Decision Making in Risk Management

Quantifying Intangible Risk Factors in Projects

Christopher O. Cox

• Publisher: CRC Press
• Year: 2021
• ISBN:
  9781000416145
• Edition: 1
• Topic: Technology & Engineering
• Subtopic: Project Management in Engineering
• Index: Technology & Engineering

Project risk management is regarded as a necessary dimension of effective project delivery. Current practices tend to focus on tangible issues such as late delivery of equipment or the implications of technology. This book introduces a framework to identify emergent behavior-centric intangible risks and the conditions that initiate them.

Decision Making in Risk Management: Quantifying Intangible Risk Factors in Projects identifies the quantitative measures to assess behavior-induced risks by presenting a framework that limits the interpersonal tension of addressing behavioral risks. Included in the book is an illustrative case study from the oil and gas sector that demonstrates the use of the framework. The missing dimension of behavior-centric intangible risk factors in current risk identification is explored. The book goes on to cover management processes, providing a systematic analytical approach to mitigate subjectivity when addressing behavioral risks in projects.

This book is useful to those working in the fields of Project Management, Systems Engineering, Risk Management, and Behavioral Science.

1Introduction

DOI: 10.1201/9781003168409-1

1.1 Introduction

Risk is a ubiquitous presence in all facets of human endeavor from investing in the stock market to choosing a profession to the weather when planning an outdoor event or vacation. Simply put, risk is the uncertainty surrounding any activity that can affect its outcome in either a positive or negative way. In Greek mythology, the beginning of the universe is said to have resulted from a game of chance played by the brothers Zeus, Poseidon, and Hades regarding dominion. Who would rule the heavens, the seas, and the underworld (Bernstein 1998) denotes the tangible outcome of the event. However, the implications of behaviors, such as Poseidon's jealousy and plotting against the rule of Zeus, are not easily quantified. In a more mortal dimension, human agents within businesses make investment decisions under uncertainty on a regular basis as they select specific projects to comprise a portfolio that will enable the firm to meet its strategic goals. The business environment in which these organizations operate and execute projects is complex (Elonen and Artto 2003) and turbulent (William and Rūta 2017); adding to this complexity are human behaviors and mental frameworks that are dynamic nonlinear systems (Afraimovich et al. 2011). Therefore, the more effective an organization is at identifying and holistically addressing uncertainty, the more likely it is to achieve its objectives (Hillson 2014).

1.2 Project Risk and Performance

Project risk management has been an area of academic interest since the end of World War II and is a recognized methodology that enhances the probability of a successful project outcome. Current project risk assessment methods are oriented toward systems that are linear and vary from basic qualitative assessment to complex statistical analysis primarily focused on tangible project factors (e.g., cost, schedule, scope, quality, etc.). Risk profiles can vary as a project moves through the development cycle. However, projects are developed and executed by diverse groups of individuals with varying perspectives, beliefs, and desires. Inherent in this diversity are the implications of emergent human behavior on project objectives due to their high variability and nonlinearity creating “blind spots” for individuals and teams.

When it comes to investment, the energy sector is the most capital intensive and one of the most complex industries in the world (Davis 2020; Schroeder and Jackson 2007)—with an expenditure of $714 billion in 2017 (Varro 2018). The environment in which this investment takes place is one where stability and certainty are rare while intricacy and ambiguity dominate the landscape (Kraaijenbrink 2019). Projects can take many years to move from conceptual planning to initial operation, subjecting them to a myriad of risks. The Project Management Institute (PMI) has utilized the term “progressive elaboration” to describe the increasing level of detail required as a project progresses through its development stages (PMI 2017); changes in the internal and/or external project environment during this evolution can create unforeseen circumstances that can impact project objectives. According to industry surveys done by management consulting firms Deloitte (Deloitte Center for Energy Solutions 2015) and Ernst & Young (EY 2014), fewer than half of projects in the energy sector meet their objectives, and more than two-thirds of executives are not confident that their organizations are experiencing optimal financial returns. An effective risk management system identifies and addresses uncertainties with the potential to impact project objectives (Hillson et al. 2006). This mediocre level of project performance in the energy sector creates the impetus for further investigation into current risk management methodology.

Project performance is measured in terms of meeting objectives and is influenced by both hard (i.e., tangible) and soft (i.e., intangible) factors. Rolstadås et al. proposed a five-aspect qualitative framework for assessing project performance that highlights the importance of risk management using both hard (structure and technology) and soft (culture, interactions, social relations, and networks) aspects (Rolstadås et al. 2014). PMI has defined risk as “an uncertain event or condition that, if it occurs, has a positive or negative effect on a project's objectives” (PMI 2017). Although risk can be either a threat or an opportunity, it generally carries a negative connotation (Chapman and Ward 2003). Current project risk measures tend to focus on the tangible factors that have a direct impact on project success in terms of cost, schedule, scope, and quality (PMI 2017). However, organizations and their project teams can also face challenges from other sources, such as stakeholder politics, misalignment of organizational cultures, and conflicting human behavioral responses (Rasmussen 1997) creating resistance to initiatives that threaten vested interests or accepted norms (Ancona et al. 1999). The ability to assign a risk exposure from intergroup politics, cultural differences, or the interpersonal conflicts stemming from them is much more difficult to quantify and “require[s] a greater degree of subjectivity and intuition” (Basu 2017).

Projects by their nature are dynamic, complex, sociotechnical systems (i.e., interactions between humans and technology) consisting of many highly interconnected components (Baccarini 1996; Carley et al. 2007). A complex system is defined by the Society for Risk Analysis (SRA) as: “A system is complex if it is not possible to establish an accurate prediction model of the system based on knowing the specific functions and states of its individual components” (Aven et al. 2018). Project complexity increases significantly as the number of elements, interactions, and interdependencies expands (Elonen and Artto 2003). Accurately addressing issues that arise can be arduous because of the cause and its resulting effect not occurring in time and space proximity (Repenning and Sterman 2001). Compounding this are the nonlinearities inherent in human behavior, where a wrong attribution of cause can give rise to what can be described as “wicked messes” (Roth and Senge 1996). These “wicked messes” arise from human judgment being subject to systematic errors, where the need to carefully analyze information is traded against the pressure to make a timely decision (Skitmore et al. 1989). Flyvbjerg et al. found that many project failures are caused in part by human behavior (Flyvbjerg et al. 2009). Behaviors are difficult to measure, creating problems for project teams and other stakeholders (Dargin 2013).

1.3 Intangible Risk Factors

The United Kingdom (UK) Oil and Gas Authority (OGA) issued a report in March of 2017 entitled “Lessons Learned from UKCS Oil and Gas Projects 2011-2016.” OGA found that “since 2011 fewer than 25% of oil and gas projects have delivered on time, with projects averaging 10 months delay and coming in 35% over budget.” The UKCS (operations in the North Sea) is one of the most mature offshore oil and gas regions in the world; ca. 100 fields have been developed, beginning with the West Sole field starting production in 1967. The report went on to say that “it was concluded that it is not necessarily ‘what’ was being built that greatly influenced the cost and schedule outcome of a project, but more ‘how’ the project was executed. Many of the reasons for deviation are non-technical in nature” (OGA 2017). These nontechnical issues will be referred to in this book as “behavior-centric intangible risks.”

The Cambridge English Dictionary has defined intangible as “influencing [one] but not able to be seen or physically felt” (Cambridge Dictionary Online). In the literature, the topic of intangibles has predominately focused on intellectual capital (O'Donnell et al. 2003), intangible asset valuation (Nichita 2019; Saunders and Brynjolfsson 2016), and emergent technology or regulation (Foxon et al. 2005). Demmel and Askin proposed the identification and inclusion of value-adding intangibles (e.g., increased flexibility, reduced lead times, etc.) when making investment in technology for manufacturing processes (Demmel and Askin 1992). In his book Intangibles: Management, Measurement, and Reporting, Baruch Lev defined intangible assets as “non-physical sources of value (claims to future benefits) generated by innovation (discovery), unique to organizational designs or human resource practices” (Lev 2001). However, literature regarding intangible risks in projects is scant, and what exists tends to focus on discrete topics. Hofman et al. defined intangible risk as “emerging or negative phenomena,” which includes issues such as interpersonal conflict and lack of appropriate resources (Hofman et al. 2017). Others have highlighted the quality of management (Jonas et al. 2013), unclear roles and responsibilities (Sanchez et al. 2009), preoccupation with personal interests (Beringer et al. 2013), unclear or conflicting priorities (Blichfeldt and Eskerod 2008), lack of end-user involvement, and unclear objectives (Morris 2008). Thamhaim and Wilemon addressed conflict caused by interpretation of procedures on the basis of cost and/or schedule estimates (Thamhaim and Wilemon 1975). This list of intangible factors can be divided into two interrelated groups, behavior-centric factors (interpersonal conflict) and causal factors (unclear or conflicting priorities); however, the literature lacks a clear distinction between the two.

In aviation accident investigation and processing-plant operational safety (e.g., chemical, refining, etc.), human actions are considered the highest contributor to failure because of the complex interaction of humans and technology. Because of this inherent complexity, these sociotechnical interactions are viewed as nonlinear systems or “causal webs” (O'Hare 2000; Rasmussen 1997). Rasmussen recognized the intangible implications of behavior in the context of safety risk in processing-plant environments. His conceptual framework—the dynamic model of safety and system performance—is a ternary model of constraints (economic, workload, and performance), with behavioral reactions within these boundaries exhibiting random responses or “Brownian movements.” These movements are the result of interactions among management's expectations of efficiency, the implications on agent workload, and agent response to this potential increase in effort. Rasmussen referred to these interactions as “gradients.” However, he recognized, “the problem is that all work situations leave many degrees of freedom to the actors for choice of means and time for action even when the objectives of work are fulfilled and a task instruction or standard operating procedure in terms of a sequence of acts cannot be used as a reference of judging behavior.” Rasmussen went on to say that “we need a framework for identification of the objectives, value structures, and subjective preferences governing the behavior within the degrees of freedom faced by the individual decision maker and actor” (Rasmussen 1997). These interactions or gradients can be conceptualized as a network of interacting networks, a metanetwork, within an organization.

Currently, there is no any quantitative framework available to identify and analyze intangible factors or “subjective preferences governing behaviors” in projects. To address this gap, metanetwork analysis (MNA) is proposed. MNA is an extension of traditional social network analysis (SNA). SNA has been applied to construction projects to identify opportunities to enhance project effectiveness within the broader organizational context by enhancing knowledge transfer and collaboration in project teams (Chinowsky et al. 2008). However, SNA is limited to the assessment of network interactions among individuals (agents) or social groups. MNA removes this constraint and extends the analytical capability to multiple networks, providing a means for a comprehensive assessment of project network elements and interactions (Carley 2002). The metanetwork technique provides the framework to address the analytical gap in Rasmussen's conceptual model by leveraging mathematically robust social network measures.

1.4 Goal and Objectives for Book

Although focused on project delivery, the goal of this book is to provide technical practitioners (engineering, project management, operations, etc.) and managers with a rigorous and user-friendly framework to identify behavior-centric intangible risks and the conditions that initiate them throughout the business cycle. The objectives of this book are as follows:

1. Provide an analytical framework that can be used in project r...