Managing scope is a critical process in information technology (IT) project management. Reporting the status of scope requires both an understanding of the status of individual activities and the aggregation into an overall status for the project. Unlike cost and schedule which have the objective measures of currency spent or days passed, scope is subjective. Understanding the status of scope as a project moves forward is critical to success; however, many times IT projects fail due to mismanagement of scope constraints. Recent research has confirmed status reporting and analysis as a major problem in IT projects. Other research has looked at how computational intelligence (CI) techniques might be applied to the domain of project management for cost and time constraints. This study looks at scope, a third constraint of project management. Since scope has properties of imprecision and vagueness, fuzzy logic would be an appropriate tool from Computational Intelligence. This study focuses on using the recently proposed Z-mouse for the collection of status information, and then using fuzzy logic for the reporting of project status for the scope constraint.
Project managers collect data on the performance of their
projects in order to be able to report the status and to
forecast future performance. The Standish Group recently
surveyed 400 organizations and reported that only 32% of
information technology projects were successful, with
close to a quarter of the projects reported as failures
Weill and Broadbent have stated that information
technology (IT) is "very strongly project based"
As mentioned, the overall project status should be
determined by aggregating the individual activity statuses.
Instead, it is often reported as the opinion of the project
manager, which is subjective. Recent postings on the
LinkedIn internet site for professional project managers
requesting help on project status met with a wide variety of
rapid responses indicating the high interest level of
practicing project management professionals. Since
executive managers tend to focus on problem areas, this
translates to projects in trouble and as a result, there is a
tendency to under report status. Snow and Keil
investigated variance between the true status of a software
project from the reported status and found that accuracy
was a major problem. "The intangible nature of software
makes it difficult to obtain accurate estimates of the
proportion of work completed, which may promote
misperceptions regarding project status"
Snow and Keil found that in addition to misperceptions
in the status of a software project, project managers might
also censor the status reports of poorly performing
projects. They cited an example of a project that lost $125
million over 3 years, yet senior management did not have
any insights into the problems. "The combined effects of
project manager misperceptions (errors) and bias in
reporting leads to what we call “distortion” in the project
status information received by senior executives"
Projects by definition are unique, and "because of the unique nature of projects, there may be uncertainties... The project team must be able to assess the situation and balance the demands in order to deliver a successful project" (PMBOK 2008). Assessments are the feedback during the execution of a project so that the project can be guided to a successful completion. As projects move forward, project managers are constantly gathering data on the status, converting that data into useful information to be reported, and then acting upon the information. Often the data is vague, or needs interpretation. An example of vague data is that it is difficult to determine to what extent the scope is being met. To label project scope as 67.35% met is recognized as impractical precision. The imprecision in the data is the subject of this study, and rather than using traditional methods to attempt to quantify scope, computational intelligence offers new tools and techniques for capturing vagueness. Computational intelligence tools can "identify semantically ambiguous concepts and convert them to fuzzy sets" (Cox, 1999) which can then be resolved into solutions that can be handled by project managers.
With over 300,000 members, the Project Management Institute (PMI) is recognized worldwide as an authority on project processes. Their Project Management Book of Knowledge (PMBOK) does not spell out the format of status reports, nor does it tell project managers specifically how to write a status report. Instead the PMBOK identifies processes, defines inputs, tools and techniques, and the data flows that tie the processes together (PMBOK 2008). The PMBOK, as stated in section 1.1, is an assembly of good practices that has the consensus and general agreement of project management professionals. The PMBOK "is a guide rather than a methodology. One can use different methodologies and tools to implement the framework" (PMBOK 2008). This gives practitioners the flexibility to choose techniques that work for their given situation.
The Project Management Institute's PMBOK identifies the performance reporting process as part of their Monitoring and Controlling process group (PMBOK 2008). The PMBOK lists three outputs from the performance reporting process: 1) Performance reports, 2) Organizational process assets updates, and 3) change requests. The purpose of the reports is to act as feedback into the processes that "track, review, and regulate the progress and performance of the project; identify any areas in which changes to the plan are required; and initiate the corresponding changes" (PMBOK 2008). To this extent, data is converted into actionable information guiding the project to completion. This process of reporting performance is crucial to initiating corrective actions and preventive actions, and becomes part of the organization's lessons learned historical database.
When reporting the status of projects, Dow and Taylor
have found that project dashboards are often used by senior
managers
It would seem that numerical inputs into reports and dashboards should yield an objective status for reporting purposes. The ideal ought to be that for a given activity, the fixed numerical data goes in and a Green, Yellow, or Red project status comes out. The next stage in the process would be that the individual activities are then mechanically aggregated into an overall project status. The reality is that there are many factors that influence the decision to label a project status with a particular status value for a singular activity, and that the aggregation of those statuses for multiple activities of the critical constraints is open to interpretation as well.
When the project status is not a clear green or red, Barnes and Hammell found that "ambiguity is present in the scenario where the expert had to decide that the status of a project is Yellow" (Barnes 2009). Looking at the case of rating just one of the activities in a project, it is simple for status green. Most managers would look at a truly green activity and agree that the status is okay, or green. Beyond green status, it becomes questionable. Barnes has shown that yellow status can be misinterpreted or communicated as green.
The problem is much worse when the project is in
serious trouble. Snow and Keil found that IT project status
of red is frequently misreported
The second stage in reporting status, aggregation of the constraints into an overall project status, has been studied by a number of authors. But there are complexities that make the automatic summarization difficult. For example, a project that is ahead of schedule might also be significantly over cost at that point in time. What is the true status of that project? Just looking at the raw data might yield a green status on schedule, but a red status for cost. However, the costs might reflect that fact that the project is ahead of schedule, so it might be the case that the project will finish ahead of schedule, and ultimately within cost constraints. Ahead of schedule, and meeting cost constraints when completed would seem to mean the project is "green", in spite of a "red" cost. This implies that making status a simple mechanical output of numeric inputs can produce status errors. Human intervention is required to interpret the data into meaningful information.
The cost and schedule constraints of project management have numerical quantities that can be measured. The numbers have an element of objectivity which can be used in forecasts. Econometric methods such as regression analysis and autoregressive moving averages, or time series methods such as linear prediction, trend estimation, and moving averages have been used by practitioners of project management (PMBOK 2008). Currencies are tracked and reported using time series methods such as earned value (PMBOK 2008). Calendar dates and/or labor hours can be tracked for the time constraint. Depending on the project, quality might also be measurable and reportable.
Scope, however, is much more difficult to measure, and
at the same time is the critical element from which the time
and cost are derived. Richardson and Butler stated that
"the concept of project scope is a foundation idea. It
establishes the base for much of the subsequent
management activities"
In a similar manner, scope and the WBS feed into cost
estimates and cost management. This means that if the
scope is wrong, the time and cost estimates will be wrong,
or if the scope changes then time and cost can be severely
impacted. Time and cost estimates are calculated by
activity, but the list of activities comes from the scope
definitions and WBS that were completed early in the life
cycle of a project
Schwalbe states that managing scope is especially
difficulty on IT projects. Scope can be relatively undefined
at the beginning, can grow out of control due to creep, and
suffer from an inability to verify
Weill and Broadbent have stated that projects are late
sometimes due to specification changes, or new business
needs that occur during the project
The IT industry is full of examples of scope creep. A
Google search of the term "project scope creep" produced
over 4 million hits. A quick review of just a fraction of
these web sites demonstrates a common assumption: that a
project manager knows exactly and precisely the scope,
and that the problem is that the scope changes or grows.
This is a questionable assumption. Fleming and
Koppelman, major advocates of the deterministic Earned
Value model, admit that "earned value accurately measures
project performance, but must assume that scope definition
is adequate"
Computational intelligence (CI), implemented in a variety of soft computing techniques, has allowed the automation of the handling of vague and imprecise data. Computational intelligence offers a revolutionary set of tools capable of responding to fuzzy, inaccurate inputs. This research envisions that these tools and techniques can be effectively applied to project status assessment. This study concentrates on Information Technology (IT) projects, in particular the scope constraint, because of the inherent lack of a measure for scope. The IEEE Computational Intelligence Society defines CI as a number of core technologies, among them fuzzy systems, neural networks, evolutionary programming, and genetic algorithms (IEEE 2011). These technologies build intelligent systems to help with complex problems in which the information and data are vague, approximate, and uncertain. For this research computational intelligence will focus on fuzzy logic as applied to project status. In order to put a reasonable boundary around the subject, only project scope status will be evaluated.
Lotfi Zadeh proposed the concept of fuzzy variables that
are linguistic in the 1960's. For project cost these
linguistic variables might be (costs = {over, on cost,
under})
Example: "hot", not 85 degrees Nonlinear and changeable Analog (ambiguous), not digital (yes/no)
Zimmermann expanded upon Zadeh's description of
fuzziness as that of possibility, with the idea of a possibility
distribution. Zimmermann’s example is that a fuzzy set
F~ = { (1,1), (2,1), (3,0.8) } has a possibility
distribution such that 0.8 is the possibility that X is 3
Some authors have suggested that in spite of objective and
measureable numbers in cost and time constraints, there
can be fuzziness in the interpretation of those numbers. Li,
Moselhi, and Alkas proposed a forecasting method for cost
and schedule constraints using Fuzzy Logic to compensate
for the variability found on construction projects. They
looked at four different, generalized methods to forecast
project status
Other researchers have applied computational intelligence tools to project management for schedule and time control. Jin-Hsien Wang and Jongyun Hao proposed a Fuzzy Linguistic PERT (Program Evaluation & Review Technique) to replace stochastic methods that use means and standard deviations. They assert that too much data may be needed to obtain the random variable distribution, so fuzzy methods are more applicable (Wand 2007). Wang and Hao expanded PERT/CPM (Critical Path Method) by storing each activity duration as a fuzzy set.
Klakegg, et al, have already analyzed what should be measured in projects, and at the same time they acknowledge that warning signs of problems are "often unclear and imprecise" (Klakegg 2010). They describe what, but not how to measure the subjective constraints. While other researchers have proposed how fuzzy set theory can be integrated into project management across the time and schedule constraints, this work focuses on the scope constraint. Additionally, we will use a CI tool to capture scope status directly from experts in a more realistic, human friendly form. Once the status is captured, it can then be aggregated into an overall scope status for a project. Without an objective criterion such as currency spent or elapsed time, scope is difficult to measure. Fuzzy systems allow the capturing of this subjective data, and then the aggregation using recognized fuzzy set mathematics.
This study proposes to use computational intelligence (CI) tools, in particular alternative tools like fuzzy logic, to understand the status of a project's scope. This use of CI is in contrast to the more conventional bivalent logic, which Zadeh described as working well with exact numbers, intervals, and probabilities. Rather than the hard, crisp nature of bivalent logic, these CI alternatives have been sometimes labeled "soft computing."
Zadeh stated that "it is a common practice to ignore imprecision, treating what is imprecise as if it were precise" (Zadeh 2009). The computing power available in the 21st century allows for the implementation of the concepts that Zadeh called computing with words (Zadeh 2009). Given the imprecise nature of project scope due to the linguistic nature of requirements, it makes more sense to use fuzzy intervals and fuzzy sets to capture the essence of the status of scope.
In his acceptance speech when receiving the Ben Franklin award at Villanova University in 2009, Zadeh provided an analogy for fuzzy logic:
In bivalent logic, the writing/drawing instrument is a ballpoint pen. In fuzzy logic, the writing/drawing instrument is a spray pen—a miniature spray can — with an adjustable, precisely specified spray pattern (Zadeh 2009).
Zadeh has stated that a valid application of fuzzy logic is in the handling of imperfect information. At Villanova Zadeh went on to say that "imperfect information is defined as information which in one or more respects is imprecise, uncertain, vague, incomplete, unreliable, partially true or partially possible" (Zadeh 2009). This leads to the core concept that membership in a fuzzy set is a matter of degree. For project managers, when looking at the status of a given line item's scope using fuzzy logic, that status is allowed to be a matter of degree. In practical terms this means that the scope of an item can be of status mostly yellow, and at the same time that same scope item can be of status a little red.
This study uses a fuzzy data collection tool proposed by
Zadeh, colloquially referred to as the Z-mouse. This tool is
a spray paint web gadget that implements Zadeh’s spray
paint analogy. Jose Barranquero and Sergio Guadarrama
created the Z-mouse to gather fuzzy opinions, or
perceptions as they call it, from users
This study builds upon their prototype by evaluating the fitness of their Z-mouse concepts when applied to project management. Project managers are asked to rate the scope for a WBS activity on a scale that is words, not numbers. It is anticipated that the non-numeric scale will be quickly recognized and easy to use by experienced project managers. Figure 1 illustrates the Z-mouse web gadget using a non-numeric, linguistic scale.
Barranquero and Guadarrama go on to state that the
Z-mouse can be easily learned by non-expert end users
In contrast to fuzzy systems, social scientists have used
psychometric scales extensively in survey research. In
Likert scales the survey participants are asked to select one
number from a variety of choices. Many times these
choices are an ordered scale, forcing the user to select one
and only one value
One drawback from using Likert is that it cannot handle that people will perceive a given choice as falling into two categories simultaneously. Those models view this human tendency as a paradox, or a violation of the rules. A fuzzy system allows that project managers might perceive the status as mostly yellow, with some modest amount of red. Having both statuses at the same time for one activity is an acceptable possibility in fuzzy systems. Another drawback to scaling systems is that a statistically valid number of participants are required in order to validate the data. In project management there might only be two or three participants working on a WBS activity, a number not amenable to conventional crisp probability.
This study gives participants a description of an activity and asks them to evaluate that activity for the status of the scope. Figure 2 gives an example that is illustrative of the types of questions in this survey.
Activity 1: Web Page Design End users have requested changes to the web pages that should be relatively simple to accomplish. However, these end users are known to change their minds frequently. Time Constraint: the project is on schedule
Cost Constraint: this activity is within the budget Scope Constraint: use the Z-mouse to input the status Figure 3 is an example of a potential response using the Zmouse. The individual inputting the status would select one of the four shades of grey from the pallet, and then paint the status bar where they think appropriate. If they want to indicate a lesser importance, then they would select a lighter shade of grey. Figure 3 would be an example of a project manager deciding that scope constraint was mostly yellow, yet leaning towards red. It should be pointed out that these spray paint data points are converted to numeric values, and then evaluated using the strict mathematical rules of fuzzy sets.
The next step is to aggregate the individual scope statuses
for each activity into an overall status for a project. Since
the origins are the fuzzy words (green-yellow-red), the
proposed aggregation method would be an implementation
of Zadeh's computing with words. Zimmermann offers
three common methods to aggregate the individual inputs:
COA (center of area), COS (center of sums), and MOM
(mean of maxima)
Professional project managers have objective data for the time and cost constraints on their activities. With the introduction of an input tool to capture the status of scope, the measuring and reporting of subjective opinions of scope status can be done. The next step would be that each and every WBS activity would have a scope status that could be aggregated into an overall project scope status. Based on the gathering of this scope data it is expected that this would become the third constraint in a fuzzy system such as the one proposed by Li, Moselhi, and Alkas that only addresses cost and schedule. Since IT projects are unique and, thus have vague and imprecise scope requirements, it is believed that two questions will be answered in the positive by this study: 1) fuzzy logic can provide a tool for measuring scope of individual activities, and 2) the fuzzy scope can be aggregated into a meaningful project status. IEEE Computational Intelligence Society statement of scope: http://ieee-cis.org/about_cis/scope/