Christos Ellinas

Toward Project Complexity Evaluation: A Structural Perspective

Complexity is often quoted as an independent variable that challenges the utility of traditional project management tools and techniques. A large body of work has been devoted in exposing its numerous aspects, yet means for quantitatively assessing it have been scarce. Part of the challenge lies in the absence of hard evidence supporting the hypothesis that projects can be considered as complex systems, where techniques for measuring such complexity are better established. In response, this study uses empirical activity networks to account for the technological aspect of five projects. By doing so, the contribution of this study is twofold. First, a procedure for the quantitative assessment of an aspect of project complexity is presented; namely, structural complexity. Second, results of the analysis are used to highlight qualitatively similar behavior with a well-known complex system, the Internet. As such, it suggests a transition from the current, metaphorical view of projects being complex systems to a literal one.From a practical point of view, this study uses readily captured and widely used data, enabling practitioners to evaluate the structural complexity of their projects to explore system pathologies and, hence, improve the decision-making process around project bidding, resource allocation, and risk management.

How Robust Is Your Project? From Local Failures to Global Catastrophes: A Complex Networks Approach to Project Systemic Risk

Current societal requirements necessitate the effective delivery of complex projects that can do more while using less. Yet, recent large-scale project failures suggest that our ability to successfully deliver them is still at its infancy. Such failures can be seen to arise through various failure mechanisms; this work focuses on one such mechanism. Specifically, it examines the likelihood of a project sustaining a large-scale catastrophe, as triggered by single task failure and delivered via a cascading process. To do so, an analytical model was developed and tested on an empirical dataset by the means of numerical simulation. This paper makes three main contributions. First, it provides a methodology to identify the tasks most capable of impacting a project. In doing so, it is noted that a significant number of tasks induce no cascades, while a handful are capable of triggering surprisingly large ones. Secondly, it illustrates that crude task characteristics cannot aid in identifying them, highlighting the complexity of the underlying process and the utility of this approach. Thirdly, it draws parallels with systems encountered within the natural sciences by noting the emergence of self-organised criticality, commonly found within natural systems. These findings strengthen the need to account for structural intricacies of a project’s underlying task precedence structure as they can provide the conditions upon which large-scale catastrophes materialise.

Exploring Structural Patterns Across Evolved and Designed Systems: A Network Perspective

Our desire to deliver increased functionality while setting tighter operational and regulative boundaries has fueled a recent influx of highly coupled systems. Nonetheless, our current capacity to successfully deliver them is still in its infancy. Understanding how such Designed systems are structured, along with how they compare to their naturally Evolved counterparts, can play an important role in bettering our capacity to do so. Based on this premise, the structural patterns underlying a wide range of seemingly unrelated systems are uncovered using tools from network science. By doing so, structural patterns emerge and are subsequently used to highlight both similarities and differences between the two classes of systems. With a focus on the Designed class, and assuming that increased structural variety fuels design uncertainty, it is shown that their adherence to statistical normality i.e., expected vs. encountered patterns and statistical correlations between combinations of such patterns is rather limited. Insight of this sort has both theoretical context agnostic approaches are increasingly relevant within the domain of Systems Engineering, yet are rarely used and practical transferability of knowledge implications.

Modelling indirect interactions during failure spreading in a project activity network

Spreading broadly refers to the notion of an entity propagating throughout a networked system via its interacting components. Evidence of its ubiquity and severity can be seen in a range of phenomena, from disease epidemics to financial systemic risk. In order to understand the dynamics of these critical phenomena, computational models map the probability of propagation as a function of direct exposure, typically in the form of pairwise interactions between components. By doing so, the important role of indirect interactions remains unexplored. In response, we develop a simple model that accounts for the effect of both direct and subsequent exposure, which we deploy in the novel context of failure propagation within a real-world engineering project. We show that subsequent exposure has a significant effect in key aspects, including the: (a) final spreading event size, (b) propagation rate, and (c) spreading event structure. In addition, we demonstrate the existence of ‘hidden influentials’ in large-scale spreading events, and evaluate the role of direct and subsequent exposure in their emergence. Given the evidence of the importance of subsequent exposure, our findings offer new insight on particular aspects that need to be included when modelling network dynamics in general, and spreading processes specifically.

Dynamics of Organizational Culture: Individual Beliefs vs. Social Conformity

The complex nature of organizational culture challenges our ability to infer its underlying dynamics from observational studies. Recent computational studies have adopted a distinctly different view, where plausible mechanisms are proposed to describe a wide range of social phenomena, including the onset and evolution of organizational culture. In this spirit, this work introduces an empirically-grounded, agent-based model which relaxes a set of assumptions that describes past work–(a) omittance of an individual’s strive for achieving cognitive coherence; (b) limited integration of important contextual factors—by utilizing networks of beliefs and incorporating social rank into the dynamics. As a result, we illustrate that: (i) an organization may appear to be increasingly coherent in terms of its organizational culture, yet be composed of individuals with reduced levels of coherence; (ii) the components of social conformity—peer-pressure and social rank—are influential at different aggregation levels.

Exploring Structural Patterns Across Evolved and Designed Systems: A Network Perspective: EXPLORING STRUCTURAL PATTERNS ACROSS EVOLVED

Our desire to deliver increased functionality while setting tighter operational and regulative boundaries has fueled a recent influx of highly coupled systems. Nonetheless, our current capacity to successfully deliver them is still in its infancy. Understanding how such Designed systems are structured, along with how they compare to their naturally Evolved counterparts, can play an important role in bettering our capacity to do so. Based on this premise, the structural patterns underlying a wide range of seemingly unrelated systems are uncovered using tools from network science. By doing so, structural patterns emerge and are subsequently used to highlight both similarities and differences between the two classes of systems. With a focus on the Designed class, and assuming that increased structural variety fuels design uncertainty, it is shown that their adherence to statistical normality i.e., expected vs. encountered patterns and statistical correlations between combinations of such patterns is rather limited. Insight of this sort has both theoretical context agnostic approaches are increasingly relevant within the domain of Systems Engineering, yet are rarely used and practical transferability of knowledge implications.

Exploring Structural Patterns Across Evolved and Designed Systems

Our desire to deliver increased functionality while setting tighter operational and regulative boundaries has fueled a recent influx of highly coupled systems. Nonetheless, our current capacity to successfully deliver them is still in its infancy. Understanding how such Designed systems are structured, along with how they compare to their naturally Evolved counterparts, can play an important role in bettering our capacity to do so. Based on this premise, the structural patterns underlying a wide range of seemingly unrelated systems are uncovered using tools from network science. By doing so, structural patterns emerge and are subsequently used to highlight both similarities and differences between the two classes of systems. With a focus on the Designed class, and assuming that increased structural variety fuels design uncertainty, it is shown that their adherence to statistical normality i.e., expected vs. encountered patterns and statistical correlations between combinations of such patterns is rather limited. Insight of this sort has both theoretical context agnostic approaches are increasingly relevant within the domain of Systems Engineering, yet are rarely used and practical transferability of knowledge implications.