WBS are popular for obvious reasons in terms of providing greater insight and detail, and from there the potential for more accurate costing of a project, as well as resource planning and potential constraints (Miller and Hobbs, 2009). The opportunity to outsource and potentially shorten or ‘crash’ a project by completing sub-tasks more quickly offers practical insight into where lost time can be regained, or if there is the potential for reorganising project tasks due to unforeseen circumstances such as a failure to deliver a critical piece of equipment, or overruns in other tasks. This level of detail is therefore helpful on a practical level, but in much the same way as a Gantt chart, a WBS is potentially exposed to a very high degree of subjectivity and is heavily reliant on previous experience and knowledge of similar tasks and also the capacity for outsourcing (Kerzner, 2013). A further layer of complexity associated with outsourcing within WBS was highlighted by Rodrigues and Williams (1997) in a study of software projects, in that if project tasks are outsourced and then reintegrated, it is absolutely critical that there is a very high degree of communication. Similarly, a study by Sharon et al., (2011), highlighted an alarming number of instances where project tasks have been decoupled on the basis of data contained within a WBS, only to find that it was impossible to reintegrate certain elements.
Another problem identified with the utilisation of WBS is highlighted by Atkinson (1999), who describes the systemic problem of ‘guesstimation’ in their use. This occurs in projects where there are novel aspects and those involved have little to no experience of the specific task and the possibility for further breaking down certain activities. Pons (2008) believes that WBS take no account of the time resource necessary for reintegration if such tasks have been distributed and this in his opinion contribute to overall project overruns and excess. Moreover, further meta-analytic study regarding the utilisation of WBS by van der Hoorn and Whitty (2015a) found multiple inaccuracies in their use. These typically related to ‘hanging’ tasks, or timelines which did not add up properly, leaving days and tasks unaccounted for. This is more alarming on the basis that there is a great deal of sophisticated software available to assist project management teams in creation of both Gantt charts and WBS, so such hanging tasks and inaccurate calculation of days should, theoretically, be impossible. Vanhoucke and Vandevoorde (2007) recognise that such issues and inaccuracies can only be attributed to human error, and the old adage of ‘rubbish in, rubbish out’ regarding the quality of data submitted to a software program and the subsequent output. There appears to be no research into the specifics of whether or not individuals responsible for developing WBS project deliberately gamed them or introduce slack in the same way as Gantt charts, but it would not be an unrealistic supposition on the basis of the close interrelationship of Charts and WBS and their similar utilisation in project management.
PRINCE2 is a structured approach to planning and executing projects which originated in the IT industry in the mid-to-late 1990s, and has become accepted as the de facto
project management framework for both UK government and also wider international projects (Charvat, 2003). On the one hand this can be regarded as a sensible strategy, in order to ensure complete consistency in understanding of the execution of projects, and to avoid some of the pitfalls already outlined in respect of Gantt charts and work breakdown structures, such as difficulties when reintegrating subtasks of projects which have been outsourced or decoupled. PRINCE2 is a highly structured and in the words of Elkington and Smallman (2002, p.51) an “exceptionally rigid
” approach to project management, which can be traced to its historical origins in IT. The reason for this is that in the early days of IT development and its explosion in popularity around the turn of the century, the rapid pace of development in IT and also like understanding regarding its capacity and capability meant that many IT projects were subject to a high degree of uncertainty (Winter et al., 2006b). In order to counteract this uncertainty stemming especially from clients who would fail to appreciate the complexity of IT projects and issues of integration, PRINCE2 was a means of preventing so-called scope creep, whereby projects begin to vary very considerably from the original objectives costing time and resources, thus directly contributing to a failure to achieve overall project manage success (White and Fortune, 2002).
In this regard, PRINCE2 is typically highly effective at preventing scope creep because of its rigid and tightly defined approach. Every individual involved in the project has a very clear set of objectives and metrics, and these objectives should, ideally, be directly associated with the overall objective(s) of the project (Meredith and Mantel, 2011). This in itself is laudable, as it does have, in theory, the capacity to deliver a well-planned project within resource time and budget constraints, known as the ‘Iron Triangle’ of project management as discussed by Atkinson et al., (2006). However, whilst PRINCE2 may indeed remain a popular project management framework for large government projects, it attracts many critics. They highlight issues in respect of more time being spent in planning and preparation of actual execution of the project (McHugh and Hogan, 2011); allegations of completing paperwork for its own sake rather than to the benefit or positive outcome of the project (Turner et al., 2009); absolute failure to acknowledge external environmental changes and therefore the need for some measure of flexibility and adaptability in response to changing circumstances (Špundak, 2014); and a rigid compartmentalised mind-set whereby individuals working on the project will fail to adapt in response to the situation their colleagues and fellow team members find themselves in (Brones et al., 2014). It means that whilst a PRINCE2 driven project may indeed meet its own measures and metrics success, these measures and metrics can differ quite considerably from client expectation - an issue which is expanded upon later in this chapter in regards to defining measures of success.
2.2.4 Agile Methodologies
As far back as the late 1990s, it was already recognised that there could be difficulties in respect of PRINCE2 because of its rigid and excessively document-driven approach which could become inefficient in its own right (Augustine et al., 2005). This resulted in a school of thought regarding so-called agile methodologies, also grounded in IT and recognition of changing environments and rapidly developing technologies. Unlike many construction and engineering projects, it is often the case that IT projects can be more easily decoupled and parcelled out into subtasks (Sutherland et al., 2007). Furthermore, IT projects have a tendency to be more iterative in their approach in reaction to changing needs and expectations, and during the course of the project certain facets can emerge which were not previously apparent in the planning and preparation stages. Highsmith (2009) explains that agile methodologies refers to a group of project management frameworks such as waterfall projects and also scrum, which offer the opportunity for greater flexibility in project timescales and resources, and the ability to flex projects to some degree. Understandably, this very flexibility can make supporters of more rigid approaches uncomfortable as they believe that it lacks certainty and can become difficult to explain to clients, especially those who are seeking a defined bottom-line figure (Maylor, 2010). However, in counterpoint to this, Hass (2007) argues that agile methodologies are a closer reflection of the way in which contemporary projects operate.
More recent research into agile methodologies outside the scope of IT reveal that surprisingly they gained little traction despite a number of apparent advantages in respect of flexibility and adaptability, whilst still maintaining a measure of control. Dybå and Dingsøyr (2008) attribute this to the fact that the main professional bodies of project management in the traditional sense, PMBOK (Project Management Body of Knowledge), and the Project Management Institute (PMI) continue to advocate more traditional linear approaches to project management, drawing upon their experience and also highlighting a greater proportion of negative dimensions associated with agile project management such as a lack of control, lack of certainty, and lack of visibility. This being said, Conforto et al., (2014) argue against this, demonstrating in a series of research articles that it is unrealistic to expect either a highly rigid or completely flexible approach to project management to be successful, and there is a need to find a middle ground between the two recognising the realities of the need for certainty for planning and preparation purposes, yet also flexibility in reaction to the realities of external operating environments and changing circumstances. In this regard, agile methodologies have continued to remain more popular in an IT environment, and despite obvious lessons which could be transferred to other environments, there appear to have been limited attempts to do so. Systems thinking, which is discussed later in this chapter, is perhaps a more contemporary interpretation of agile thinking, but one better suited for the realities of modern life.
2.3 Project Management Logic
The logic of project management recognises the necessity of completing a particular tasks or elements in a specific order so as to reach eventual project outcomes. However, as the literature thus far has already demonstrated, each of the well-recognised models and frameworks of good practice project management are subject to inherent flaws and weaknesses, largely related to the issue of inaccurate information for planning and scoping in the first instance. The reasons for such inaccurate information have already been discussed relating to human error, misleading measurements in the sense of KPIs or similar causing individuals to ‘game’ the process of planning for projects, and also more recently recognition of the matter of planned uncertainty and risk in projects. In reality, very large projects spanning months or even years are subject to external conditions which can significantly affect their overall success, and whilst there is a logicality in sticking rigidly to the project plan, real-life often intervenes (van der Hoorn and Whitty, 2015b). For example, it can become apparent during the delivery of project that is necessary to extend its scope, certain conditions can be imposed partway through a project, or barriers can become apparent, and it is not unusual for clients to change their mind during the course of the project as new aspects become apparent (Mansoor et al., 2016).
Reconciling the need for project management control against some measure of flexibility in overall project logic is one which continues to challenge academics and practitioners in the field. The recognition of a systems thinking approach to project management logic has thus gained attention, as analysing and planning a project holistically is a more realistic reflection of the way in which projects and their external environment interact. Furthermore, some academics have highlighted that a myopic focus on specific measures of project management ‘success’, such as cost, time, or quality (a subjective measure in itself), often results in the perverse situation that the project can be considered a failure even though it is completed, because it has not achieved one or more of these metrics (Atkinson et al., 2006). Some suggest that incorrect scoping of the project in the first instance means that a high proportion of projects are destined to fail in any event, but a counter opinion put forward by Vijayasarathy and Butler (2016), is that in practice a holistic approach recognises that there is no single ‘best’ way of operating a project in linear terms and still hope to achieve successful project outcomes on the basis that such linear logical approaches assume that all external variables hold constant; This is perhaps possible in a small-scale project, but unrealistic in a large one. Mansoor et al., (2016) conclude that on this basis, projects can indeed be considered successful in terms of actual eventual outcomes, for example the client is satisfied, but that in terms of rigid adherence to methodology the evidence identified in the literature and meta-analytic studies of construction projects suggest otherwise. The overarching conclusion, therefore, is that the relationship between the perceived definition of project management success, and the logic of how this is achieved should be reconsidered in terms of balance and project outcomes relative to a broader range of circumstances. It is this foundation which gives rise to the systems thinking approach discussed below.
2.4 Planned Uncertainty in Projects
2.4.1 Systems Thinking
Proponents of the systems thinking approach Hitchens (2017) and also Kasser (2017) both advocate the concept of project planning approached in a holistic and interconnected manner. The construct of systems thinking broadly holds that as organisations represent a structured grouping of interconnected systems both internally, and also externally in terms of supply chain networks and customer relationships, it is in fact more realistic to approach project planning and management with this overarching mindset. Hitchens (2017) and also Kasser (2017) have long supported the idea of systems thinking in a multidisciplinary capacity, recognising that systems thinking as abstract construct is interlaced with many levels of organisational behaviour and activity. Thus, each argues, it is in fact more logical to adopt this approach to effective project management as this is more likely to deliver successful project outcomes which have realistic measures of project success. However, they equally recognise that for those educated in a linear approach to project management, this approach to project management thinking may take some time to absorb and apply.
Although both Hitchens (2017) and Kasser (2017) share a belief in the value of the systems thinking approach towards end outcomes, they do differ somewhat in their approach to working through such systems thinking frameworks. Hitchens (2017) is firmly people-based in his approach, recognising the irrationality of human behaviour in many circumstances, and comparing with behavioural economists such as Ariely (2008) and the way in which it is easier to ‘nudge’ human behaviour towards planned outcomes and allowing individuals to work more freely rather than prescriptively, an important caveat being a very clearly defined set of achievable outcomes and any specific requirements being widely communicated. Hitchens (2017) supports this approach to project management on two fundamental grounds; firstly if left to their own devices, people will often develop innovative and effective solutions to solving problems, and secondly this encourages more creative outcomes. Hitchens (2017) also believes that this has the outcome of being more efficient overall, as rather than spending time attempting to game restrictive project management systems or completing paperwork for its own sake, (a noted criticism of PRINCE2 for example), people are treated as adults and deliver effective project outcomes to the mutual benefit of all concerned.
Conversely, Kasser (2017), an engineer by training, adopts a more structured approach to systems thinking, providing a range of alternative systems thinking frameworks or toolkits which can be utilised in different circumstances, and, in his view, provide guidance although not prescriptive instruction on alternative ways of addressing issues challenges and problems more holistically. Kasser’s (2017) focus is more concerned with recognising a wider ripple effects or unintended outcomes of a rigid linear approach to project management which can create inefficiencies, bottlenecks, and problems elsewhere. He suggests that greater thought and consideration should be given to the questions to ask during planning and scoping project as understandably, if not asked specific questions, it is not necessarily wise to assume that individuals will volunteer what then ultimately transpires to be critically important information. However, it is important to reiterate that both Hitchens (2017) and Kasser (2017) believe in the value of focusing on the end objective with broader outcomes in mind, rather than a narrow prescriptive approach which can become counter-effective.
Other more traditional project management scholars also recognise that facets of the systems thinking approach have merit in contemporary business environments, most notably the importance of recognising the unavoidable existence of risk to a greater or lesser degree (Chapman and Ward, 2008). Much has been researched in respect of risk management in projects, and definitions of risk themselves vary depending on the context of the project and the individual or groups recognising the risk and its potential impact. A line of research into the management of planned risk, treating it as an opportunity as opposed to a threat, has gained some interest, typically utilising the power of statistics to consider the relative impact of possible variables which can directly and indirectly influence projects (Hopkin, 2017). Known as the PUMP approach (Planned Uncertainty in Managing Projects), this has some similarities with a systems thinking approach, and also that of agile project thinking stemming from IT-based project management. Supporters of this more flexible or adaptive approach to project management such as Chapman and Ward (2008) concur with both Kasser (2017) and Hitchens (2017) as regards the role of people in managing project uncertainty, and the need for project managers in particular to think laterally regarding the success and overall management of projects, thinking more widely regarding alternative variables and approaches. Indeed, Gardiner (2005) was discussing this concept more than a decade ago in regards to the need to think more broadly in respect of definitions of project management success, and measures of how this is achieved.
2.5 Linking Logic and Gross Margin as a Metric
2.5.1 Definition of Project Success
Turning to the challenging question of what constitutes project management ‘success’, this an issue which as alluded to during discussions in this chapter, is one which divides commentators and stakeholders alike (Dvir et al., 2003). The reason for this is that definitions of success in project management have long been driven by the so-called iron triangle, the purported trivium of cost, resources, and scope, whereby it is a commonly held belief that it is impossible to adjust any one of these three without impacting upon quality. Atkinson (1999), describes the iron triangle as two best guesses and arbitrary metric, similarly Velayudhan and Thomas (2016) concur that focusing purely on cost as a measure of success largely overlooks much wider interpretations of project management success in recognition of the systems thinking argument that project scope can often become somewhat fluid at a client's behest. Todorović et al., (2013) point out that one of the main challenges against PRINCE2 for example is that projects undertaken using this approach are considered a success because they followed the rigid framework demanded by PRINCE2, even though the client or end user is unhappy. It highlights the potentially bizarre outcomes which can result from rigid and structured thinking regarding metrics of project management success.
Davis (2014) also agrees that dogmatic adherence to financial metrics alone, not necessarily cost, has distorted approaches and understanding of what constitutes project management success, and in his opinion this is what results in project management gaming and also the deliberate under or over inclusion of slack in projects based on the motivating factors of KPI's internally, or an attempt to win contracts on the basis of external imposition. Likewise, Serra et al., (2015) argues that organisations which operates as cost centres work towards arbitrary accounting metrics which do not fit comfortably within project management activity. Principally because accounting activity is linear and has already been established, projects can rarely be described as such in the modern environment. Shenhar et al., (2001) believe that in a worst-case scenario this results in counterintuitive or even diversionary outcomes as individuals responsible for scoping or delivering part of the project, understandably, work towards their own personal metrics, entirely ignoring wider organisational outcomes or potentially even adverse situations. This is where a systems thinking approach would be far more relevant in understanding and then determining what constitutes project management success, although Van Der Westhuizen and Fitzgerald (2005) recognise that is much more challenging to establish in the first instance and may not fit comfortably within established project management frameworks. Such complexity and confusion when working towards widely albeit superficially understood project management frameworks helps to explain why it is easier and safer for many project managers to utilise conventional approaches, even though they may well be in full knowledge of the fact that such conventional approaches offer little in the way of true information regarding project management progress or success. In the opinion of Kerzner (2013), this requires a radical approach, more in line with systems thinking which will help those involved in projects think more sensibly about measures of success.
2.5.2 Using Gross Margin as a Success Metric
However, in pragmatic recognition of the fact there is no simple solution to the need to measure projects in order to provide at least some indication of progress and control, it seems unlikely that established financial metrics at least will be abandoned in the foreseeable future. Furthermore, it is also wrong to suggest that financial metrics are themselves fundamentally inappropriate, as they do provide an objective measure of success, and for many end-users or clients of projects, financial concerns are likely to be important (Meredith and Mantel, 2011; Pinto, 2013). However, it is acknowledged that a single-minded focus on financial metrics for their own sake is a flawed approach. A true scientific assessment of any situation requires the isolation of a single variable, and one which can be measured on a like-for-like basis in order to understand its overall effect or influence. Ergo, consideration of the cumulative effect of gross margin on projects is considered to be a means of highlighting the impact of rigid adherence to established frameworks without due consideration for wider impact. Obvious examples include hanging tasks as referred to earlier in respect of Gantt charts and also excessive slack associated with WBS. It is hoped, therefore, that by using gross margin as a consistent metric across all projects irrespective of their resources or end objectives, it will be possible to demonstrate the impact of poor decision-making and highlight to those involved in projects the value of adopting a systems thinking approach.
2.6 Conceptual Framework
The discussions throughout this chapter have highlighted competing opinions and perspectives regarding good practice in project management, and challenges associated with the need to balance advance planning and preparation against the reality of changing organisational and external environments. Uncertainty is now, paradoxically, a fixed feature of business planning, meaning that project managers themselves must learn to embrace the existence and reality of uncertainty, and also consider wider interrelated outcomes in terms of project planning, management, and outcomes. To this effect, Figure 2.1
below conceptualises these competing outcomes in a visual framework to illustrate their inter-relationship with one another.
Figure 2.1: Conceptual Framework (Author, 2017)
This chapter has critically discussed concepts, theories, and opinions from academics and practitioners regarding alternative approaches to effective project management. Particular attention has been directed towards discussions of what constitutes the best approach to project planning, and corresponding measures of project success. There is growing evidence in support of the view that planned, rigid, and strictly linear approaches to project management are both ineffective and inaccurate leading to a flawed or false sense of positive project management outcomes relative to wider project influences. This has given rise to support for a systems thinking approach which is more holistic in nature and recognises the interconnected nature of projects and their impact upon organisational activity, and potentially also other concurrent projects in organisational pipelines. Debating the question of how project management success should be measured, it is posited that by monitoring the cumulative impact of gross margin, this is likely to be a better indicator of whether or not the project is indeed successful, and thus the conceptual framework shown above illustrates why this metric has been identified as one which can fit comfortably with a systems driven approach. Accordingly, chapter 3 which follows describes the methodology utilised in this study to analyse the impact of gross margin as a metric of project management success, framed within a systems thinking approach.
This is a quantitative design that tests a hypothesis, it is deductive in approach using the Mann-Whittney U test. The outcome will be a tested hypothesis and null hypothesis.
The Mann-Whitney U test was developed as a test of stochastic equality
(Mann and Whitney, 1947). However, it is not often that the test is directly interpreted in this way. In practice, the Mann-Whitney U test is more broadly used to interpret whether there are differences in the "distributions" of two groups
or differences in the "medians" of two groups
. However, this is not so much a choice that you make, but is based on whether the distribution of scores for both groups of your independent variable
(e.g., the distribution of scores for "Logic Linked Gannt Chart" and the distribution of scores for "Non-Logic Linked Gannt Chart" for the independent variable, "Planning") have the same shape
or a different shape
If the two distributions have a different shape
, the Mann-Whitney U test is used to determine whether there are differences in the distributions
of the two groups. However, if the two distributions are the same shape
, the Mann-Whitney U test is used to determine whether there are differences in the medians
of the two groups.
The Mann-Whitney U test works by ranking each score of the dependent variable (e.g., engagement), irrespective of the group it is in (e.g., logic or non-logic), according to its size, with the smallest rank assigned to the smallest value. The ranks obtained for logic linked projects are then averaged, as are the non-logic linked ranks. This results in a mean rank for logic linked projects and a mean rank for non-logic linked projects. If the distributions are identical, which is the null hypothesis of the Mann-Whitney U test, the mean rank will be the same for both logic linked and non-logic linked projects. However, if one group (e.g., non-logic linked) tends to have higher values than the other group, that group's scores will have been assigned higher ranks and will have a higher mean rank (and vice versa for the group with lower scores). It is this difference in mean rank that is tested by the Mann-Whitney U test for statistical significance. Using this approach, different distributions of scores can be accommodated by the Mann-Whitney U test when determining whether values (i.e., via mean ranks) are different between two groups.
In some cases it may be required to explicitly state the null and alternative hypotheses for a Mann-Whitney U test, and then state which was accepted and rejected at the end of the experiment. One such null hypothesis might be:
H0: the distribution of scores for the two groups are equal
And the alternative hypothesis might be:
HA: the distribution of scores for the two groups are not equal
However, another way to express the alternative hypothesis is as follows:
HA: the mean ranks of the two groups are not equal
The reason for describing the alternative hypothesis with respect to mean ranks is due to a problem that can occur if groups with different variances are present. Under these conditions, you can have very different distributions but still not reject the null hypothesis of equal distributions (see, for example, Hart, 2001) or get a good idea of whether values are higher or lower in one group compared to another. Indeed, any interpretation of differences between groups becomes difficult when variances are not equal.
It is the intention to utilise data from 120 projects selected from within the organisation to which the researcher has access, and these will comprise projects selected at random from over the last 13 years within the organisation. Projects of differing sizes and which focus on different aspects of engineering will be selected, but have the benefit of adopting similar structure as they have emanated from the same organisation. From within these 120 projects, 25%, i.e. 30 projects have utilised a systems approach with notable benefits already evident.
4.2 Data Analysis
SPSS (Statistical Package for Social Sciences) will be used in order to compare data from the projects and ascertain whether a systems approach is reliably more beneficial for project outcomes, utilising gross margin as the dependent variable. It is the intention to organise the data gathered from the projects comparing two key aspects and variables.
- Comparison between a logic linked (Systems approach) WBS & effect on gross margin and a non-logic linked WBS & effect on gross margin.
- Deliver / Handover dates using a logic linked (systems approach) vs Delivery / Handover dates using a non-logic linked approach.
The outcome will ascertain which of these variables appear to have the greatest effect on the reliability or otherwise of traditional and systems thinking approaches to PM.
In order for the Mann-Whitney U test to be undertaken, four assumptions must be met. The first three relate to your choice of study design, whilst the fourth reflects the nature of the data:
- Assumption #1: The data has one dependent variable that is measured at the continuous or ordinal level.
- Assumption #2: The data has one independent variable that consists of two categorical, independent groups (i.e., a dichotomous variable).
- Assumption #3: The data has independence of observations.
- Assumption #4: The distribution of scores for both groups of the independent variable.
5.0 Theoretical Hypothesis
Null-Hypothesis: H0 “The presence of a WBs and fullly logic linked Gantt schedule bar chart DOES NOT allow for a modelling of the project sufficient to assist the project manager in enhancing opportunities for a better project outcome, (which in this sample of projects is assessed by comparing gross margin)”.
Hypothesis: H1 “
…is not (statistically)proven
5.0 Ethical Considerations
As the data gathered will be secondary in nature, concerns regarding research ethics will focus on the confidentiality and security of the data which is likely to be commercially sensitive, and the necessity of focusing on objective project outcomes (Saunders et al., 2015). As the researcher has access to and knowledge of the projects they will be analysing, it is also important to maintain an objective perspective in interpreting the output of the SPSS analysis in light of wider contextual factors.
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