”, International Journal of Project Management, Vol. 39 No. 5, pp. 507-519, doi: 10.1016/j.ijproman.2021.03.004.

Zhong, Y., Chen, Z., Zhou, Z. and Hu, H. (2018), “Uncertainty analysis and resource allocation in construction project management”, Engineering Management Journal, Vol. 30 No. 4, pp. 293-305, doi: 10.1080/10429247.2018.1492269.

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