Modeling and Simulation of Project Management through the PMBOK® Standard Using Complex Networks

Cardona-Meza, Luz Stella; Olivar-Tost, Gerard

doi:https://doi.org/10.1155/2017/4791635

Complexity

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Abstract Introduction Discussion and Conclusions Conflicts of Interest Acknowledgments References Copyright Related Articles

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Emerging Applications of Complex Networks

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Research Article | Open Access

Volume 2017 | Article ID 4791635 | https://doi.org/10.1155/2017/4791635

Modeling and Simulation of Project Management through the PMBOK® Standard Using Complex Networks

Luz Stella Cardona-Meza¹and Gerard Olivar-Tost²

Academic Editor: Dimitri Volchenkov

Received27 Aug 2017

Accepted12 Nov 2017

Published04 Dec 2017

Abstract

Discussion about project management, in both the academic literature and industry, is predominantly based on theories of control, many of which have been developed since the 1950s. However, issues arise when these ideas are applied unilaterally to all types of projects and in all contexts. In complex environments, management problems arise from assuming that results, predicted at the start of a project, can be sufficiently described and delivered as planned. Thus, once a project reaches a critical size, a calendar, and a certain level of ambiguity and interconnection, the analysis centered on control does not function adequately. Projects that involve complex situations can be described as adaptive complex systems, consistent in multiple interdependent dynamic components, multiple feedback processes, nonlinear relations, and management of hard data (process dynamics) and soft data (executive team dynamics). In this study, through a complex network, the dynamic structure of a project and its trajectories are simulated using inference processes. Finally, some numerical simulations are described, leading to a decision making tool that identifies critical processes, thereby obtaining better performance outcomes of projects.

1. Introduction

Projects have long been considered business practices of high value for organizations, with important results in general. Therefore, project management is considered a key factor for the success of projects and strategic objectives of companies [1]. In 1950, the social construct of project management was first introduced (in the United States Air Force). Its first proponent was Brigadier Bernard Schriever, who implemented the concept of concurrence, integrating all the elements of a project into a single program and budget, executed in parallel and not in sequence. Since then, specific techniques have arisen—histograms, chronograms, concepts of the life cycle of a project, and the work breakdown structure, which make up the knowledge base of the classic perspective of projects [2].

According to Padalkar and Gopinath [3], a significant part of the first studies on project management, which continued up to the 1980s, used conceptual or analytical methods. These methods focused on the optimization of scheduling based on the premise that project activities and their interrelations were fixed and measurable [4–8].

Subsequently, in the 1990s, new results of empirical experiments studying the success and failure of projects surfaced [9–22]. Defining the concepts of success and failure associated with projects and their management is not an easy task, and there is no consensus on their definition or measurement. According to Baccarini [10], there must be a distinction between the success of a project as measured by the fulfillment of the requirements of the end product, and the success of the project management as measured habitually in terms of time, costs, and quality [23].

The pursuit of the success/failure of projects has led to an expansion of research on organization contexts that are broader, behavioral, and interdisciplinary [9, 14, 17, 24–28]. This has promoted other research on such topics as contingencies, behavior, and governance in projects, interrelations between projects, decision making, and the perspective of complexity [2].

From the 1960s, a small deviation from the classic deterministic perspective started to emerge, in which nondeterministic processes began to be considered. This included criticism of PERT and the beta-distribution [29–31], and the treatment of project management as being not only deterministic [32]. The modeling of the uncertainty of project phenomena began to be considered as assumptions about attributes considered static broadened [33–39]. System dynamics began to be used for modeling the nonlinear effects of feedback loops in projects [40–43] and the modeling of projects under diffuse or probabilistic assumptions [44–48].

The dominant research focus has remained instrumentalist, with attempts to design models or methods of decision making with the goal of analyzing the performance of a project (e.g., the Project Management Body of Knowledge (PMBOK) standard from the Project Management Institute, which was founded in 1969 and its 5th edition was published in 2013).

The nondeterministic school of thought finds meaning in the weak theoretical nature of project management [1, 49–55].

From a brief exploration of literature, several attempts to model projects through assumptions related to complexity are found [56–61]. Several studies have theoretically discussed, defined, or provided constitutive elements of complexity [42, 50, 62–67, 67–73]. Other studies have done the same with regard to uncertainty [18, 60, 74–77].

In addition, the literature review provides evidence of the existence of several international organizations that have been expanding the body of knowledge of project management: ISO (21500), International Project Management Association, 1972, standard ICB 3.0, Association for Project Management, standard PRINCE2, Project Management Institute (PMI), 1969, PMBOK standard, International Centre for Complex Project Management, 2011, and New England Complex Systems Institute, amongst others.

For the purpose of this study and the evaluation of project management, the analysis is based on the PMBOK standard from the PMI, given its importance and international prevalence. It was chosen with the goal of describing an analytic structure of processes and as a tool for simulating complex networks used to evaluate project management nondeterministically.

1.1. Project Management as a Complex System

Complexity theory as applied to organizations [78] can also be applied to projects [42, 62]. All projects have attributes of interconnection, hierarchy, communication, control, and emergency, which are generally useful attributes for describing all types of systems [79]. In addition, most big and small projects exhibit characteristics of complex adaptive systems. They exhibit such characteristics as phase transitions, adaptability, and sensibility to initial conditions [79].

A complex project is a complex system made up of different elements interconnected to achieve an objective. Such a system can be described by a dynamic system, whose parts interact with each other and with their environment, and such interactions give rise to new properties that did not previously exist [80].

According to [79], the most important characteristics exhibited by complex projects, seen as complex adaptive systems, are as follows:(i)Auto-organization: a project can suffer two types of perturbations—those of an exogenic nature (relating to changes of its environment) and those of an endogenous nature (relating to internal attribute changes that modify the relationships within the system) [81]. After a given perturbation, the project is reorganized until a new emerging structure is adopted, which can be fixed as long as no new environment or internal parameter changes occur.(ii)Hierarchy: projects as systems might contain other systems—the members of the temporary executive organization of the project in turn belong to other subsystems. In addition, the structures of work breakdown form hierarchies for the execution of activities, and the project can be perceived from different levels depending on the interest of the observer and so on.(iii)Nonlinearity: small perturbations cause effects in projects. The result of a small variation in the exogenous inputs or endogenous parameters can lead to considerable variations in the system (either immediately or in the future), contrasting a linear effect.(iv)Adaptability: adaptive systems can reorder their internal structure without the intervention of an external agent. This property, which is the product of unconscious learning, increases the probability that the system survives turbulent and unstable environments.

The remainder of this article is structured in the following manner. In Section 2, the modeling of process dynamics is described based on the PMBOK standard, along with the creation of a complex network. Section 3 is dedicated to numerical simulation and the results obtained. Finally, Section 4 describes the conclusions.

2. Modeling of a Complex Network of Processes Based on the PMBOK Standard

This section describes a possible algorithm to model complex project management through the creation of a complex network, in which nodes are the different processes of the project, and edges are the exchanges of information between such.

2.1. Determination of Generalities

Research, such as [82], suggests an appropriate sequence to develop a project management plan based on the PMBOK and focused on network theory. This research analyzes the activities of a project from a classic and deterministic perspective, while the present work evaluates the behavior of project management from the perspective of complexity, describing a structure for analyzing processes as a model that evaluates the dynamics of connections through simulation in a complex network, and an analysis of the behavior of the characteristics of a complex project.

The methodological guide PMBOK, in its 5th edition (2013), describes five process groups and 10 knowledge areas that can be used to identify the relevant factors in arbitrary projects. The 10 knowledge areas are integration, scope, time, costs, quality, human resources, communications, risks, procurement, and stakeholders.

The five process groups are initiating, planning, executing, monitoring and controlling, and closing:(i)Initiating process group includes processes that define a new project or phase of an existing one, helping establish the vision and requirements. In this group, there are two subprocesses.(ii)Planning process group includes processes carried out to establish the scope of the plan, define and review the objectives, and develop an action plan to reach such objectives. In this group, there are 24 subprocesses.(iii)Executing process group includes processes targeted at completing the work defined in planning, with the goal of satisfying the requirements of such. In this group, there are eight subprocesses.(iv)Monitoring and controlling process group includes processes required to trace, analyze, and direct the progress and performance of the project, making necessary changes to it. In this group, there are 11 subprocesses.(v)Closing process group includes processes required to close a phase of the project or its entirety. In this group, there are two subprocesses.

2.2. Definition of the Analytic Structure of Processes

To model and simulate the analytic structure of processes, the following steps are defined.(i)Describe the subprocesses in terms of the flow of information.(ii)Define the model of connections between subprocesses.(iii)Determine parameters of the simulation.(iv)Present and discuss the results of the simulation of a complex network of subprocesses.

3. Simulation of the Complex Network of Processes or Subprocesses

In this section, subprocesses and the connections between them are described in terms of the flow of information. In addition, the parameters of the simulation are determined and the results of the simulations displayed.

3.1. Description of Subprocesses in terms of Information Flow

(a) There are 26 initiating and planning subprocesses. They are the following: develop the project charter (see the example in Figure 1), identify stakeholders, develop project management plan, plan scope management, collect requirements, define scope, create the EDT/WBS, plan schedule management, define activities, sequence the activities, estimate activity resources, estimate activity durations, develop schedule, plan cost management, estimate costs, determine budget, plan quality management, plan human resources management, plan communication management, plan risk management, identify risks, perform qualitative analysis, perform quantitative analysis, plan risk response, plan procurement management, and plan stakeholder management.

(b) There are 8 subprocesses in the executing process: direct and manage the work of the project (see an example in Figure 2), perform quality assurance, acquire project team, develop project team, manage project team, manage communications, conduct procurements, and manage stakeholder engagement.

(c) There are 11 subprocesses in the process of monitoring and controlling: monitoring and controlling project work, performing integrated change control, validating scope, controlling scope, controlling the schedule, controlling costs, controlling quality, controlling communications, controlling risks, controlling procurements, and controlling stakeholder engagement.

(d) There are two subprocesses in the closing process: close the project or phase and close the project or phase (procurements).

(e) Three new subprocesses are added (extending the PMBOK standard), and they are repository (contains the information of the project), exogenous (effects of the known environment variables), and novelties (effects of unknown variables).

As a result, 49 nodes are identified: 25 belonging to the subprocess of planning, 8 to the subprocess of execution, 11 to the subprocess of monitoring and controlling, 2 to the subprocess of closing, and 3 additional ones. In order to execute the activities of each subprocess, it is necessary for information to flow from other subprocesses and for new information to be generated, which flows into other subprocesses.

3.2. Connection Model between Subprocesses

Based on the subprocesses defined previously, relationships between these processes are defined as connections in the network, which are temporal. In addition, each subprocess is a node in the complex network.

The connections, or links, are established by the optimal relationship principle, which finds efficiencies between nodes and eliminates redundancies (i.e., not revisiting a node if it has been updated already). In order to elucidate this concept, consider the node “develop the project charter.”(i)The nodes that we label as sources are those that can send the following information: (1) exogenous node and/or (2) qualitative analysis of risk node.(ii)The nodes that we label as outputs are those that can receive information from (1) repository node, (2) developing project management plan, (3) planning scope management, (4) collecting requirements, (5) defining scope, (6) planning schedule management, (7) planning cost management, (8) planning risk management, and/or (9) identifying stakeholders.

When the node “develop the project charter” is activated by a source node, it processes the input information and then activates the output processes. These output processes in turn can activate other processes with which they have some relationship within the complex network. Thus, not only output node connections but also the secondary node sequence of the outputs is modeled.

3.3. Determining the Parameters of the Simulation

The conditions and hypothesis established in this subsection are the following, corresponding to the PMBOK standard.(i)The 49 subprocesses are determined based on PMBOK.(ii)It is assumed that there exists perfect information of the project in the planning phase and that the phases of executing, monitoring and controlling, and closing are carried out.(iii)The project considered is arbitrary, and the simulation attempts to evaluate the behavior of the project management are as defined by PMBOK.(iv)Each node is identified by a number, in order to simplify the simulation process.(v)Through color labeling, the dynamics of each node and connection can be established (see [83]):(a)green node, idle state(b)blue node, sending information state(c)yellow node, receiving information state(d)red node, processing information state(e)purple connection, active state sending information(f)black connection, inactive state(vi)The processing time of each node and the delay of the connection between two nodes are determined by a discrete uniform distribution that takes integer values between 1 and 11. This is done to exemplify the process, without setting the values to specific real cases, and these values are in the time units of the project.(vii)Connections are simulated concurrently. This takes place whenever a source node transfers information to several sinks, and when the sink node has several outputs (see [84–86]).(viii)The phases of project management are simulated in the following sequence: planning, executing, monitoring and controlling, and closing. This is also based on the PMBOK standard.

To illustrate the above, the subprocess “develop the project charter” is taken as an example, using a numerical identifier to simplify the simulation process, as follows: source nodes: exogenous (1) and qualitative analysis of risk (38); sink nodes: project charter (3); output nodes: repository (0), develop project management plan (4), plan scope management (9), collect requirements (10), define scope (11), plan schedule management (15), plan cost management (22), plan risk management (36), and identify stakeholders (46).

The aforementioned is described by Table 1, which contains 47 rows although only 1 is shown as an example.

Each output node can connect with other nodes and so on until the update/change of information is completed.

For example, when node 4, an output node, is updated/modified, it connects with the sink nodes, which in turn connect with the output nodes, and this process is repeated until all the related information is updated/modified (see Table 2). The table contains 403 rows although only a few are shown as an example.

The graphical representation of this dynamic, both of the nodes (which do not change) and the connections (which change with time), displays the different states as the complex network evolves. Using the previous example, this dynamic is depicted through the complex network represented in Figures 3, 4, 5, and 6, which form a sequence through time.

As depicted in this subsection, a change of states is observed, given by the dynamics of nodes and the dynamics of edges. For this example, activation starts at nodes 1 and 38, which process information; once they receive (red color), they then establish connections with node 3; once they do, nodes 1 and 38 send information (blue color), and node 3 receives information (yellow color), with an active connection (purple color). Then, node 3 processes the received information (red color). This process occurs repeatedly until connections ready for information processing are established.

Another way to understand the dynamics of nodes and connections, related to the previous example, is depicted in Figure 7 as a temporal network [87].(i)Initial state: nodes 1 and 38 process information concurrently (red color), in an amount of time determined by the discrete uniform distribution.(ii)Next state: nodes 1 and 38 transmit information (blue color), node 3 receives information (yellow color), and the connection between nodes 1-3 and 38-3 is active (purple color).(iii)Next state: node 3 processes information (red color), nodes 1 and 38 are idle (green color), and connections between nodes 1-3 and 38-3 are inactive (black color).(iv)The process continues.

3.4. Results and Discussion of the Complex Network of Subprocesses Simulation

The results are described based on the simulations performed with the software created for that end. Not all the graphs can be displayed owing to space constraints.

The results of the simulation are as follows.(i)Based on the discrete uniform distribution considered previously for calculating times, a duration of 21,068 units of time for the planning phase and of 4,115 time units for the executing phase is obtained (summing to a total of 25,183 time units up to the end of this phase). The monitoring and controlling phase has a duration of 5,028 time units (summing to 30,211 time units); and the closing phase has a duration of 90 time units (giving a total of 30,301 time units). Thus, the total time taken by the simulation is 30,301 time units. As mentioned previously, a discrete uniform distribution taking values between 1 and 11 is used, to determine both the duration of information processing of each node and the delay time of each connection between two nodes.(ii)There are 45,145 degrees of the complex network (90,290, input and output degrees) (see Figure 8). This means that the node repository has the greatest number of connections to other nodes given that it is where all the information of the project is stored. The rest of the nodes most relevant to the complex network are the following: develop project management plan, develop schedule, identify risks, and plan procurement management. Therefore, it can be concluded that these nodes are the most likely to be updated or modified. We later define these nodes as strong nodes, because they connect with the greatest number of other nodes.

The nodes with greater degree are the following.(i)Node repository (0), degrees: 12,194(ii)Node developing project management plan (4), degrees: 6,194(iii)Node developing schedule (20), degrees: 5,908(iv)Node identifying risks (37), degrees: 4,981(v)Node planning procurement management (42), degrees: 4,539.

The nodes with the smallest degree are the following.(i)Node planning scope management (9), degrees: 313(ii)Node planning cost management (22), degrees: 312(iii)Node planning schedule management (15), degrees: 323(iv)Node planning risk management (36), degrees: 344(v)Node planning risk responses (40), degrees: 353(vi)Node of qualitative analysis of risks (38), degrees: 354(vii)Node of quality control (28), degrees: 450.

Therefore, the node planning scope management has the least number of connections to other nodes. In addition, these nodes, where only the baselines of the project are determined (schedule, costs, and risks), are less likely to be modified/updated. Thus, they are less important than other nodes are for project management, and thus we define them as weak nodes in project management.

Below, the nodes with the greatest degrees in the complex network simulation are displayed (see Figures 9, 10, and 11). The planning phase starts at the beginning, the executing phase starts after 21000 time units, the monitoring and controlling phase starts when 25000 time units have passed, and the closing phase starts when the number of time units is 30000.

In Figure 9 corresponding to node 0 (repository), there is the same pattern of behavior of the degrees in this node throughout the five phases (planning, executing, monitoring and controlling, and closing). However, some peaks are noticeable during the planning phase.

In Figure 10 corresponding to node 20 (developing schedule), the behavior of the degrees of this node is high during the planning and execution phases. Thus, this node is likely to be modified/updated in these two phases but is not as likely to do so in the monitoring and controlling and closing phases.

Using the results of the simulation of all nodes, produced by the software developed for that goal, it is concluded that node 4 (developing project management plan) is the second highest degree node on average and is the node most vulnerable to changes/updates, given the high rate of modifications to it throughout the simulation. Therefore, it is a strong node in the network. Based on the results of the computation of node degrees, strong nodes and weak nodes can be identified in the complex network as a product of the project management simulation.

Strong nodes are defined as those with a higher degree during the simulation could be more vulnerable to modifications and have a higher degree of connections with other nodes. In the previous example, the strong nodes are 0, 4, 20, 7, and 42.

On the other hand, weak nodes are defined as those with a smaller degree during the simulation, those that could be less vulnerable to modifications, and those that have a lower degree of connections with other nodes. In the previous example, the weak nodes are 9, 22, 15, 36, 40, 38, and 28.

It can be inferred that if a strong node is not activated (e.g., from a lack of information), the project could be in a greater state of uncertainty.

The definition of strong and weak nodes, and their potential activation, is essential and new in complex project management given the elucidation of critical nodes. These critical nodes require more attention to succeed in projects of great complexity.

The following are other important measurements of the complex network.(i)Assortativity: −0,01052: If the value is <0, then the relationships in the network are established between nodes of different degree. In project management, this is because a node can in turn update/modify a secondary network, and nodes with higher degree interact with nodes with lower grade.(ii)Density: 18.42653: Such a high density is noteworthy, given that it could indicate a high effect of new information in the system because there are nodes connected to other nodes, those ones connected to others, and so on. Thus, updates produced by new or modified information add complexity to project management.(iii)Diameter: 3: it is the maximum distance between two nodes in the network. In project management, the diameter measurement yields a measure of how far away the nodes can be.(iv)Adjacency: the nodes with the greatest adjacency are the following.(a)Node 42 (plan procurement management) with node 37 (identify risks): adjacency value 1.055(b)Node 42 (plan procurement management) with node 0 (repository): adjacency value 903(c)Node 42 (plan procurement management) with node 10 (collect requirements): adjacency value 899(d)Node 20 (develop schedule) with node 4 (develop project management plan): adjacency value 776(e)Node 20 (develop schedule) with node 0 (repository): adjacency value 775(f)Node 19 (develop project management plan) with node 0 (repository): adjacency value 602.

Nodes with higher adjacency are those that are in groups of nodes with higher degree. In this case, they are node 42 (plan procurement management) and node 20 (develop schedule). These nodes in the project management are strong nodes that generate connections with a high number of other nodes, being strong nodes in the complex network.(i)Betweenness: nodes with the highest values are the following: node 0 (repository): 809.949, node 4 (develop project management plan): 486.7958, and node 20 (develop schedule): 123.04543. These are strong nodes through which the most amount of information passes, with the most amount of control over the network.(ii)Closeness: node 2 (novelties): 0.02; node 8 (close the project or phase): 0.342657343: For the analysis, node 2 can be ignored because throughout the simulation the possibility of novelties was not included. Thus, node 8 is closest to the center of the network. The node closest to the center of the network can be considered that where the simulation ends, because either a phase of the project ends there, or the entire project ends.(iii)Clustering: node 1 (repository) is the most connected node throughout the simulation, given that it is where all the information is stored.

4. Discussion and Conclusions

The classic perspective is not sufficient to understand the high number of interrelations established in the different phases of a complex project. Thus, project management should be approached from the perspective of complexity, and a complex project should be understood as a complex system. Using the algorithmic methodology established in this study, a modeling scheme can be constructed for any type of project. This is the main contribution of this work, and the stochastic simulation and subsequent analysis are one of the fundamental results of this research.

Weak nodes and strong nodes can be identified, thereby identifying the most vulnerable nodes to be modified/updated and those that, if not activated, could generate higher thresholds of uncertainty for a project. The strong nodes in the standard structure of PMBOK are node 4 (develop project management plan) and node 20 (develop schedule). The weak nodes are node 9 (plan scope management) and node 22 (plan cost management).

The strong nodes in the standard structure of PMBOK are node 4 (develop project management plan) and node 20 (develop schedule). The weak nodes are node 9 (plan scope management) and node 22 (plan cost management).

These results suggest that, given PMBOK, the scope and costs should have almost no changes/updates throughout the management of the project. In addition, the classic perspective assumes that behavior is deterministic from planning to closing of the project or phase. Thus, some rigidity can be established in the nature of the project when it is in a complex situation.

Given this, the following important question arises. What happens if nodes 4 and 20 disappear from the network (e.g., if they fail to be activated because of a lack of information)? To answer this question, new simulations are run in which nodes 4 (develop project management plan) and 20 (develop schedule) are not activated in the modeling of the network.

The results are as follows.(i)Degrees: 29,552, with a 32% decrease from the initial simulation (degrees: 90,290): This confirms that the mentioned nodes have a significant influence over the others and that the information outputted from such is important for the project management. Higher levels of uncertainty are generated by the absence of the nodes, which is key for the project.(ii)Density: it is 6.031020, with a 32% decrease from the initial simulation (density: 18.42653).(iii)Diameter becomes 4, increasing the maximum distance between two nodes in the network. This suggests that the nodes (4 and 20) are bridges that aid in the flow of information throughout the network.

Another conclusion is that increasing importance should be attributed to the use of tools based on the science of complexity in order to interpret complex project management, given that they provide additional information not available from classic-perspective tools.

Finally, the structure of the process network described can be used in the future to advance different types of simulations for any type of project. In particular, any sequence in the construction of the network can be simulated. As new information is delivered, the network updates the nodes/subprocesses as required.

This study establishes measurements of uncertainty, efficiency, and robustness, based on the complex network, which can be interpreted for the management of complex projects. This enables decision making prior to the start of the execution of a project. The report of this research will be described in a future article.

Conflicts of Interest

The authors declare that there are no conflicts of interest regarding the publication of this paper.

Acknowledgments

The authors thank Professor Rafael Rentería for his contributions to this article and his important reflections on complex projects. Gerard Olivar-Tost acknowledges DIMA Project Grant Modelamiento Avanzado de Mercados de Energía Eléctrica para Toma de Decisiones de Inversión y Establecimiento de Políticas Code 35467 from Universidad Nacional de Colombia.

References

S. J. Whitty and H. Maylor, “And then came complex project management (revised),” International Journal of Project Management, vol. 27, no. 3, pp. 304–310, 2009.
View at: Publisher Site | Google Scholar
P. W. G. Morris, J. R. Pinto, and J. Söderlund, “The oxford handbook of project management,” 2012.
View at: Google Scholar
M. Padalkar and S. Gopinath, “Are complexity and uncertainty distinct concepts in project management? a taxonomical examination from literatura,” International Journal of Project Management, 2016.
View at: Google Scholar
R. Kolisch, “Serial and parallel resource-constrained project scheduling methods revisited: theory and computation,” European Journal of Operational Research, vol. 90, no. 2, pp. 320–333, 1996.
View at: Publisher Site | Google Scholar
W. Herroelen, B. De Reyck, and E. Demeulemeester, “Resource-constrained project scheduling: a survey of recent developments,” Computers & Operations Research, vol. 25, no. 4, pp. 279–302, 1998.
View at: Publisher Site | Google Scholar
R. Kolisch and R. Padman, “An integrated survey of deterministic project scheduling,” Omega , vol. 29, no. 3, pp. 249–272, 2001.
View at: Publisher Site | Google Scholar
W. Herroelen and R. Leus, “Robust and reactive project scheduling: a review and classification of procedures,” International Journal of Production Research, vol. 42, no. 8, pp. 1599–1620, 2004.
View at: Publisher Site | Google Scholar
S. Hartmann and D. Briskorn, “A survey of variants and extensions of the resource-constrained project scheduling problem,” European Journal of Operational Research, vol. 207, no. 1, pp. 1–14, 2010.
View at: Publisher Site | Google Scholar | MathSciNet
C. S. Lim and M. Z. Mohamed, “Criteria of project success: an explanatory re-examination,” International Journal of Project Management, vol. 21, pp. 411–418, 1999.
View at: Google Scholar
D. Baccarini, “The Logical Framework Method for Defining Project Success,” Project Management Journal, vol. 30, no. 4, pp. 25–32, 1999.
View at: Google Scholar
T. J. Kloppenborg and W. A. Opfer, “The current state of project management research: trends, interpretations, and predictions,” Project Management Journal, vol. 33, no. 2, pp. 5–18, 2002.
View at: Google Scholar
A. S. Pillai, A. Joshi, and K. S. Rao, “Performance measurement of R&D projects in a multi-project, concurrent engineering environment,” International Journal of Project Management, vol. 20, no. 2, pp. 165–177.
View at: Google Scholar
D. Tesch, T. J. Kloppenborg, and J. K. Stemmer, “Project management learning: what the literature has to say,” Project Management Journal, vol. 34, no. 4, pp. 33–39, 2003.
View at: Google Scholar
J. R. Turner and R. Müller, “The project manager’s leadership style as a success factor on projects: a literature review,” Project Management Journal, vol. 36, no. 1, pp. 49–61, 2005.
View at: Google Scholar
K. Jugdev and R. Müller, “A retrospective look at our evolving understanding of project success,” Project Management Journal, vol. 36, no. 4, pp. 19–31, 2005.
View at: Google Scholar
S. Rozenes, G. Vitner, and S. Spraggett, Project control: literature review, Project Management Institute, 2006.
M. Huemann, A. Keegan, and J. R. Turner, “Human resource management in the project-oriented company: a review,” International Journal of Project Management, vol. 25, no. 3, pp. 315–323, 2007.
View at: Publisher Site | Google Scholar
G. P. Prabhakar, “What is project success: a literature review,” International Journal of Business and Management, vol. 3, no. 9, 2009.
View at: Publisher Site | Google Scholar
L. A. Ika, “Project success as a topic in project management journals,” Project Management Journal, vol. 40, no. 4, pp. 6–19, 2009.
View at: Publisher Site | Google Scholar
C. Barclay and K.-M. Osei-Bryson, “Project performance development framework: an approach for developing performance criteria & measures for information systems (IS) projects,” International Journal of Production Economics, vol. 124, no. 1, pp. 272–292, 2010.
View at: Publisher Site | Google Scholar
H. Wi and M. Jung, “Modeling and analysis of project performance factors in an extended project-oriented virtual organization (EProVO),” Expert Systems with Applications, vol. 37, no. 2, pp. 1143–1151, 2010.
View at: Publisher Site | Google Scholar
R. Müller and K. Jugdev, “Critical success factors in projects: Pinto, Slevin, and Prescott – the elucidation of project success,” International Journal of Managing Projects in Business, vol. 5, no. 4, pp. 757–775, 2012.
View at: Publisher Site | Google Scholar
A. de Wit, “Measurement of project success,” International Journal of Project Management, vol. 6, no. 3, pp. 164–170, 1988.
View at: Publisher Site | Google Scholar
T. Cooke-Davies, “The ‘real’ success factors on projects,” International Journal of Project Management, vol. 20, no. 3, pp. 185–190, 2002.
View at: Publisher Site | Google Scholar
A. Belout and C. Gauvreau, “Factors influencing project success: the impact of human resource management,” International Journal of Project Management, vol. 22, no. 1, pp. 1–11, 2004.
View at: Publisher Site | Google Scholar
D. Aloini, R. Dulmin, and V. Mininno, “Risk management in ERP project introduction: review of the literature,” Information and Management, vol. 44, no. 6, pp. 547–567, 2007.
View at: Publisher Site | Google Scholar
P. Littau, N. J. Jujagiri, and G. Adlbrecht, “25 Years of stakeholder theory in project management literature (1984–2009),” Project Management Journal, vol. 41, no. 4, pp. 17–29, 2010.
View at: Publisher Site | Google Scholar
M. Padalkar and S. Gopinath, “Delays in projects: a game-theoretic study,” in Trends and Research in the Decision Sciences: Best Papers from the 2014 Annual Conference, M. Warkentin, Ed., pp. 191–212, Pearson Education, Upper Saddle River, NJ, USA.
View at: Google Scholar
F. E. Grubbs, “Letter to the Editor—Attempts to validate certain PERT statistics or ‘Picking on PERT’,” Operations Research, vol. 10, no. 6, pp. 912–915, 1962.
View at: Publisher Site | Google Scholar
K. R. MacCrimmon and C. A. Ryavec, “An analytical study of the PERT assumptions,” Operations Research, vol. 12, no. 1, pp. 16–37, 1964.
View at: Publisher Site | Google Scholar
R. J. Schonberger, “Why projects are ‘always’ late: a rationale based on manual simulation of a PERT/CPM network,” Interfaces, vol. 11, no. 5, pp. 66–70, 1981.
View at: Publisher Site | Google Scholar
A. J. Shenhar and D. Dvir, Reinventing Project Management, Harvard Business School Press, 2007.
J. M. Burt, “Planning and dynamic control of projects under uncertainty,” Management Science, vol. 24, no. 3, pp. 249–258, 1977.
View at: Publisher Site | Google Scholar
T. M. Cook and R. H. Jennings, “Estimating a project’s completion time distribution using intelligent simulation methods,” Journal of the Operational Research Society, vol. 30, no. 12, pp. 1103–1108, 1979.
View at: Publisher Site | Google Scholar
T. M. Williams, “Criticality in stochastic networks,” Journal of the Operational Research Society, vol. 43, no. 4, pp. 353–357, 1992.
View at: Publisher Site | Google Scholar
R. A. Bowman, “Efficient estimation of arc criticalities in stochastic activity networks,” Management Science, vol. 41, no. 1, pp. 58–67, 1995.
View at: Publisher Site | Google Scholar
J. G. Cho and B. J. Yum, “An uncertainty importance measure of activities in PERT networks,” International Journal of Production Research, vol. 35, no. 10, pp. 2737–2758, 1997.
View at: Publisher Site | Google Scholar
S. E. Elmaghraby, Y. Fathi, and M. R. Taner, “On the sensitivity of project variability to activity mean duration,” International Journal of Production Economics, vol. 62, no. 3, pp. 219–232, 1999.
View at: Publisher Site | Google Scholar
C. Chapman and S. Ward, “Estimation and evaluation of uncertainty: a minimalist first pass approach,” International Journal of Project Management, vol. 18, no. 6, pp. 369–383, 2000.
View at: Publisher Site | Google Scholar
T. Williams, C. Eden, F. Ackerman, and A. Tait, “Vicious circles of parallelism,” International Journal of Project Management, vol. 13, no. 3, pp. 151–155, 1995.
View at: Publisher Site | Google Scholar
A. G. Rodrigues and T. M. Williams, “System dynamics in project management: assessing the impacts of client behaviour on project performance,” Journal of the Operational Research Society, pp. 2–15, 1998.
View at: Google Scholar
T. M. Williams, “The need for new paradigms for complex projects,” International Journal of Project Management, vol. 17, no. 5, pp. 269–273, 1999.
View at: Publisher Site | Google Scholar
C. Eden, T. Williams, F. Ackermann, and S. Howick, “The role of feedback dynamics in disruption and delay on the nature of disruption and delay (D and D) in major projects,” Journal of the Operational Research Society, vol. 51, no. 3, pp. 291–300, 2000.
View at: Publisher Site | Google Scholar
S. Chanas and P. Zieliński, “Critical path analysis in the network with fuzzy activity times,” Fuzzy Sets and Systems, vol. 122, no. 2, pp. 195–204, 2001.
View at: Publisher Site | Google Scholar
T. R. Browning and S. D. Eppinger, “Modeling impacts of process architecture on cost and schedule risk in product development,” IEEE Transactions on Engineering Management, vol. 49, no. 4, pp. 428–442, 2002.
View at: Publisher Site | Google Scholar
S. van de Vonder, E. Demeulemeester, W. Herroelen, and R. Leus, “The use of buffers in project management: the trade-off between stability and makespan,” International Journal of Production Economics, vol. 97, no. 2, pp. 227–240, 2005.
View at: Publisher Site | Google Scholar
C. Jensen, S. Johansson, and M. Löfström, “Project relationships-a model for analyzing interactional uncertainty,” International Journal of Project Management, vol. 24, no. 1, pp. 4–12, 2006.
View at: Google Scholar
S.-P. Chen, “Analysis of critical paths in a project network with fuzzy activity times,” European Journal of Operational Research, vol. 183, no. 1, pp. 442–459, 2007.
View at: Publisher Site | Google Scholar
A. J. Shenhar and D. Dvir, “Toward a typological theory of project management,” Research Policy, vol. 25, no. 4, pp. 607–632, 1996.
View at: Publisher Site | Google Scholar
A. J. Shenhar, “One size does not fit all projects: exploring classical contingency domains,” Management Science, vol. 47, no. 3, pp. 394–414, 2001.
View at: Publisher Site | Google Scholar
J. Söderlund, “Building theories of project management: past research, questions for the future,” International Journal of Project Management, vol. 22, no. 3, pp. 183–191, 2004.
View at: Publisher Site | Google Scholar
S. Cicmil, T. Williams, J. Thomas, and D. Hodgson, “Rethinking project management: researching the actuality of projects,” International Journal of Project Management, vol. 24, no. 8, pp. 675–686, 2006.
View at: Publisher Site | Google Scholar
H. J. Smyth and P. W. Morris, “An epistemological evaluation of research into projects and their management: methodological issues,” International Journal of Project Management, vol. 25, no. 4, pp. 423–436, 2007.
View at: Publisher Site | Google Scholar
P. W. G. Morris, “Research and the future of project management,” International Journal of Managing Projects in Business, vol. 3, no. 1, pp. 139–146, 2010.
View at: Publisher Site | Google Scholar
M. Jacobsson and A. Söderholm, “Breaking out of the straitjacket of project research: in search of contribution,” International Journal of Managing Projects in Business, vol. 4, no. 3, pp. 378–388, 2011.
View at: Publisher Site | Google Scholar
S. Austin, A. Newton, J. Steele, and P. Waskett, “Modelling and managing project complexity,” International Journal of Project Management, vol. 20, no. 3, pp. 191–198, 2002.
View at: Publisher Site | Google Scholar
S. Howick and C. Eden, “The impact of disruption and delay when compressing large projects: going for incentives?” Journal of the Operational Research Society, vol. 52, no. 1, pp. 26–34, 2001.
View at: Publisher Site | Google Scholar
W. Xia and G. Lee, “Grasping the complexity of is development projects,” Communications of the ACM, vol. 47, no. 5, pp. 68–74, 2004.
View at: Publisher Site | Google Scholar
W. Xia and G. Lee, “Complexity of information systems development projects: conceptualization and measurement development,” Journal of Management Information Systems, vol. 22, no. 1, pp. 45–83, 2005.
View at: Google Scholar
S. H. Cho and S. D. Eppinger, “A simulation-based process model for managing complex design projects,” IEEE Transactions on Engineering Management, vol. 52, no. 3, pp. 316–328, 2005.
View at: Publisher Site | Google Scholar
M. Danilovic and T. R. Browning, “Managing complex product development projects with design structure matrices and domain mapping matrices,” International Journal of Project Management, vol. 25, no. 3, pp. 300–314, 2007.
View at: Publisher Site | Google Scholar
D. Baccarini, “The concept of project complexity—a review,” International Journal of Project Management, vol. 14, no. 4, pp. 201–204, 1996.
View at: Publisher Site | Google Scholar
M. T. Pich, C. H. Loch, and A. De Meyer, “On uncertainty, ambiguity, and complexity in project management,” Management Science, vol. 48, no. 8, pp. 1008–1023, 2002.
View at: Publisher Site | Google Scholar
S. C. Sommer and C. H. Loch, “Selectionism and learning in projects with complexity and unforeseeable uncertainty,” Management Science, vol. 50, no. 10, pp. 1334–1347, 2004.
View at: Publisher Site | Google Scholar
H. Benbya and B. McKelvey, “Toward a complexity theory of information systems development,” Information Technology and People, vol. 19, no. 1, pp. 12–34, 2006.
View at: Publisher Site | Google Scholar
T. Cooke-Davies, S. Cicmil, L. Crawford, and K. Richardson, “Mapping the strange landscape of complexity theory, and its relationship to project management,” Project Management Journal, vol. 38, no. 2, pp. 50–61, 2007.
View at: Google Scholar
J. Geraldi and G. Adlbrecht, “On faith, fact, and interaction in projects,” Project Management Journal, vol. 38, no. 1, pp. 32–43, 2007.
View at: Google Scholar
H. Maylor, R. Vidgen, and S. Carver, “Managerial complexity in project based operations: a grounded model and its implications for practice,” Project Management Journal, vol. 39, no. S1, pp. S15–S26, 2008.
View at: Google Scholar
L.-A. Vidal and F. Marle, “Understanding project complexity: implications on project management,” Kybernetes, vol. 37, no. 8, pp. 1094–1110, 2008.
View at: Publisher Site | Google Scholar
Girmscheid and Brockmann, “The inherent complexity of large scale engineering projects,” in The Annual Publication of International Project Management Association, vol. 29, pp. 22–26, 2008.
View at: Google Scholar
T. Brady and A. Davies, “From hero to hubris? reconsidering the project management of Heathrows Terminal 5,” International Journal of Project Management, vol. 28, no. 2, pp. 151–157, 2010.
View at: Publisher Site | Google Scholar
S. Lenfle, “The strategy of parallel approaches in projects with unforeseeable uncertainty: the Manhattan case in retrospect,” International Journal of Project Management, vol. 29, no. 4, pp. 359–373, 2011.
View at: Publisher Site | Google Scholar
R. V. Ramasesh and T. R. Browning, “A conceptual framework for tackling knowable unknown unknowns in project management,” Journal of Operations Management, vol. 32, no. 4, pp. 190–204, 2014.
View at: Publisher Site | Google Scholar
M. V. Tatikonda and S. R. Rosenthal, “Technology novelty, project complexity, and product development project execution success: a deeper look at task uncertainty in product innovation,” IEEE Transactions on Engineering Management, vol. 47, no. 1, pp. 74–87, 2000.
View at: Publisher Site | Google Scholar
J. R. Turner and R. Müller, “On the nature of the project as a temporary organization,” International Journal of Project Management, vol. 21, no. 1, pp. 1–8, 2003.
View at: Publisher Site | Google Scholar
S. Ward and C. Chapman, “Transforming project risk management into project uncertainty management,” International Journal of Project Management, vol. 21, no. 2, pp. 97–105, 2003.
View at: Publisher Site | Google Scholar
R. Atkinson, L. Crawford, and S. Ward, “Fundamental uncertainties in projects and the scope of project management,” International Journal of Project Management, vol. 24, no. 8, pp. 687–698, 2006.
View at: Publisher Site | Google Scholar
P. Anderson, “Complexity theory and organization science,” Organization Science, vol. 10, no. 3, pp. 216–323, 1999.
View at: Publisher Site | Google Scholar
K. Remington and J. Pollack, “Tools for complex project,” 2010.
View at: Google Scholar
D. Arellano, J. Danti, and M. F. Pérez, Proyectos y Sistemas, PMI-INCOSE, 2016, (Spanish).
R. García, Sistemas Complejos, Editorial GEDISA, 2006 (Spanish), (Spanish).
C. Ruiz-Martin and D. J. Poza, “Project configuration by means of network theory,” International Journal of Project Management, vol. 33, no. 8, pp. 1755–1767, 2015.
View at: Publisher Site | Google Scholar
P. Holme and J. Saramaki, “Temporal networks,” Physics Reports, vol. 519, no. 3, pp. 97–125, 2012.
View at: Google Scholar
S. Boccaletti, V. Latora, Y. Moreno, M. Chavez, and D. W. Hwang, “Complex networks: structure and dynamics,” Physics Reports, vol. 424, no. 4-5, pp. 175–308, 2006.
View at: Publisher Site | Google Scholar
S. Boccaletti, G. Bianconi, R. Criado, C. I. del Genio et al., “The structure and dynamics of multilayer networks,” Physics Reports, vol. 544, no. 1, pp. 1–122, 2014.
View at: Publisher Site | Google Scholar | MathSciNet
H. Sayama, Introduction to the Modeling and Analysis of Complex Systems, 2015.
A. K, Network Analysis Literacy: A Practical Approach to The Analysis of Networks, Springer, 2016.

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Copyright © 2017 Luz Stella Cardona-Meza and Gerard Olivar-Tost. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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