Dimensions of project complexity and their impact on cost estimation
Leon Herszon, MSc, PMP
PhD student, University of Huddersfield
VP, International Institute for Learning (IIL)
Kaushal Keraminiyage, PhD
University of Huddersfield
To this day, most projects still do not meet their objectives, even with all the knowledge and best practices learned so far. According to The Standish Group (2009), just 32% of all projects surveyed were successful in delivering the required product with all its features and functions, and on time and on budget. This statistic includes any type of project, whether complex or not. Recent studies are increasingly suggesting that highly complex projects are adding significant challenge due to the impact of complexity and its different factors, hereon called dimensions. (Cooke-Davies, Crawford, Patton, Stevens, & Williams, 2011), (Baccarini, 1996).
One important way to look and define complexity is a situation with a large amount of elements interconnected and interdependent but whose influence on each other cannot be predicted. Accordingly, this study has identified 23 potential complexity dimensions related to project management and is considering their relevance to preparing project cost estimates. Since at present the traditional cost estimation processes (Project Management Institute, 2012) do not take into consideration the dynamic nature of complexity, a model that incorporates complexity dimensions in cost estimation may be beneficial in addressing this limitation. A proposed draft model is presented in this paper.
Complex or Complicated?
First and foremost there is a need to clarify the difference between complicated and complex. While complexity is a common word, there is not yet a full and commonly accepted answer to what it is. Different people think of different meanings when they use the word, and there is still a great deal of confusion about it; and a clarification and concept proposition for this term might be helpful to future researchers. A good start would be to understand the origin of complicated and complex.
The word complicated comes from the Latin “complicatus” (past participle of “complicare”) which means “to fold together,” and is related to projects that have a large amount of parts that are interconnected and interdependent. Complex, on the other hand, comes from the Latin “complexus” and “complecti,” which mean “to entwine,” and this word is related to projects where each individual part can change and where each change might or not affect the other parts (Cooke-Davies et al., 2011).
But what exactly is the modern meaning of complexity? Is it just one word, or does it involve multiple variables and what are these variables? What is the best approach to understand complexity? To answer these questions, there is a need to understand what has been presented in the literature better.
Starting with Bar-Yam (2004), one may realize that complexity is not just a matter of the size, the duration, or the number of parts that a specific system has. Complex problems are the ones that do not have an immediate resolution and would persist once they appear. In other words, the resolution of complex problems is not easily done (Bar-Yam, 2004). The more the human knowledge and especially technology advances, the more the existing systems become complex. Organizations are becoming more complex and dealing with increasingly complex environments. One decision by an individual could impact an entire organization, or a decision of one region may impact the entire world. Today's interdependence of financial markets is just one example.
Another important way to define complexity is how hard (or easy) it is to describe the system. One can describe something by using words (written or verbal), graphical representations, comparisons, and the like. If the understanding of a system is proportional to the understanding of its description, one also concludes that it should be directly related to how well the presenter describes the whole, the parts, and their relationship, but it also depends on the recipient's level of knowledge (Bar-Yam, 2004). For example, how would someone describe a chair to a two-year old child or to an adult? A poor description will create incomplete or faulty understanding, which will directly affect the success to deal with that system.
Edmunds (1999, p. 72) defines complexity as the “property of a model which makes it difficult to formulate its overall behavior in a given language, even when given reasonably complete information about its atomic components and their inter-relations”
Looking into the behavior and interrelations of the components, Simon (1982) states that complex systems are made of a great number of multiple interacting components in which it is difficult to understand the behavior of each individual component or to predict the behavior of the entire system, based on what is known of the starting conditions. Complementing this view, Williams (2001) considers complexity as a condition between numerous elements in a system and the numerous forms they can relate to each other.
The Project Management Institute (PMI), which is dedicated to share best practices on project management, states that “complexity is a characteristic of a program or project or its environment that is difficult to manage due to human behavior, system behavior, and ambiguity” (PMI, 2014, p. 12). Furthermore, the International Centre for Complex Project Management (ICCPM), which is dedicated to developing research and delivering education and support services related to project complexity, states that “complex projects are open, emergent and adaptive systems that are characterised by recursiveness and nonlinear feedback loops” (ICCPM, 2012).
A conclusion is that complex problems do not have one single solution that can be effectively used. Each situation might need a different form of resolution, and different complex systems perspectives might also be needed. One example of looking at complexity suggests that there are different possible approaches that would result in failure, but a very limited number (if not only one) that will result in success. The more complex a situation is, the harder it is to find the limited options that may lead to success.
Based on the above, a working definition of complexity for this research is presented below by the author of this paper. It assumes that one does not have knowledge or control to establish the outcome of the interaction between the parts.
According to this definition, complexity is “a dynamic state that has an unknown outcome and an increased level of difficulty since one does not know if or how each part affects or is affected by the other(s).”
The Cost Estimation Process
Cost overruns are common in modern-day projects. Recent statistics reported by Rolstadås, Hetland, Jergeas, and Westney (2011) shows that the original estimate, made in 2003 for the facilities required by the London Olympics of £ 4 billion, was revised in 2007 to a sum of £ 9.3 billion, and will actually require another £ 5 billion for associated transport projects. Furthermore, the authors report on the cost overruns in the Shell Sakhalin-2 project, which started with an original estimate of $10 billion and currently is estimated at over $28 billion.
More examples are provided by Flyvbjerg (2005), who notes that the Eurotunnel (between theUK and France) was delivered 80% over budget for construction and 140% over budget for for financing. The International Space Station had a $5 billion cost overrun. Boston's Central Artery/Tunnel project was 275% over budget for construction and $11 billion over its financial budget. The Denver International Airport was 200% and $5 billion over these budget, respectively. The Bangkok Skytrain cost $2 billion and did not realize half of its estimated benefits.
These statistics show the reality of how the traditional cost estimation process fails when dealing with complexity and complex projects. Even though different approaches for cost estimation can be found, the one provided by the Project Management Institute (PMI) has more clarity and broader use worldwide. Hence it is considered as the base practice for cost estimation within the scope of this research.
The PMI published two main references on this topic: A Guide to the Project Management Body of Knowledge (PMBOK Guide®)—Fifth Edition (2012) and the Practice Standard for Project Estimating (2011), which offers a clear and easy-to-follow process for cost estimation in projects as can be seen on the Exhibit 01 and Exhibit 02, respectively.
Cost Estimation Process
The project management processes presented by PMI are divided into Inputs (what needs to be considered for each process to be executed), Outputs (the expected results or deliverables of that process), and Tools and Techniques (for use in transforming the inputs into outputs).
Exhibit 01–Process of cost estimation (PMI, 2012)
Similarly but in a more concise way, the Practice Standard for Project Estimating (PMI, 2011) also describes the estimation approach as presented below.
Exhibit 02–Create estimates process (PMI, 2011)
Cost Estimation Techniques
According to the literature, there are many techniques to estimate costs on projects, and practitioners with great experience have used them for decades. They range from analogous (top-down), to parametric, to definitive (bottom-up) methods. These techniques are well-documented in various parts of the literature, however, Exhibit 03 below helps to provide better understanding for non-practitioners.
Exhibit 03–Types of estimating techniques in the context of decomposing a WBS (PMI, 2011)
The techniques presented are based on the best practices of cost estimation but do not consider the influence of complexity dimensions explicitly. The only dimension of complexity that is present is risk, which has been an aspect of traditional project management for several decades.
One aspect that seems relevant is that estimates are normally produced by technical people (i.e.,engineers), who, by nature, are optimistic They normally think that the work can be done, that a solution can be found, even though there are several complexity factors like interdependency of elements, uncertainty, innovation, and politics (Cooke-Davies et al., 2011).
Currently, a cost estimate is often produced based on a standard, “one-size-fit-all” approach. Hence the chances for that estimate to be accurate are low, often leading to projects cost overruns. For that reason, it is important to take into consideration the potential dimensions of project complexity.
Dimensions of Complexity
As Merrow (2011) discusses, the quantity of complex projects is growing and will continue to grow. While the importance of these projects for society is great, these projects face great challenges and often fail at a greater rate, especially due to cost overruns. Not much work was done in the past on how to deal with such issues.
Within the literature, it is common to find statements about the problems that complexity can bring to projects, like overestimation of benefits, underestimation of costs, failure to meet client requirements, misalignment between stakeholders with different and competing views and goals, the lack of right resources to manage the project, and the lack of proper tools to manage complex projects, leading to overwork and low performance (Hass, 2009), (Flyvbjerg, Bruzeluis, & Rothengatter, 2010), (Cooke-Davies et al., 2011).
Even though complicated projects can be managed using existing processes and best practices, the same approach might not work on complex projects because they exhibit non-linearity, and emergent behavior (Remington, Zolin, & Turner, R. 2009) (Baccarini, 1996) defines project complexity as consisting of many varied interrelated parts.
The ten knowledge areas presented by the Project Management Institute (PMI, 2012)—Scope, Time, Cost, Quality, Human Resources, Communication, Risks, Stakeholders, Procurement, and Integration—do not deal with competing demands commonly present in complex projects. According to several authors, the standard project lifecycle (PLC) approach presents severe limitations, and a new paradigm should be developed (Flyvbjerg, Bruzeluis, & Rothengatter, 2010), (Klakegg, Williams, Walker, Andersen, & Magnussen, 2010), (Baker & English, 2011), Rolstadås, Hetland, Jergeas, & Westney, 2011).
With that in mind, an analysis was conducted which found 23 potential complexity dimensions that could impact projects. The methodology of the analysis involved first performing a literature review for references to dimensions of project complexity. Sixteen dimensions were referred to at least twice during the search and are listed below with brief descriptions in order of the most referenced.
Dimensions Identified in Literature Review:
- Dependency and Interdependency: This dimension deals with the relationship between the elements that make up the project. This relationship can be one of dependency (in which some elements are dependent on one another, and some are not) and/or interdependency (in which each element is mutually dependent on others). In complex projects, you might not know what the result of an interaction between the elements of that project will be. (Remington & Polack, 2007), (Herbemont & César, 1998), (Baccarini, 1996), (Vidal & Marle, 2008), (Williams, 2001), (Danilovic & Browning, 2007), (Ivory & Aldeman, 2007), (Cicmil, Crawford, Richardson, &Cooke-Davies, 2007) (Geraldi, 2008), (Kerzner and Belack, 2010), (Remington et al., 2009), (Levin & Ward, 2011), (Bar-Yam, 2004).
EXAMPLE: The weather forecast, where we don't know how, or if, a specific event occurring in another part of the world will impact our local weather—as in the“butterfly effect.”
- Innovation to Market: This dimension is related to the level of innovation or uniqueness of the product generated by the project during the timeframe from when its creation to its release. Innovation level impacts market-related activities, time, and effort to define and “freeze” requirements. The higher the innovation level, the more difficult it is to establish and keep the requirements as originally defined. (Remington and Polack, 2007), (Herbemont & César, 1998), (Baccarini, 1996), (Vidal and Marle, 2008), (Williams, 2002), (Shenhar & Dvir, 2007), (Shenhar, Zhao, Melamed, & Holzmann, 2012), (Luhmann & Boje, 2001).
EXAMPLE: Releasing a product with a new color involves a lower level of innovation than when the first Post-it® note was launched in the market.
- Technology: The term technology is used here in its broader meaning. It is not limited to information technology but refers to any technology that needs to be used on a specific project. Normally technology complexity is found on projects that use a new or untried technology. The less the team or performing organization knows or has used that technology, the more complex the project will become. (Remington & Polack, 2007), (Herbemont & César, 1998), (Baccarini, 1996), (Vidal & Marle, 2008), (Williams, 2002), (Shenhar & Dvir, 2007), (Levin & Ward, 2011).
EXAMPLE: The Apollo program, which had the objective of landing Americans on the Moon and return them safely to Earth.
- Uncertainty: Uncertainty presented itself as an important aspect of complexity, since one cannot forecast the outcome of the interactions between elements, which makes managing such project very challenging. This dimension is related to the level of uncertainty existing, not just in a project but also in the product, or in the development process. Other dimensions can also impact the level of uncertainty, like innovation to the market and technology. (Remington & Polack, 2007), (Williams, 2002), (Danilovic & Browning, 2007), (Kerzner & Belack, 2010), (Shenhar & Dvir, 2007), (Remington et al., 2009), (Vidal &Marle, 2008).
EXAMPLE: Developing a new drug where the side effects are not known.
- External Environment Constraints: This dimension is related to the existing external environment of an organization (on a local, regional, country, or global level) and how it adds to the complexity of a project. Factors such as changes to existing regulations, the fluctuation of the market, and a shift in the political or regulatory environment are included here. (Flyvbjerg, 2005), (Levin & Ward, 2011), (Priemus, Flyvbjerg, & Wee, 2008), (Rolstadås et al., 2011), (Cooke-Davies et al., 2011).
EXAMPLE: A change in taxation can significantly impact the feasibility of a project.
- Political Influence (Politics): This dimension is related to the level of political influence involved in the project. The political influence can be internal or external to the organization. The stronger the political influences to assure approval of that project and to attend the interests of certain stakeholders, the higher the chances for an overestimation of benefits and underestimation of costs. (Flyvbjerg, 2005), (Levin & Ward, 2011), (Priemus et al., 2008), (Rolstadås et al., 2011), (Cooke-Davies et al., 2011).
EXAMPLE: A public construction project may be impacted heavily by political interest, which makes managing them more complex (e.g., the Eurotunnel).
- Product and Project Size: This dimension is related to both the size of the product, service, or result produced by the project, –or to the work that needs to be done to deliver the product, service, or result, as well as to the size of the project itself. This dimension is considered as a critical aspect of project complexity. (Vidal &Marle, 2008), (Kerzner & Belack, 2010), (Levin & Ward, 2011), (Williams, 2002).
EXAMPLE: The simplest project is the delivery a standard component (i.e., a memory chip), followed by creating a subsystem (i.e., a computer motherboard), moving to the delivery of an entire system (i.e., a computer), and finally to producing an array of systems (i.e., a computer network).
- Organizational Capability: This dimension is related to how capable (structurally, technically) an organization is of managing the project and delivering the required product. It is also directly associated with the appropriate (or inappropriate) selection of project personnel. (Remington & Polack, 2007), (Herbemont & César, 1998), (Baccarini, 1996), (Remington et al., 2009).
EXAMPLE: A company that does not have technical people with experience developing applications on the “cloud” would encounter an extra layer of complexity when they engage in such a project.
- Timeframe: Time, which is often being referred on the literature as having a direct effect on how complexity, was identified as a dimension by both project team members and stakeholders. Timeframe is related to the duration of the project, specifically when durations are extended due to the complexity of the project itself. This dimension deals with the timeframe of the project, notably with ones that have long durations. The longer the timeframe, the more chances that changes will impact the project, which increases the level of complexity. (Remington &Polack, 2007), (Herbemont & César, 1998), (Remington et al., 2009), (Hass, 2009).
EXAMPLE: Building the necessary transportation infrastructure for the 2014 Soccer World Cup was impacted by changes in priorities during the length of the project and ended up not being implemented.
- Stakeholder Interaction: This dimension deals with the interaction between stakeholders and how this might impact the project. Stakeholders have different interests in the project and interaction among them could be a challenge. They might have different (and some times conflicting) interests, motivations, and power levels. Stakeholders’ views of project success can also be different, and in many cases powerful stakeholders have no direct participation or awareness of what is happening on the project. (Flyvbjerg, 2005, Remington et al., 2009), (Ivory & Alderman, 2005), (Cooke-Davies et al., 2011).
EXAMPLE: Different interests by key stakeholders add additional complexity to a project.
- Clarity of Goals: This dimension defines how well defined the goals of the project are and the impact this has on how the project will be managed and how decisions will be made. The lack of clear goals results in a diverse set of assumptions by various stakeholders, which might impact the implementation strategy and project performance. (Turner & Cochrane, 1993), (Remington et al., 2009), (Cooke-Davies et al., 2011).
EXAMPLE: A goal of “improving the work environment” is vague, not measurable, and not specific; so that different stakeholders may interpret it very differently. The SMART technique (Specific, Measurable, Attainable, Realistic, Time-bound) could be used to validate whether or not a goal is well-defined.
- Risk: A risk has a probability of happening and a degree of impact should it happen. A proper risk assessment can allow an organization to set up the proper structure to manage a complex project. Also it becomes clear that the more risks—specially the unknown ones—the more complex a project might be, since one does not know what can happen and the repercussion to the other elements of the project. (Shenhar & Dvir, 2007), (Levin & Ward, 2011), (Kerzner and Belack, 2010).
- Degree of Trust: This dimension examines the degree to which existing stakeholders have a trusting relationship with one another (i.e., supplier and project team; senior management and project team). The more trust is cultivated among the stakeholders, the less complex their interactions. (Geraldi, 2008), (Müller & Geraldi, 2007).
EXAMPLE: On a project in which the project manager does not trust the team—and for that reason does not share sensitive but important information—there is an additional level of complexity due to the lack of transparency and clear communication.
- Project Management Maturity Level: This dimension addresses the degree of relative maturity that the organization has achieved in project management. There are several models that measure this, all of which use a five-level approach. The assumption is that more mature organizations will be better able to manage complex projects and deliver the products. (Levin & Ward, 2011), (Kerzner & Belack, 2010).
EXAMPLE: The Kerzner Project Management Maturity Model features the following levels: Level 1—the existence of common language; Level 2—the existence of common processes to manage projects; Level 3—the implementation of a project management methodology; Level 4—the existence of qualitative and quantitative benchmarking efforts; and Level 5—the implementation of continuous improvement efforts.
- Project Description: This dimension focuses on the level of difficulty encountered when describing the project, including all of its elements. Even though this might be influenced by how much knowledge or experience one has with a specific type of project, it is not the same as dimension # 9 (knowledge and experience). It is possible to have knowledge and experience about a project but be challenged when describing the project, its scope, interactions, and components. (Remington et al., 2009), (Bar-Yam, 2004).
EXAMPLE: Describing a project to an audience who have no understanding of the subject matter.
- Pace/Speed to Market: This dimension is related to how fast the product can and should enter the market. The pace will be defined as regular, fast, time-critical, or blitz. The less time there is to manage and deliver a product, the more layers of complexity there are. (Shenhar, 2001), (Shenhar & Dvir, 2007).
EXAMPLE: Responding to a catastrophe (i.e., 2005 Hurricane Katrina, 2010 Gulf of Mexico oil spill) with limited time to plan and execute is much more complex than implementing a frequently executed type of project.
These findings in the literature were next cross-referenced to dimensions of complexity identified in a group of 50 complex projects from the aerospace industry; these projects not only covered aerospace but also included an extensive variety of components such as avionics, defense, engineering, information technology, and construction. They were not limited to North America but included participation from South America and European organizations, which makes the representation less region-centric.
Dimensions Identified in Complex Projects Analysis (listed in alphabetical order):
- Budgetary Constraints: Normally all projects are limited with regard to budget. This dimension is related to how it constrains the ability to manage the project and what level of experience the project manager needs to have with projects of this size (budget-wise).
EXAMPLE: If the organization is limited to managing a specific budget (i.e., up to $100 million), a larger one (i.e., $1 billion) might add a new level of complexity to the project. Similarly, if there were a limitation placed on the project's budget, the performing team might need to implement approaches that had previously not been used. This, too, might add to the complexity level of the project.
- Communication Quality: This dimension is related to the quality of the communication on the project, factoring in both direct and indirect elements. “Direct” communication refers to written or spoken words, signs, images, or any other method that explicitly reveals the true intention. “Indirect” communication refers to communication in which meaning is inferred or implicit and when the true intention is not directly revealed. While direct communication may focus on clarity of message, indirect communication may favor achieving harmony or “saving face.”
EXAMPLE: In direct communication, more value is placed on honesty than on being polite; in indirect communication, the opposite is true. This dimension is greatly impacted by the country and/or organizational culture of the people involved.
- Cultural Differences and Resistance: In today's world, team members and stakeholders might come from various parts of the world with different cultural backgrounds. The possible cultural differences can impact the project and create resistance around tasks that need to be performed. The more cultural clashes exist, the more complexity is added to interactions between stakeholders.
EXAMPLE: The Airbus A380 project done by participants from five different countries.
- Economic Uncertainty: This dimension is related to the existing economic environment for an organization and the challenges that this environment presents to the project. Economic uncertainty can result in a lack of stability with regard to many variables that might affect the project.
EXAMPLE: Times of economic crisis (i.e., the 2008 global financial crisis) have an impact on most organizations and consequently add complexity to existing projects.
- Environmental and Safety Impact: This dimension is associated with the degree to which a project may impact the environment and/or safety of those within the organization and community. Many times, the potential impact of a project on the environment or safety of the population is not considered. Once this level of concern is factored in, it necessarily adds an increasing level of complexity to the project.
EXAMPLE: Building a factory at a specific location might endanger local fauna or increase the levels of pollution, thus adding new variables to the project's feasibility and complexity level.
- Impact on Society: This dimension is related to the impact of the project on society and how it will affect the social interactions, stakeholders, and communities. Some impacts can be easily defined, but others are not even known, which can bring an extra level of complexity to the project. Commonly used is the social impact assessment (SIA), a methodology to review the social effects of infrastructure projects and other development interventions.
EXAMPLE: The Affordable Care Act (Obamacare) in the United States.
- Knowledge and Experience: This dimension is related to how much knowledge and/or experience a key decision maker or project manager has regarding all elements (parts/components) of the product and work that needs to be done on that project. This correlates to previous experience in developing a similar product.
EXAMPLE: The construction of a house would be much more complex for a project manager with experience developing software than for a project manager involved in building houses.
Of all the dimensions only six were found to exist in both the literature and in the group of complex projects—Technology, Uncertainty, Pace/Speed to Market, External Environment Constraints, Product and Project Size, and Stakeholder Interaction—and an additional seven dimensions were only found in the studied group of complex projects.
A survey is also in progress to gather feedback from project manager practitioners worldwide for a final validation of the dimension list provided above. The top dimensions will be used to develop a model to be incorporated on the cost estimation process.
Draft Model that Incorporates Complexity Dimensions
Based on the list of complex projects dimensions, a draft of a model can be prepared. It should take into consideration the list of potential dimensions allowing project managers to select the ones that might impact their project. In the example below (Exhibit 04), a list of five dimensions is used:
Exhibit 04- Draft model
Start by mapping where the project stands on each dimension (red line) and identifying the dimension(s) with higher mark(s). These dimensions should be investigated further, and more information should be obtained to take into consideration possible impacts to the cost estimation. The next step would be for the cost estimation process to incorporate the complexity dimensions as inputs and allow the practitioner to take into consideration these aspects before doing the initial cost estimate. Following the cost estimation process developed by the PMI (2012) , a revised process would llok like the following:
Exhibit 05–Proposed cost estimation process
Complexity has several possible factors or variables (complexity dimensions) that can influence projects and their cost estimates (Shenhar & Dvir, 2007). These dimensions can also influence each other (Baccarini, 1996), (Williams, 1999). This dynamic state calls for a cost estimation model that has the capability to consider several dimensions, which would not be achieved by the static nature of a simple set of guidelines. Such a model would help planners think about the project complexity dimensions, consider what they may be missing, get more information about the parts that are not clear, and modify project plans according to complexity.
It would help project practitioners to determine the qualitative degree of project complexity better and help improve the cost estimation process. Based on a thorough literature review and a study of 50 complex projects, 23 complexity dimensions have been identified within this research. These factors will be verified through a worldwide survey targeting project management experts and will be further refined based on a planned series of in-depth interviews with experts. A draft model has been presented to incorporate complexity dimensions into the cost estimation process of projects.
More information about this research can be obtained directly with the corresponding author via email at [email protected].
Baccarini, D. (1996). The concept of project complexity—A review. International Journal of Project Management, 14, 201–204.
Baker, H. K. & English, P. (2011). Capital budget valuation: Financial analysis for today's investment projects. New York, NY: John Wiley & Sons.
Bar-Yam, Y. (2004). Making things work: Solving complex problems in a complex world. Cambridge, MA: Knowledge Press, NECSI.
Cicmil, S., Crawford, L., Richardson, K. & Cooke-Davies, T. (2007). We're not in Kansas anymore, Toto: Mapping the strange landscape of complexity, and its relationship with project management. Project Management Journal, 38(2).
Cooke-Davies, T., Crawford, L., Patton, J. R., Stevens, C. & Williams, T. M. (2011). Aspects of complexity: Managing projects in a complex world. Newtown Square, PA: Project Management Institute.
Danilovic, M. & Browning, T. R. (2007). Managing complex product development projects with design structure matrices and domain mapping matrices. International Journal of Project Management, 25(3), 300–314.
Edmunds, B. (1999). Syntactic measures of complexity. (Unpublished Doctoral Dissertation), University of Manchester, Manchester, UK.
Flyvbjerg, B. (2005). Design by deception: The politics of megaproject approval. Harvard Design Magazine, 22, 55–59.
Flyvbjerg, B., Bruzeluis, N. & Rothengatter, W.(2010). Megaprojects and risk: An anatomy of ambition. Cambridge, MA: Cambridge University Press.
Geraldi, J. (2008). Patterns of complexity: The thermometer of complexity. Project Perspectives, 29, 4–9.
Hass, K. (2009). Managing complex projects: Anew model. Tysons Corner, VA: Management Concepts Press.
Herbemont, O. & César, B. (1998). Managing sensitive projects: A lateral approach. New York, NY: Routledge.
ICCPM (2012). Complex Project Manager Competency Standards. Complex Project Management Leadership and Excellence. Australian Government Canberra, Australia: Department of Defence. International Centre for Complex Project Management.
Ivory, C. & Aldeman, N. (2007). Partnering in major contracts: paradox and metaphor. International Journal of Project Management, 25, 386–393.
Ivory, C. & Alderman, N. (2005). Can project management learn anything from studies of failure in complex systems? Project Management Journal, 36, 12.
Kerzner, H. & Belack, C. (2010). Managing Complex Projects. New York, NY: John Wiley & Sons.
Klakegg, O. J., Williams, T., Walker, D., Andersen, B. & Magnussen, O. M. (2010). Early warning signs in complex projects. Newtown Square, PA: Project Management Institute.
Levin, G. & Ward, J. L. (2011 ). Program management complexity: A competency model. London, UK: Taylor & Francis Group.
Luhmann, J. T. & Boje, D. M. (2001). What is complexity science? A possible answer from narrative research. emergence: Complexity and Organization, 3, 158–168.
Merrow, E. W. (2011). Industrial megaprojects: Concepts, strategies, and practices for success. New York, NY: John Wiley & Sons.
Müller, R., Geraldi, J. & Turner, R. (2007). Linking complexity and leadership competency of project managers. IRNOP VIII Conference International Research Network for Organizing by Projects. Brighton, UK.
Project Management Institute(2011). Practice standard for project estimating. Newtown Square, PA: Author.
Project Management Institute. (2012). A guide to the project management body of knowledge, (PMBOK Guide®) – Fifth Edition. Newtown Square, PA: Author.,
Project Management Institute.. (2014). Navigating complexity: A practice guide. Newtown Square, PA: Author.
Priemus, H., Flyvbjerg, B. & Wee, B. (2008). Decision-making on mega-projects: Cost-benefit analysis, planning and innovation. Cheltenham, UK: Edward Elgar Publishing.
Remington, K. & Polack, J. (2007). Tools for complex projects. Farnham, UK: Gower Publishing Limited.
Remington, K., Zolin, R. & Turner, R. (2009). A model of project complexity: Distinguishing dimensions of complexity from severity. 9th International Research Network of Project Management. Berlin, Germany: QUT Digital Repository.
Rolstadås, A., Hetland, P. W., Jergeas, G. F., & Westney, R. E. (2011). Risk navigation strategies for major capital projects: Beyond the risk of predictability 1-5. London, UK: Spinger Science and Business Media.
Shenhar, A. (2001). One size does not fit all projects: Exploring classical contingency domains. Management Science, 47, 394–414.
Shenhar, A. & Dvir, D. (2007). Reinventing Project Management: The Diamond Approach to Successful Growth & Innovation, Watertown, MA: Harvard Business School Press.
Shenhar, A., Zhao, Y., Melamed, B. & Holzmann, V. (2012). The Challenge of Innovation in Highly Complex Projects. Rutgers University.
Simon, H. A. (1982). Sciences of the artificial. Cambridge, MA: MIT Press.
The Standish Group International. (2009). CHAOS Summary 2009. Boston, MA:Author.
Turner, J. R. & Cochrane, R. A. (1993). Goals-and-methods matrix: Coping with projects with i-defined goals and/or methods of achieving them. International Journal of Project Management, 11, 93–102.
Vidal, L. A. & Marle, F. (2008). Understanding project complexity: Implications on project management. Kybernetes, 37, 1094–2001.
Williams, T.M. (1999). The need for new paradigms for complex projects. International Journal of Project Management, 17, 269–273.
Williams, T. M. 2001). Modeling complex projects. Chichester, UK: John Wiley & Sons.
1 This paper is based on the interim findings of ongoing PhD research, which focuses on incorporating complexity dimensions into the cost estimation process
© 2014, Leon Herszon
Originally published as a part of the 2014 PMI Global Congress Proceedings – Phoenix, Arizona, USA