Missing Nonlinearities in Quantitative Project Management

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Missing nonlinear relationships in quantitative project

management

Pavel Barseghyan, PhD

http://pavelbarseghyan.wordpress.com/

The most important problem of modern methodologies in project management is first of all, the

need for improvements of the situation with the massive failures of projects and the related hugefinancial losses.

It should be noted that, despite great efforts to develop sophisticated new quantitative methods in

this area, such as System Dynamics (SD), Earned Value Management (EVM) and others, the

situation with massive failures of projects did not change significantly for the better over the pasttwenty years. But these are the years during which there was a serious and important progress in

the modern methodologies of quantitative project management, including SD and EVM.

Thus, on the one hand we see a real progress and prospects in the quantitative project

management, on the other hand almost unchanged statistics of project failures, suggestingpossible serious drawbacks of these methods.

This is a serious challenge for developers and users of modern PM methodologies, because the

main purpose of the development of quantitative methods in this realm is to increase the level ofcontrollability of projects, and as a consequence, reduce the number of failures of projects.

Since, in fact, this goal was not achieved, or the achieved results were so modest that they do not

justify the huge amounts of money spent, then the question naturally arises about the analysis of

the causes of this state of affairs.

Analysis of the extensive literature on this subject and many years of experience with project

data analysis to create new methodologies in project management indicate that one of the biggest

reasons for the failure of projects, along with other no less serious causes, is the disadvantages

and undeveloped quantitative methods in project estimation.

A more detailed analysis of this problem shows that these shortcomings of modern quantitative

methodologies of project management related to the fact that they do not take into account a

number of nonlinear relationships between project parameters, which are necessary to reflectadequately the essence of the project and the behavior of the team of performers.

The main sources for obtaining these functional relationships between project parameters incontemporary PM are the statistical project data mining, mental models and expert information.

Statistical project data mining results have low accuracy for project estimation and other

purposes. Therefore, without significant improvements in statistical methods, their use to assessprojects just does not make sense because of the large estimation errors.
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Mental models contain a considerable portion of subjectivity and consequently estimation risks

are high even for the short term project report generation purposes because of accuracy problems

and qualitative nature of mental models. Therefore, it is advisable in the current quantitativemethodologies to find a replacement of mental models through the development of more

adequate models in the form of reliable and data independent functional relationships between

the parameters characterizing the process of human labor.

As for the expert methods, traditionally they were not able to predict correctly major delaysduring the project execution. The situation with these predictions is interesting because, if the

duration of a typical task according to the experts opinion has a natural upper limit, then none of

the experts as an estimate of the duration will specify a value that exceeds the natural limit anumber of times, since such a decision has no justification. But in reality, very often the actual

duration of work exceeds the expected time a few times. This is the phenomenon of the delay of

human work, which is very difficult to explain and manage.

Also the situation with expert estimates can help to explain another phenomenon, which is the

relative constancy of the percentage of failed projects during the last twenty years, as the expertestimates have a dominant role in both old and new PM technologies.

Analysis indicates that one of the main reasons for this disadvantageous situation in quantitative

project management is the missing nonlinearities in human labor description and mathematicalmodeling. This suggests that the leading methodologies in the area of quantitative project

management, such as SD and EVM, despite of their great positive role in this area, are in need of

further improvements of a fundamental nature. In particular, these improvements may involveconsideration of various nonlinearities inherent to the behavior of both the projects, regardless of

their size and complexity, as well as development teams again, regardless of the number of

people in the team.

These nonlinear relationships that accompany the work of people and need to be describedquantitatively can be divided into three following groups.

1. Nonlinear relationships between project parameters that arise as a consequence of

the balance between complexity of work, objectives of work and productivity of

work performers.

2. Nonlinearities that arise as a consequence of the limited capabilities of work

performers and limitations that are connected with technological feasibility of work

3. Nonlinear relationships that characterize communication and contacts between

people, and , as a consequence, team productivity

First group of nonlinearities:

Nonlinearities that arise as a consequence of the balance between complexity of work,

objectives of work and productivity of work performers

Main source of nonlinear functional relationships between the parameters characterizing the

process of human labor, it is a natural balance between the three following group of factors:


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1. Complexity of the work that includes the size and the difficulty of work,

2. Goals and objectives of work,

3. Professional capabilities of the work performers.

Each specific combination of these three components determines a particular state of humanwork as a system. The quantitative reflection of the balance between the complexity of work, the

objectives of work and team productivity is the equation of state that reflects the equilibrium of

the process of human labor. Any project as a specific kind of human work can have its own

equation of state too.

Any change in work parameters leads to the transition of the work from one state to another

which occurs at predictable trajectories. These transition trajectories in the project space are the

nonlinear functional relationships between the parameters of work.

State equations contain all possible functional relationships between the parameters of work(project) therefore it cannot be used directly for project estimation. But in combination with the

objectives of work or projects goals equation of state can serve as a basis for deriving the above

mentioned nonlinear functional relationships suitable for project estimation purposes.

Second group of nonlinearities:

Limited capabilities of work performers and limitations that are connected with

technological feasibility of work

The main sources of these nonlinearities are the limited capabilities of the work performers

(development team and individuals in it), as well as limited technical and technologicalfeasibility of the projects in the specific area of industry.

If the complexity of the project of specific technical product is close to the limiting possibilities

of engineering and technology at that time, then this may give rise to a number of nonlinearities

in the sense of the technical feasibility of project.

If the complexity and, therefore, the difficulty of the project are close to the professional limits ofthe development team, then regardless of the absolute complexity of the project more

nonlinearities can arise between the parameters of human work, causing delays and failures of

projects.

If in addition we take into account that for the economic reasons both projects and project teamsshould be close to the upper limits of their capacities, it is clear that people involved in such

projects are almost always working in the field of double nonlinearity.

Third group of nonlinearities:

Nonlinear relationships in communication and contacts between people


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The work of any human group is impossible to imagine without communication within the team

and communication of the team with the outside world. Communication is literally the core of

any organization of human labor and is taking place through the contacts between people, theintensity and effectiveness of which have direct influence on the productivity of human labor.

Communications have a dual effect on the productivity of human groups and the size of thegroups in this sense is important. On the one hand communication through discussions and

exchange of ideas enhances productivity of working groups. On the other hand communicationreduces the labor productivity because of the wasted extra time needed for the contacts between

people. Therefore, the nonlinear dependence of productivity on the number of people must serve

as a basis for human work organization both for small and large development teams.

Typical nonlinear communication characteristics of the group of people are the dependency of

the number of internal contacts between group members on the group size, the dependency of the

number of external contacts of group members on the group size, and so on.

The same nonlinear relationship between the productivity of the team and its size should be thebasis for constructing a hierarchical cell structure of organizations.

In such organizational structures the roles of cells are played by human groups, which are

characterized by the dynamics of their internal and external contacts. In turn, the quantitative

description of such hierarchical structures is based on nonlinear communication characteristics ofindividuals and groups.

All of the mentioned nonlinearities are very important for an adequate description of human

labor, and in particular for the quantitative description of the project works. Therefore, these

nonlinearities must be an organic part of any model to represent the total effort of human labor,

its duration, cost, and the various risks associated with successful job performance.

Currently, the existing quantitative techniques in project management do not take into account

these nonlinearities in the mathematical models of effort, duration and other parameters of

human labor.

To fill this gap in the quantitative project management it is necessary to develop fundamentally

new methods of mathematical description of human labor. In other words it is necessary to

change the paradigm in this field and make a transition from primitive empiricism and

fragmentary mathematical models to a more fundamental quantitative description of humanlabor. To do that it is advisable to use the existing more advanced methods and techniques that

are developed in quantitative fields of knowledge such as physics, mathematical biology,mathematical economics, etc., as well as to develop new methods for problems related to specifictasks of the quantitative description of human labor.

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Missing Nonlinearities in Quantitative Project Management