Tracking Project Progress with Earned Value Management Metrics

A Real Case

Maria Teresa Baldassarre

, Nicola Boffoli

, Danilo Caivano

1,2

and Giuseppe Visaggio

1,2

Department of Informatics, University of Bari, Bari, Italy

SER&Practices SPIN-OFF, Bari, Italy

Keywords: Earned Value Management, Decision Model, Project Monitoring.

Abstract: According to the Project Management Institute (PMI) project management consists of planning, organizing,

motivating and controlling resources such as time and cost in order to produce products with acceptable

quality levels. As so, project managers must monitor and control project execution, i.e. verify actual

progress and performance of a project with respect to the project plan and timely identify where changes

must be made on both process and product. Earned Value Management (EVM) is a valuable technique for

determining and monitoring the progress of a project as it indicates performance variances based on

measures related to work progress, schedule and cost information. This technique requires that a set of

metrics be systematically collected throughout the entire project. A consequence is that, for large and long

projects, managers may encounter difficulties in interpreting all the information collected and using it for

decision-making. To assist managers in this tedious task, in this paper we classify the EVM metrics

distinguishing them into five conceptual classes and present an interpretation model that managers can

adopt as checklist for monitoring EVM values and tracking the project’s progress. At this point of our

research the decision model has been applied during an industrial project to monitor project progress and

guide project manager decisions.

1 INTRODUCTION

Project management is the discipline of planning,

organizing, motivating, and controlling resources in

order to fulfil specific goals, whereas a project is a

temporary effort with a defined start and end point,

usually time and budget constrained, carried out to

meet unique goals and objectives and deliver results

that provide added value and innovations to current

practices on time and within budget (Pyster and

Thayer 2005, PMI 2013) conforming to certain

quality expectations.

The phases of the project management lifecycle

include: project initiation, planning, execution,

monitoring and control and closing (Figure 1) (PMI

2013). In planning project managers define project

plans. While in monitoring and controlling they

track and regulate the progress and performance of a

project and identify project parts where changes

must be applied. Successful project completion

requires that managers continuously monitor and

control the execution and progress of the activities

with respect to the plan and adopt corrective actions

whenever necessary. Under such conditions it is

crucial that project performances be observed and

measured regularly to identify variances from the

project plan, comparing for example differences

between actual values (budget, resource

consumption, start finish dates) and planned ones.

This is especially true in software contexts where,

being human-centred it is difficult to predict factors

such as productivity and performances, and therefore

project duration and costs. Literature provides

several evidences of software project failure

(Marshal 2006, Pressman 2002, Standish Group

2010).

Figure 1: Project Management Lifecycle Processes (PMI

2013).

502

Teresa Baldassarre M., Boffoli N., Caivano D. and Visaggio G..

Tracking Project Progress with Earned Value Management Metrics - A Real Case.

DOI: 10.5220/0005470305020508

In Proceedings of the 17th International Conference on Enterprise Information Systems (ICEIS-2015), pages 502-508

ISBN: 978-989-758-097-0

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

Earned Value Management - EVM (PMI 2013)

is considered among the most reliable for objectively

tracking performance and progress of a project.

According to Efe and Demirors (2013) EVM is

defined as “Management with lights on”. Traces of

this technique date back to the 1800s before being

adopted in military domains such as NASA (2010)

and DoD (DoD 1998, DoD 2002, EVM 2000, PMI

2013) There are also several evidences of the

success of this technique for project monitoring and

control (Jaafari 1996, Raby 2000, Wells and Duffey

2003, Fleming and Koppelman 1998, Australia

2006, Sulaiaman 2006). There is little support in

literature on decision support tools that guide data

collection and interpretation as pointed out in other

studies as well (Nkasu and Leung 1997, Basili et al.

2002, Garcia et al. 2004, Donzelli 2006).

Given this gap, our intention in this paper is to

clarify the meaning of EVM indicators and provide

guidance for their interpretation. Our contribution is

therefore twofold:

 Conceptual Categories: we have organized the

EVM indicators in conceptual categories each

with a specific meaning and scope;

 Decision Model: we have provided a decision

model able to guide project managers in

interpreting EVM metric values and support

them in making the most appropriate decisions

during project execution.

The proposed solutions have been validated in a

real industrial case study. Within the study, the

conceptual classes and decision model have been

used to apply the EVM metrics and interpret their

values during monitoring and control activities of

the entire project, in order to support decision

making.

The rest of the paper is organized as follows: in

the next section we describe the conceptual classes

used to classify the EVM metrics, as well as the

decision model we propose for interpreting EVM

values. In section 3 we have presented how the

model has been used in a real industrial case study

where managers adopted the model for monitoring

project performances. Finally conclusions are drawn.

2 PROPOSAL: CONCEPTUAL

CATEGORIES AND DECISION

MODEL FOR EVM METRICS

Managing a project, independently from its

application domain, involves going through three

phases (Figure 2): (i) define work; (ii) schedule &

budget; (iii) measure performance. In “define

work”, project activities are identified and a work

breakdown structure (or similar) is developed in

order to identify the relations between the activities

and work products. This structure should be detailed

so the work can be categorized into individual

elements of work. Next, in the “schedule and

budget” phase, the project manager defines how the

WBS activities are organized; Scheduling also

involves arranging work packages into logical

frameworks that define the project milestones. As

the project progresses “monitoring and controlling”

processes are carried out to measure performances.

Figure 2: Project phases.

Literature offers several techniques such as

COCOMO II (Bohem 2000), Use Case Points

(Smith 2000) and Function Points (IFPUG 2004)

that use past project data to estimate size, effort and

cost. They do not allow to “monitor and control”

projects. Opposed to these are approaches such as:

GQM-QIP (Basili et al. 2002), PDCA (Tague 2004),

TQM (Mathews 2006), and EVM (PMI 2003). The

idea behind EVM is that it prevents rather than cures

by identifying and solving problems early, as soon

as they arise. It acts as an early alarm for signalling

trends and deviations from the original project plan,

so that a manager can promptly take action, make

corrections and get the project back on track, in line

with schedule and budget restrictions. It is important

that the technique be systematically applied

throughout the project in order to detect variances

when they are small and easy to correct, instead of

discovering unpleasant surprises at the end of the

project, when the situation is unrecoverable and the

project is bound to fail or be cancelled. EVM is

made up of several metrics that may generate

confusion for a project manager having to collect,

measure, analyse and interpret them during the

project lifecycle. To this end, we have proposed a

classification of the metrics and organized them in

conceptual categories.

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2.1 EVM Conceptual Categories

The categories identified reflect the general meaning

of the metrics and their application with respect to

project progress. The classification consists of five

categories:

2.1.1 Project Constraints

When defining the project plan the project manager

must take into account the project constraints such

as budget available, resources that can be assigned to

the project activities, and time restrictions. In this

sense, two relevant indicators that represent this

information are: (i) Budget At Completion (BAC),

expresses an initial estimation of budget allocated to

the project; (ii) Time At Completion (TAC),

expresses the initial estimation of time required to

complete all the project activities. Both these

indicators (Figure 3) are fixed and established when

the project plan is defined.

TAC

Startup

% Budget

Costs

(K€)

100%

50%

BAC

Time

BCWS

ACW P

BCWP

Figure 3: EVM metrics.

2.1.2 Basic Indicators

This category is made up of three metrics that

express the earned value of the project at a certain

point in time, generally in correspondence to a

milestone established in the project plan: (i)

Budgeted Cost of Work Scheduled (BCWS):

planned value, is the amount of money budgeted to

complete the scheduled work; (ii) Budgeted Cost of

Work Performed (BCWP): earned value, is the

budgeted cost of work that has actually been

performed in carrying out a scheduled task at a

certain time point, usually related to a milestone;

(iii) Actual Cost of Work Performed (ACWP): the

actual cost sustained for carrying out the project up

to a specific milestone.

2.1.3 Derived Indicators

This category comprises two metrics obtained from

the basic ones that express variances between

planned and actual values collected in the milestone

check points, in absolute values: (i) Cost Variance

(CV = BCWP-ACWP): expresses the difference

between the cost of the work performed in

accordance to the project plan carried out up to a

specific point in time (BCWP) and the actual cost

sustained; (ii) Schedule Variance (SV=BCWP-

BCWS): expresses the difference between the cost

of the work carried out up to a certain point in time

and the cost of work that should have been done

according to the project plan (BCWS).

2.1.4 Synthesis Indicators

These metrics express synthetic information in

percentages. Cost Performance Index (CPI) and

Schedule Performance Index (SPI) are indicators of

how closely accomplished work is on budget and

schedule: (i) Cost Performance Indicator (CPI =

BCWP/ACWP) shows the efficiency of the

utilization of the resources on the project. (ii)

Schedule Performance Indicator (SPI =

BCWP/BCWS) shows how the work is progressing

compared to the original schedule.

Both of these formulas begin with the Earned

Value (BCWP), which is the value of the work

already accomplished. SPI and CPI ratios help

managers evaluate the project at any point and make

changes.

A manager should first calculate these two

synthesis indicators to have an idea of the project

status and whether there is a deviation (positive or

negative) from the baseline and then go into detail

by considering the derived indicators (SV and CV),

which provide a quantitative (absolute value)

evaluation of the deviation.

2.1.5 Predictive Indicators

This category includes two metrics that express the

estimate at completion (EAC) which forecasts the

value of the project with respect to time and cost

when the project is complete. Studies show that

EACs based on CPI and SPI values tend to be

significantly higher and are also more accurate

(Christensen and Thayer 2001). We have adopted

the following formulas for calculating them: (i)

Estimate At Completion – Cost (EACC =

BAC/CPI): expresses the amount of money

estimated to be spent at the end of the project given

its progress; (ii) Estimate At Completion – Time

(EACT = TAC/SPI): estimates the end time of the

project given the current state of progress.

Keep in mind that although BAC and TAC are

fixed at the beginning of the project, the EAC values

most likely change compatibly and conformingly as

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the synthesis indicators change during project

execution.

2.2 Decision Model

The concept of granularity is very important in the

application of EVM and interpretation of the

collected values. In particular, SPI, CPI, SV and CV

measured at a project level (high granularity) are

useful for top management, portfolio/program

managers, but turn out to be almost insignificant for

a project manager who, without any other

information, is not able to make any considerations

or valuable interpretations. On the other hand, if the

indicators are calculated with respect to an

individual sub-project, phase, task (low granularity),

rather than the overall project, it is possible to:

monitor the actual state of the sub-project, phase,

task compared to the project plan; designate

budget/resources saved on an activity to mitigate

risks related to other late or over budget activities,

allowing to optimize project performances. The level

of granularity as well as milestone checkpoints, with

respect to which entity and how often EVM

indicators are to be collected, should be defined at

project start, taking into account the critical points

and risk factors and can be changed during

execution, if the case.

The resulting amount of data collected at each

milestone checkpoint during the entire project is

considerable. As so, its interpretation can become

quite challenging for a project manager and for the

entire management team involved in analysing the

data, identifying weaknesses, avoiding problems

from occurring and promptly acting when they arise.

For this reason, as practical support to the EVM

technique we have provided a decision model

(Figure 4) to use at each milestone checkpoint. The

model basically guides monitoring activities step by

step as collected values are reported in the form and

compared to baseline values. Secondly,

interpretation guidance is provided in order to

optimize project management by using/re-allocating

available resources at their best, verifying critical

points and mitigating delays or over budget risks.

Those who are responsible of managing work

use this data in order to understand cost and

schedule performances throughout the project

lifecycle. The main goal is to point out (cost and

schedule) issues early providing the maximum time

to minimize their impact and provide an effective

manner for developing recovery plans and

improvement actions where necessary.

Figure 4: Decision Model for interpreting EVM metrics.

3 APPLICATION TO A REAL

CASE

The conceptual categories classification and decision

model have been applied in an industrial case study

within a nationally funded project (here called E-

MARK for convenience) that involved a University

and a large IT company. The project focused on

designing and developing a solution able to

automate marketing processes through use of

technologies that make use of traceable information

on the Internet. Project monitoring and control was

carried out with EVM metrics. Project managers

used the proposed classification of conceptual

classes as reference to systematically collect and

organize the values during project execution.

Furthermore, they adopted the decision model

illustrated in the previous section to guide

interpretation of collected values. Throughout the

next paragraphs detail of the project monitoring

progress is provided.

The project was organized in four work packages

and nine activities. The granularity selected for

applying the indicators related to each activity at

fixed milestones.

In Figure 5 the planned effort and costs with

respect to each project activity are reported. They

are compared to the actual values collected during

the project. Furthermore, Figure 6 shows the values

of EVM indicators for every activity. In the

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following we report the results of the interpretations

carried out, after applying the decision model to the

EVM indicators collected.

WP ACTI

ITIES

PERSON/DAYS COST SOLAR DAYS

PLANNED

PERSON/DAYS COST SOLAR DAYS

ACTUAL

WP1

WP2

WP3

48 8.151,60€ 18

9,6 1.630,32€ 3,6

81,6 13.857,72€ 30,6

14,4 2.445,48€ 5,4

48 8.151,60€ 18

91,2 15.488,04€ 34,2

144 24.454,80€ 54

33,6 5.706,12€ 12,6

9,6 1.630,32€ 3,6

480 81.516,00€ 180

40,07 8.005,36€ 18

8,01 1.601,07€ 3,6

60,1 12.008,04€ 27

12,02 2.401,61€ 5,4

20,03 4.002,68€ 9

76,13 15.210,18€ 34,2

16,03 3.202,14€ 7,2

120,20625 24.016,08€ 54

12,02 2.401,61€ 5,4

28,05 5.603,75€ 12,6

4,01 800,54€ 1,8

400,68 80.053,60€ 180

Figure 5: descriptive statistics of planned and actual

values.

ACTIVITY

% of

progress

A1 10%

A2 12%

A3 27%

A4 30%

A5 35%

A6 54%

A6 58%

A7 88%

A7 91%

A8 98%

A8 99%

100%

BCWP BCWS SV ACWP CV SPI CPI EACC EACT

8.151,60 8.151,60 0,00 8.005,36 146,24 1,00 1,02 80.053,60 6,00

9.781,92 9.781,92 0,00 9.606,43 175,49 1,00 1,02 80.053,58 6,00

23.639,64 22.009,32 1.630,32 21.614,47 2.025,17 1,07 1,09 74.532,66 5,59

26.085,12 24.454,80 1.630,32 24.016,08 2.069,04 1,07 1,09 75.050,25 5,63

34.236,72 28.530,60 5.706,12 28.018,76 6.217,96 1,20 1,22 66.711,33 5,00

41.980,74 44.018,64 -2.037,90 43.228,94 -1.248,20 0,95 0,97 83.939,70 6,29

49.724,76 47.279,28 2.445,48 46.431,09 3.293,67 1,05 1,07 76.116,54 5,70

61.952,16 71.734,08 -9.781,92 70.447,17 -8.495,01 0,86 0,88 92.693,64 6,95

74.179,56 74.179,56 0,00 72.848,77 1.330,79 1,00 1,02 80.053,60 6,00

77.032,62 79.885,68 -2.853,06 78.452,52 -1.419,90 0,96 0,98 83.018,54 6,22

79.885,68 80.700,84 -815,16 79.253,06 632,62 0,99 1,01 80.870,47 6,06

81.516,00 81.516,00 0,00 80.053,60 1.462,40 1,00 1,02 80.053,60 6,00

EVM INDIC

LUES

Figure 6: EVM values for the entire project.

The TAC (initial estimation of project duration) is 6

months, while BAC (initial estimation of project

cost) is €81.516,00. The first activity (A1) required

18 solar days, according to the plan, and a total of 40

person/days (p/d) compared to 48 planned with a

lower cost. The EVM indicators for this activity

confirm this data. In A2, descriptive statistics show

that actual values are lower than planned ones. The

project was proceeding correctly and project

managers decided to designate the extra budget to

future activities. In A3, the activities were carried

out in less time with respect to planned (27 solar

days, and 60 p/d, compared to 30 solar days and 81

p/d planned). In accordance to the interpretation of

the decision model, project managers decided to

designate part of the budget not spent and the

resources assigned to this task. In A4 the trend of

EVM indicators confirms the results of the previous

phases as they satisfy the baseline values of the

decision model. As it appears from both the

descriptive statistics and the EVM values, A5 was

carried out with less effort and cost than planned.

In A6, when the milestone checkpoint was

carried out, the project was behind schedule and not

completed yet. At this point the EVM indicators

pointed out a situation over budget as more than

expected was being spent and project cost and effort

were higher than planned. As improvement action

managers decided to designate part of the resources

saved in the previous phases to the current one.

Consequently, staff that had terminated activities

early and had the required skills were assigned to

this activity. Also, part of the budget saved in the

previous phases was also shifted to this one. This

improvement action had positive effects, i.e. at the

next milestone checkpoint the EVM indicators had

returned within the baseline values. Having

recovered both budget and resources from previous

activities, the overall budget and effort for the

project were not impacted. Indeed, the EVM

indicators related to A6 are inline with the baseline

values. This was possible because manager decisions

in previous checkpoints were taken in order to

prevent difficulties in further activities. In A7

another delay occurred. After a period of 54 days,

the activity was not completed. The EVM indicators

confirm this situation for A7 (Figure 6 first row),

which are below the threshold values. Managers

reallocating resources from previous activities or

from activities that were ahead of schedule and

below budget and shifted them to A7. For what

concerns A8, after 12.6 days it was not completed

(Figure 5). A9 requested fewer resources in terms of

performances and cost to be carried out, and

consequently indicators SPI and CPI returned to

satisfy the baselines.

Having collected EVM values during milestones

with a granularity related to activities rather than

work packages or entire project, allowed the project

managers to appropriately monitor and control the

general trend of the performance indicators and

readily act to recuperate delays accumulated during

the project. Indeed, the resources saved in on-

schedule/budget activities were allocated on other

critical off-schedule/budget ones. As so, delays were

mitigated by improvement actions without impacting

on the overall final project cost and effort, which by

the end of the project turned out to be within the

expected thresholds. Deviations from the plan in

some activities were successfully recovered in other

ones by readily reallocating budget and effort to face

problematic situations pointed out during monitoring

checks. Having adopted a decision model to guide

the interpretation of indicators turned out to be

helpful as it simplified the entire monitoring and

control process as the project progressed in time.

4 DISCUSSION AND

CONCLUSIONS

Earned Value Management technique is easy to

understand and apply. Nonetheless, there are several

critical factors that any manager should keep in

mind: collecting cost values at a low level of

granularity requires an advanced level of

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management control, as costs must be broken down

conformingly to the level of detail chosen;

determining the percentage of completion of an

activity requires “structured processes” and careful

evaluations.

EVM allows to achieve an objective evaluation

of risk and project status and, at the same time,

provides useful indicators that allow to change

management strategies, increasing or decreasing

resources assigned to activities based on

performances, in order to improve and optimize the

general progress of the project in terms of cost and

time.

Tracking earned value is of little value if the

estimating and analysis capability that it provides is

not used to operatively manage the project as it

progresses. Furthermore, reporting real project status

systematically, at regular intervals provides an

opportunity to serve as early alarm and address

potential problems readily, before it is too late and

avoid cost overrun and schedule slippage. For this

reason it is important that project managers adopt

this approach and use the decision model for

conducting project monitoring and interpreting the

indicators collected in specific milestones and

granularity entities, fixed at the beginning of the

project, in order to prevent problems from occurring

and promptly act when they arise.

EVM is not the silver bullet for project

monitoring and control, however it surely provides a

higher level of control on the project execution.

Applying our proposal to a real case has pointed out

how the classification in conceptual categories sheds

light on the multitude of EVM metrics that a

manager must handle during project monitoring

activities. Also, the decision model supported

managers in decision-making during the entire

project. This technique, given its features is more

appropriate for medium to large structured contexts

rather than small and agile ones.

We are currently refining the decision model so

it can be better tailored to any task, activity, phase,

of a project and therefore be adapted to any desired

level of granularity according to the project needs. It

is also being implemented in a decision support

system tool, as the model has been formalized in

decision tables. This solution will provide automated

support to project managers allowing them to

monitor and control EVM values with less effort.

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