A brief history of project management

Mankind has achieved magnificent project outcomes for thousands of years. Marvels such as the Pyramids of Giza, the Great Wall of China, the Parthenon, and Stonehenge were constructed without modern-day techniques and software tools, although often with abundant yet expendable human resources. The twentieth century, though, experienced a new age of industrialisation and a drive towards repeatability of manufacturing outputs, mass-production and the pursuit of greater order and efficiency. Considered a milestone in the development of project management, Henry Gantt (1861–1919) developed the Gantt chart, which was quickly co-opted by managers to help control the project process.

The 1950s marked the emergence of the ‘Program Evaluation and Review Technique’ (PERT), deployed and exercised in the Polaris missile submarine programme. PERT displays how much time (involving the most likely, optimistic, and pessimistic estimates) is allocated to a component of a project, such as a project task. This enables projects to be analysed statistically. It lays down interdependencies between these components that allow the definition of a critical path; any deviation or change from that path will have an automatic influence on the end date of the project.

Such techniques are now commonly applied in planning modern projects and often represent the core technique of management by planning. However, it was not until the 1960s that the development of these techniques led to the recognition of project management as a discipline. In 1969 the Project Management Institute (PMI) was founded. This not-for-profit project management organisation is one of the most recognised member associations in the world. It advocates providing project managers with a universal set of tools and techniques to manage projects successfully.

As a consequence, the Project Management Body of Knowledge (PMBOK) Guide was published in 1987. Over the years, further internationally recognised frameworks and sourcebooks in project management have been developed, such as PRINCE2 and the Association for Project Management’s (APM) Body of Knowledge. They form part of a wider narrative advocating a set of normative procedures that, if applied correctly, are claimed to lead to success.

Project success and failure

Project success is a tricky concept. Projects are often assessed on the classic ‘iron triangle’ objectives of time, cost, and quality. Was it delivered on time, on budget, and did it meet the original performance specifications? If we rely on these three measures, then it may not be surprising if failure can be perceived as commonplace, but there are problems with this for several reasons. Criteria drawn up in the early stages may turn out not to be correct. Knowledge-generation is inherent in many projects, and initial expectations of accurate schedules and financial plans may be unrealistic. We need to be sensible about what we expect to find out as part of the work and adjust expectations accordingly. We do not argue with the importance of time, cost, and quality, but they do represent a rather limited, short-term perspective of what constitutes success – and failure – in projects. Longer-term user or customer satisfaction after a project has been implemented, or the development of new organisational capabilities, are not necessarily captured by these measures. Projects that meet specifications, are on time and budget may be considered a success, but if their outputs remain mostly unused because the end-users were not adequately consulted, then the investment was a poor one. Spending a little more time and money mid-way through to deliver what is necessary makes more sense, but if the organisational control and reward systems penalise this, they will drive the ‘wrong’ behaviours. Success and failure are thus far from clear-cut, and ‘simple’ evaluations are often misguided.

In the light of this wider perspective on the nature of success, many projects that could be regarded as failures might end up being resounding successes (and, indeed, vice versa). Many projects, though, are still scrutinised on their efficiency targets, often with limited regard to whether the outcome produced is as useful as was intended. This myopic view of projects does not come as a surprise given that, for example, long-term satisfaction scores include soft factors that are difficult to establish and measure.

We also have to realise that success and failure is a matter of perception. Perceptions matter and they vary. Different stakeholders can have quite different views of how well the work went, and a single consensus view can be rare. A user who receives exactly the system he or she needs may not be overly worried about the budget over-run, but this may be the primary concern of the finance manager. So, whose success are we measuring and when?

Uncertainty

The concept of uncertainty is not an unidentifiable ‘thing’. For example, when throwing an unloaded dice, it is possible to calculate exactly the probability of achieving certain results. This is what is known as aleatoric uncertainty, or true variability (from alea – the Latin word for dice): uncertainty reflects the unpredictability inherent to a stochastic process.

However, in many situations, we lack sufficient information to make a probabilistic assessment of something happening. If our dice were to constantly change its shape, it would be close to impossible to calculate the likelihood of future adversity. This particular type of uncertainty is known as epistemic uncertainty (episteme – from the Greek word for knowledge). In this case, our lack of knowledge about relevant variables leads to uncertainty, promoting an evaluative mode of thought of mindfully gauging the quality of what we think we know and do not know.

In more detail, Table 1.1 summarises some key characteristics of epistemic and aleatory uncertainty:

We may be uncertain about key aspects of the project. First, with goal uncertainty there may be a lack of clarity with regards to the goal to be achieved and the best way to get there. Imagine you start a project of a type you have not engaged in before. Neither you nor your stakeholders can fully define the goal of the project with confidence. This is not uncommon – many projects involve only vaguely definable outcomes that even key owners or sponsors struggle to specify. In R&D projects, for example, managers are faced with the challenge of specifying requirements, functions and outcomes, yet the reality is that knowledge will emerge as the work progresses.

The second dimension, approach uncertainty, relates to how the already unclear goal will be achieved. Not only is the precise goal uncertain, but so too is the path towards it. Imagine you know what the goal is that needs to be achieved, but you do not know how to get there. Although ‘ways of working’ in projects are often advocated by professional organisations such as the PMI as being universal, many projects have unique (and constraining) elements, such as the new technology involved or the relationships between the key participants. One size does not always fit all. The ‘how’ in the project can be a winding and uncertain path and, as such, project managers may have to alter their initially-planned approach.

Dynamic uncertainty encapsulates the condition of constant change. It is less a question of ‘where to’ (Goal uncertainty), or ‘how’ (Approach uncertainty), but one of ‘what’: what to do given continuous change in the environment.

Finally, a category of uncertainty that is often largely ignored by major project management frameworks is relational uncertainty. Uncertainty is ultimately in the eye of the beholder. Despite the plethora of project data, people need to make sense of what they see. The same data can lead to multiple interpretations, and hence, a variety of actions, which may be unhelpful. The sum of the responses to differing perceptions may add to the level of ambiguity and confusion.

Complexity

The previous assessment unpacks the extent to which you perceive your project to be uncertain. Be aware that not only risk (aleatoric uncertainty) and epistemic uncertainty may derail your project. There is also a second element of adversity: complexity.

In a tightly coupled project, characterised by ‘time-dependent processes’, ‘little slack’ and ‘invariant sequences of operations’ (Perrow 1999), incidents can occur from initial (potentially small) failures building on themselves and rapidly becoming larger, triggering a sudden crisis (see Figure 1.1).

In a loosely coupled project, on the other hand, risk and uncertainty have limited direct implications for the other components (e.g. tasks, resources) in a project. The project does not destabilise overnight, and this allows a longer incubation phase – a creeping crisis – in which one has the opportunity to carry out some forms of intervention.

Most projects do not face such a sudden collapse; they generally face a creeping erosion of performance, a ‘death by a thousand cuts’, a crisis that gradually builds. Projects tend to have a prolonged incubation phase, where adversity gradually builds to a crisis. However, in many cases, we as project managers mindlessly ignore those warning signals of an impending crisis. We tend to ‘wake up’ at a stage when a crisis or disaster can not be averted anymore.

The evolution of risk management

The evolution of risk management – like many other planning mechanisms in project management – is characterised by the desire for certainty, quantification and the ability to prepare in advance for future events. In the distant past, people were guided by fate and faith in God’s will. Indeed, the future was perceived to be at the mercy of the gods. These long-held fundamental beliefs started to be challenged during the Renaissance (fourteenth to seventeenth century), a period of turmoil, in which the shackles of superstition were challenged, and inquisitive people such as Pascal and Fermat embraced the concept of forecasting and of building the foundations for the theory of probability.

Probability theory evolved quickly into a method of organising, leading to, for example, the mathematical basis for the insurance industry. Until the early twentieth century, human imagination was driven by repeatability and statistical analysis of the past to inform the future, and many of our modern approaches are based on probabilistic forecasting and decision-making driven by a concept called Expected Utility Theory (EUT). EUT states that decisions about risks are made by comparing their expected utility values, for example, the weighted sum of probability multiplied by impact, so that the utility of decision-making choices is weighted according to their probabilities and outcomes. Consider the following simplified example shown in Figure 1.2.

In the diagram, the probability of avoiding risks in a project through the execution of a risk response action is P, and without risk actions, Q, with P larger than Q and 1 – Q larger than 1 – P. The utility of avoiding risks (relative to the cost of materialised risk) is A, and the utility of no actions (relative to the cost of those actions) is G, while A is assumed to be greater than G. The decision by the project manager to take actions or not depends on the utility of avoiding the materialisation of uncertainty (benefit) while committing resources (cost), and on the relative magnitude of the objective or subjective probabilities.

EUT is a basic model of rational choice that underpins most methodologies for taking risky decisions and is generally regarded as a very useful and effective framework for decision-making under conditions of aleatoric uncertainty. However, the often ‘blind’ adherence to the principles of EUT and the resulting illusion of certainty was shattered by two bloody World Wars and, in more recent times, by major disasters such as the collapse of Lehman Brothers, the world recession, and the rise of world terrorism. Emerging criticism up to the present day has challenged the commonly adopted and advocated view that the past, if repeated often enough, can confidently inform the future:

What do we mean by resilience?

In its broadest sense, resilience can be seen as the capacity of a system to absorb disturbance and reorganise while undergoing change, so as to retain essentially the same function, structure, identity and feedbacks. This requires an understanding of what constitutes a system. A system is a set of elements that interact with each other and create emerging properties as a whole. Systems have functionality in that they exhibit behaviour. They also contain subsystems, which are groups of elements within the system that may, themselves, have similar properties. All systems interact with their environment as they exist within a given context, and have delineated boundaries. Systems interact with their environment to source inputs and produce outputs. Finally, they are purposeful in that they are identified by stakeholders to be of interest. So, when we talk of resilience, what we are talking about is the resilience of an entity that is, itself a system.

Resilience can be classified into several broad categories:

• Engineering (materials resilience);
• Ecology (resilience of the natural environment);
• Psychology (resilience of individuals);
• Sociology (organisational resilience).

It is the last of these that is of interest to us. This is not to say the others are not important (and may, indeed, impinge upon organisational resilience) but the focus of this book is on organisational resilience and, in particular, a specific form of organisation – the project as a mindful system, a ‘living’ organism.

The root concept of resilience can be seen in early ecological studies and is epitomised by the Panarchy model (Gunderson and Holling 2001). This model is often seen as a figure-of-eight diagram, always looping around in the process of continual change and renewal. It has four distinct but inter-related phases or stages.

1. Growth phase. This is where new opportunities and available resources are exploited and normally comes with weak interconnections and weak regulations. Pioneers and opportunists are frequently successful in this phase. Systems may experience numerous growth phases, and each one may or may not resemble previous growth phases.
2. Conservation phase. In this second phase, energy and capabilities are stored, and material accumulates. This stage is characterised by developing, increasing and stronger connections and regulations. More conservative but also more efficient specialists take over, and the system becomes increasingly stable and rigid. However, with this stability comes a commensurate loss of flexibility. There is an increasing dependence on existing structures and processes in this phase and, as a result, the system is increasingly vulnerable to disturbance.
3. Release phase. In this third phase, a shock to the system means it ‘comes undone’. Resources are released, and all kinds of capital (social, natural, economic) leak away. Connections break and regulation weakens. This phase is characterised by chaotic dynamics, uncertainty and destruction.
4. Reorganisation phase. Finally, the destruction shows creative potential and options emerge. Previously suppressed pioneers or invaders show up, and the future is ‘up for grabs’. In a perpetuating system, there is a process of restoring. However, this restoration is characterised by novelty, invention, and experimentation. Released capital can regroup around new opportunities. This phase may (or may not) end with a new identity.

A crucial component of this model is that after the shock of the release phase, there may be no reorganisation phase if that shock is so severe that it destroys the system. The metaphor often used (and where the Panarchy model was developed) is one of a forest. It grows from saplings and small plants in the growth phase. The pioneers are the many different species vying with one another. Ultimately, some dominant species gain dominance and the forest will enter a conservation phase, where it grows and becomes stable. Imagine there is a sudden forest fire (a shock to the system) that destroys many of the trees and other plants of the forest. It is now well-understood in ecology that forest fires are crucial to the continued growth, new development, and subtle change of forests. After a forest fire, the forest will go into a reorganisation phase and, eventually, enter the growth phase and the cycle begins once again, in perpetuity. However, imagine the shock to the system is that bulldozers destroy the forest, strip away all the trees and the land is turned into monoculture farmland or grazing land. The forest is no more, and there is no reorganisation phase leading to a new growth phase.

This model can apply to organisations (or people, or materials) as much as it can to ecological systems. More precisely, organisational resilience can be defined as ‘the maintenance of positive adjustment under challenging conditions such that the organisation emerges from those conditions strengthened and more resourceful’ (Vogus and Sutcliffe 2007, 3418). In this sense, organisational resilience is not about responding to a one-time crisis or about rebounding from a setback (the oft-stated ‘bouncebackability’) but is rather about continuously anticipating and adjusting to deep, secular trends that can permanently impair the earning power of a core business and about having the capacity to change before the case for change becomes desperately obvious.

Organisational resilience, as a concept, raises two issues for organisations. First, it is difficult for organisations to get good at something (resilience) at which you do not have much practice. Shocks to organisational systems might be frequent or infrequent, but organisational-threatening shocks do not come very often, and so organisations (for many of the reasons we address in this book) do not practise at getting good at being resilient. Second, there is the question of whether people in organisations are more concerned with mere survival in a world of uncertainty or whether they seek to thrive.

There are also three major issues that scholars, commentators and practitioners face when they think about resilience, none of which is easily resolved. Is resilience context-specific? Some organisations seem to be resilient in some circumstances but not others, so how can this be explained? There is also no obvious way to measure resilience as for many people, it is a vague, fuzzy thing that’s hard to understand. This means that there is no obvious way of generalising to organisations everywhere. Finally, how is resilience to be judged? You cannot just wait for a disaster to happen to find out whether organisations are resilient, and looking at organisational inputs is not always the answer as some organisations that do not plan actually perform very well during a crisis. For example, some smaller organisations tend to do very little planning and, despite this, can do very well in times of crisis.

Despite these problems, issues and challenges with the concept of organisational resilience for scholars and practitioners alike, there is no doubt that resilience as a concept has captured the imagination of business leaders worldwide, looking for an answer to the levels of uncertainty and complexity we all face.

A section on lures

The human brain is an amazing and incredibly powerful machine of synapses and neurons. But the brain is also fallible. It is not a super-computer but has evolved as a social machine. The information it receives is partial and localised (what we sometimes call culture). As a result, our behaviour is subject to cognitive biases, those annoying glitches in our thinking that cause us to make questionable decisions and reach erroneous conclusions. We are intuitive, emotional, and partial beings – we are human. It is important to distinguish between cognitive biases and logical fallacies. A logical fallacy is an error in logical argumentation (e.g. ad hominem attacks, slippery slopes, circular arguments, appeal to force, etc.). A cognitive bias, on the other hand, is a genuine deficiency or limitation in our thinking – a flaw in judgement that arises from errors of memory, social attribution, and miscalculations (such as statistical errors or a false sense of probability).

As a result, we frequently behave mindlessly in the way we deal with uncertainty, in the way we perceive it, in the way we understand it and in the way we respond to it. We will start each chapter with some of these behavioural ‘shortcomings’. People can walk, to some extent, ‘brainlessly’ through projects, driven by the original plan and what they are told to do. All we are doing in this section of each chapter is to point out some of the behaviours, learnt and emotional, that make the management of adversity difficult. What we ask is that project managers understand that these fallibilities exist; we hope you see our suggestions as ways of helping to overcome fallible human cognition.

A section on enablers

To deal with our fallibilities and our propensity to follow routines and procedures in a mindless fashion, we would like to suggest examples of ‘good practice’ – what a project manager could do (with an emphasis on ‘could’). We want to emphasise that anything we suggest that could be done to manage epistemic uncertainty is context-specific. What works in one context may not work in another. These are not hard-and-fast rules.

A section on leadership

Having suggested the ‘what’ (although intentionally non-prescriptively), we follow with the ‘how’ of implementation. If one leadership style has seemingly conquered the project management world, it is that of transactional leadership. Transactional leadership relies very much on compliance with process and procedures in which, as much as possible, situated human cognition is eliminated as a source of error through the imposition of rule-based behaviour. This approach does work if the project risks are predictable, measurable, and controllable, and people working on the project behave in reliable, rational ways. Unfortunately, management is rarely this straightforward. Mindful project managers, however, do not lead by replacing ‘thinking’; they facilitate flexibility in mindsets and empower people to learn and apply situational and professional judgement. They foster information flow and provide a culture of support and encouragement.

A section on relationships

Projects are social entities, often with a multitude of internal and external stakeholders. Whereas stakeholders add complexity, they can also be used as resources to manage epistemic uncertainty more effectively. Managing epistemic uncertainty can be a journey of painful ignorance (Kutsch and Hall 2010), sometimes on an enormous scale. Embracing the ‘unknown’ mindfully means managing and educating stakeholders. Reluctance to give in to temptation and consider the future as certain is only the beginning of stakeholder management. Not least, it is an emotional rollercoaster that requires some dedicated preparation and intervention.

All the chapters and sections in this book are complemented by two types of vignettes – indicated by text boxes – of best practices and evocative syntheses of key social and cognitive biases that challenge our ability to be collectively mindful:

Textboxes on social and cognitive biases

There is an incredible amount of written work on social and cognitive biases that constrain our ability to be mindful to uncertainty. By default, we are biased to adopt a certain direction of decision making, often to the detriment of a collective mind. The book section on ‘Lures’ will provide you with a few mini-literature reviews on memory or cognitive biases; we review the current state of knowledge on some of the key biases that inhibit our ability to mindfully lead in a project environment.

Textboxes on mindful practices

We try to learn from the best and try to understand what they do and why. While the discussions in this book draw on a wide range of research conducted both by the authors and others, we have selected three organisations which we believe have a track record of successful project delivery (with only the occasional hiccup) and, thus, epitomise some of the points we seek to make. They all carry out project work in a world of uncertainty and complexity, yet they prevail. They create a state of organisational and project awareness beyond the past and ready and prepare themselves for a future characterised by projections and speculations. When a crisis cannot be averted, response enactment is swift and pragmatic. Divergence from the expected plan does not result in simply tightening rules and procedures, but includes questioning project resilience in the widest sense possible.

With this book, we hope to help you on a journey towards a state of mindful project management, with the purpose to increase and maintain project resilience, by elaborating the ‘what’, the ‘how’ and the ‘why’. We offer a set of principles and a platform to reflect on your own context and your own projects, with self-assessment questionnaires at the end of each chapter. It is ultimately YOU, who decides what is best applied to managing adversity in the form of uncertainty and complexity, both efficiently and effectively.