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© 2012 Computer Sciences Corporation. All rights reserved.

LEADING EDGE FORUM | CSC PAPERS 2012

EARNED SCHEDULE: FROM EMERGING PRACTICE TO PRACTICAL APPLICATION

ABSTRACT

As technology expands at an ever increasing pace we are challenged to efficiently apply and utilize it

in ever shorter cycles of new products, processes, and methods, with the ultimate goal of achieving

business objectives. Accurate project performance metrics provide not only vital data for project

managers but also for business executives charged with managing complex portfolios of projects The

ability to forecast conditions at project completion and to develop corrective actions facilitates the

proper balance of schedule and budget priorities for the project manager and assists business

leaders in achieving the optimum balance of risk, return, and value.

All projects regardless of their industry or nature inevitably are faced with two fundamental questions:

are we on budget, and are we on time? As technology and project complexity increased in the 1950’s

it became apparent that traditional methods of measuring project progress were not working,

particularly on large defense projects. The United States Department of Defense’s efforts led to the

birth of Earned Value Management, EVM, to measure and forecast project cost and schedule

performance by using ratios of work value, work accomplished, and cost of work. While time has

shown that EVM’s budget metrics are very reliable, the same can’t be said for corresponding

schedule metrics. Earned Schedule, an extension to Earned Value Management, is an emerging

practice in project management that offers significant improvement in monitoring and forecasting

project schedule performance.

Earned Schedule offers methods that are straightforward yet powerful while simplifying reporting and

communication across all levels of an organization. Why do project managers at times say “the

project is x dollars behind schedule?” The answer is simply because Earned Value techniques

calculate time in terms of money. Earned Schedule provides schedule quantities directly in units of

time. Why do some projects reach 99% complete and remain there forever? The answer is that

Earned Value schedule measurements have limitations in measuring late projects and projects that

are past their planned end dates, with subtleties that aren’t always well understood by project

managers. Earned Schedule provides improved accuracy over the project’s entire duration, while

overcoming a major limitation of EVM by providing data for projects that have exceeded their planned

end date.

This paper’s target audience is the intermediate to advanced level project management practitioner,

although the introduction to Earned Value Management and Earned Schedule is very useful for a

more general audience. The paper provides background information on Earned Value Management

(EVM) and introduces the Earned Schedule (ES) method. The two methods are compared with both

theoretical and real world project data, demonstrating that Earned Schedule provides significant

management benefits over EVM. Cautions and caveats for the project management practitioner for

both methods are discussed. Improvements offered by Earned Schedule are shown with a

retrospective analysis of an actual project. Equations and examples for enhanced project forecasting

and recovery are given with example applications.

1 INTRODUCTION

Earned Schedule (ES), an extension to Earned Value Management (EVM), is an emerging practice in

project management that offers significant improvement in monitoring and forecasting project

schedule performance. Among the benefits of Earned Schedule are the expressions of schedule

variances directly in units of time, improved metrics accuracy in the latter periods of project

Author

Bill Mowery MPM, PMP

Keywords

Earned Value Management,

Earned Schedule, Project

Management, Cost

Performance Index, Schedule

Performance Index, Project

Recovery, Earned Duration,

Project Forecasting,

Cost/Schedule Control System

Criteria


© 2012 Computer Sciences Corporation. All rights reserved. 2

performance, and meaningful project metrics for the entire period of project performance. Earned

Schedule overcomes a major limitation of EVM analysis by providing meaningful metrics for late

projects, i.e. projects that have exceeded their planned end date.

The paper presents as background for the discussion of Earned Schedule a brief review of the

evolution and use of Earned Value Management (EVM) and some of its limitations in measuring and

predicting project schedule performance. The paper next provides an introduction to Earned

Schedule theory and application, with example calculations and a comparison with EVM results for

both example and actual project data, followed by a discussion of some challenges and issues with

Earned Value methods that affect both traditional EVM and ES. A section on the integration of

Earned Schedule methods with existing CSC project management tools demonstrates how to derive

immediate benefit in a practical application of the new methods. The paper concludes with

conclusions and directions for further research and application of Earned Schedule.

2 PROJECT SCHEDULE PERFORMANCE METRICS AND FORECASTING

2.1 EARNED VALUE MANAGEMENT OVERVIEW

Project management emphasizes management of the classic “triple constraints” of scope, budget,

and schedule. Budget and schedule management focus on monitoring status and forecasting future

performance against an approved project plan. As projects in the defense industry in the 1950s

increased in technical complexity and size, measuring progress using simple ratios of money and

time expended versus money and time planned without considering the actual amount of project work

accomplished resulted in some significant overruns of budget and schedule. The demand for more

sophisticated management tools led to the Program Evaluation and Review Technique (PERT) in the

defense industry and its related Critical Path Method (CPM) in the construction industry. With a focus

on analysis of resource loaded networks, PERT and CPM proved to be important early methods to

address management reporting concerns and provided the earliest methods of Earned Value

Management. While providing a significant improvement, the challenges in the complexity of the

PERT method before computerized tools were generally available, attempts to make one set of tools

fit all project types, and administrative challenges within the government resulted in limited industry

adoption of these tools. Building on experience gained from PERT, the United States Air Force

formed the Cost / Schedule Planning and Control Specifications group in 1963. Shifting focus from

requiring contractors to adopt a single method to requiring adherence to specific criteria led to the

development of the Cost/Schedule Control System Criteria (C/SCSC) in 1967 (Fleming, 1988). In

addition to establishing a new collaborative approach between the defense department and private

industry, C/SCSC was a major factor in the beginning of the widespread application of EVM (Abba,

2000). There are many excellent references on Earned Value Management available (Fleming &

Koppelman, 2000) so only a brief overview of EVM concepts is presented in this paper.

In discussing EVM topics this paper uses the current terminology found in the latest version of the

Project Management Institute’s A Guide to the Project Management Body of Knowledge (Project

Management Institute, 2008). Readers may be more familiar with older versions of terminology that

are still commonly in use. Some of the terms where differences exist include:

Budgeted Cost of Work Scheduled (BCWS) / Planned Value (PV) – the amount of work planned to

be accomplished at a point in time or cumulatively.

Actual Cost of Work Performed (ACWP) / Actual Cost (AC) – the amount of money or resources

expended in order to accomplish the amount of work achieved for the reporting period.

Budgeted Cost of Work Performed (BCWP) / Earned Value (EV) – the amount of planned work

actually accomplished or achieved, or the value of work earned.

Earned Value Management establishes a time-phased Performance Measurement Baseline (PMB)

for assessing the progress of project objectives and compares the resources expended at a point in



time to the actual amount of work accomplished. While measurement may be defined in units

appropriate for a particular project, the PMB is usually expressed in terms of money spent. When

considering EVM techniques it is important to maintain the distinction between the value of work and

the cost of work. Many projects express the value of work in terms of money, and of course, cost is

likewise expressed in the same terms. But while it is logical to express cost as money, expressing the

deliverable of a project in terms of money is in reality an indirect reference, since the actual project

deliverable is a good or service, and that good or service tendered is in fact the objective of the

project. In project planning there is a logical reason to consider the cost and value synonymous, but

once the project is under way and attention turns to monitoring progress the difference between cost

and value should be quickly apparent.

Consider a hypothetical project with the objective of installing 18,000 feet of fiber optic cable over 18

months (Planned Duration) at a total cost (Budget at Completion) of $231,280. Table 1 shows the

Performance Measurement Baseline for periodic and cumulative values for the example.

Table 1. Example Performance Measurement Baseline

Now let’s determine the project’s status after 12 months. Suppose that 12,326 feet of cable are

installed at a total cost of $179,820. Earned Value is calculated as the ratio of work accomplished to

total work planned:

We also find that we have spent $179,820 (Actual Cost). Considering only cost alone would lead us

to believe that the project is $179,820 / $231,280 = 77.75% complete without considering that at the

current spending rate using the entire budget would not allow installation of all cable planned, but

only

ft.

Looking strictly at work implies that the project is 12,326 / 18,000 = 68.48% complete without

recognizing the unpleasant truth that installing the planned 18,000 feet of cable at current actual rates

is likely to cost

or $31,316 over budget. The integration of cost and schedule information relative to actual work

accomplished is a major advantage of EVM over previous methods that considered the factors

separately.

As a project progresses EVM analysis compares the relationship of Planned Value (PV), Earned

Value (EV), and Actual Cost (AC) over time as shown in Figure 1. The Planned Value curve as

defined by the PMB terminates at Budget at Completion (BAC), the project’s total planned value. In

understanding the limitations and mechanisms of EVM schedule metrics it is important to keep in

mind that project completion is defined as EV = PV = BAC.



Figure 1. Earned Value Performance Curves and Equations1

The Actual Cost and Earned Value curves are plotted based on actual measurement of project

performance, and using these values, we begin to develop metrics for analysis and reporting. We find

Cost Variance from Equation 1, with a positive result indicating under budget performance

Equation 1. Earned Value Cost Variance

and we find Schedule Variance similarly by

Equation 2. Earned Value Schedule Variance

EVM uses performance indices, ratios of actual and planned values, to describe efficiency of

schedule and budget achieved and to forecast future performance. The most common of these

indices are the Cost Performance Index (CPI), calculated by Equation 3.

Equation 3. Earned Value Cost Performance Index

and the Schedule Performance Index, shown in Equation 4.

Equation 4. Earned Value Schedule Performance Index

A performance index value greater than one indicates better than planned actual performance and a

value less than one indicates less than planned performance. Using these performance indices, next

1 Figure 1 was updated July 2012.



our attention turns to the questions of how much the project is likely to cost, what the total variance

will be, and how long it will take to complete the project. These questions are answered by the

Estimate at Completion

Equation 5. Estimate at Completion

the Variance at Completion

Equation 6. Variance at Completion

and the Estimate at Completion for Time

Equation 7. Variance at Completion, Time

Returning to the fiber optic cable installation project, we can see the results of the calculations for

each of these equations applied to performance data in Table 2. The EVM equations offer

calculations to answer each of the preceding questions in a simple and straightforward manner.

Table 2. Example Project Actual Performance

While performance indices may be used in various advanced calculations, the CPI value of 0.88 in

Table 2 indicates that each dollar spent is producing only 88 cents’ worth of work, and the SPI value

of 0.94 implies that each eight-hour day worked results in only 7.5 hours of effective work. What is the

long-term effect of this less-than-optimal performance on project completion? VAC forecasts that the

project will finish $31,310 over budget and EACt predicts finishing late by 1.1 months.

A metric related to CPI, the To Complete Performance Index (TCPI), defines the level of performance

required to meet an objective and is calculated by

with the p subscript indicating that the equation is for baseline plan analysis. In simple terms, what

level of efficiency (CPI) must be reached in order to achieve the plan? In addition to defining a target

performance level, TCPI has interesting characteristics of its own (Lipke, 2009c). If TCPI is less than



1.0 then the forecast budget is considered achievable. If TCPI is greater than 1.10 then performance

is most likely beyond recovery, and detailed reevaluation of the plan is probably in order. Intermediate

values require the project manager’s increased attention. For the example fiber optic project, Table 2

shows TCPIp is already at 1.42, indicating that it’s highly unlikely the project will finish within its

baseline budget with current performance levels.

We have calculated EAC = $262,590, or $31,310 over budget, but is it probable that the project can

complete in less than this latest forecast? Given current performance levels, is it likely we can

complete the project for $10,000 less? To answer the question we use an alternate form of TCPI

based on EAC:

While this represents a significant improvement in current performance levels, TCPIe indicates that

the reduced budget is at least a reasonable, if still difficult, goal.

2.2 EARNED VALUE ISSUES AND CONCERNS

Although Earned Value Management represents a significant contribution to project management,

from its earliest development and adoption limitations were apparent (Fleming, 1988). Experience

and further research have shown that while EVM methods for cost are reliable (Fleming &

Koppelman, 2003), schedule metrics are not as robust. Among the problems are schedule metrics

expressed monetary terms, the lack of reliable metrics for projects whose duration has exceeded the

baseline end date, and the unreliability of EVM schedule calculations in the latter stages of a project.

While the logical measurement of schedule parameters should be in units of time, EVM expresses

schedule variances in terms of money. This anomaly is due to the nature of the original composite

view of schedule and budget as shown in Figure 1. The Performance Measurement Baseline (PMB)

is represented by PV, terminating with the total planned budget, Budget at Completion (BAC). SV is

the difference between points on the EV and PV curves at a given time, and since this difference is

taken from the vertical axis, which is in dollars, SV is likewise expressed in dollars. It logically follows

that EVM schedule ratios are also computed using dollars (e.g. Schedule Variance percentage, SV%

= SV / PV, Schedule Performance Index, SPI = EV / PV). It is easy to see how those not well versed

in EVM may not fully appreciate the significance of the phrase “The project is x dollars behind

schedule.” Such a statement typically results in further questions, such as “How many days late will

the project be?” Intuitively and logically, this is an appropriate question, since we think of schedules in

terms of time.

The answer to the question of how long is answered with ratios of money. As shown in the overview

of EVM, converting money to a forecast of project duration uses the equation

While the form of the equation above is the version given by the Project Management Institute

(Project Management Institute, 2005) it’s easy to see that the equation is simplified as



Figure 2 – Earned Value Project Completion

which seems to be most commonly used. It is worthwhile to note that while the general form of the

equation implies the use of EVM’s SPI, we can substitute alternate or more complex / compound

performance indices in this equation. While this ratio does provide a time based measure it is still

limited by its dependence on SPI, a performance measure based on money and bound by the

relationship of EV, PV, and BAC.

We can see EVM’s inability to provide meaningful schedule metrics for projects that have exceeded

their planned duration by examining the schedule equations and by practical example. In analyzing

the equation for EACt above, recall that PD and BAC are fixed quantities defined during project

planning, while SPI = EV/PV. At a project’s baseline completion date PV = BAC as illustrated in

Figure2. Likewise at project completion EV = BAC, and we begin to infer that there are limits on how

project duration may be forecasted based on the relationship of SPI and BAC. We can analyze the

equation using calculus to determine

or by looking at the simplified version of the equation

we see that when SPI = 1 then EACt = PD. When a project’s baseline end date has passed (PV =

BAC) and the value of EV is approaching BAC, the forecasted duration begins to decrease, and

eventually reaches its limit of PD once all work is complete (EV=PV=BAC) as illustrated in Figure 3.



Figure 3 - Earned Value Performance for Late Projects

While the preceding discussion addresses the values that EACt can have, it does not consider when

those values are calculated. Let’s examine a scenario for a late project. Assume a project has

completed most of its planned work, with EV = 990, PV = 1,000, BAC = 1,000, and PD = 12. These

metrics yield SPI = EV/PV = 0.99, which when applied in

EACt=

BACSPIBACPD

gives a final duration of 12.12 months. Figure 2 shows that as EV approaches the final value for PV,

forecasted project duration decreases because the limit for EV is always BAC. Regardless of whether

we perform the calculation during performance period ten or fifteen, this cost-based ratio yields the

same result, and can show that an in-progress project has a forecasted completion date in the past.

Let’s examine this inconsistency by returning to our struggling fiber optic cable installation project.

The performance data in Table 3 includes EVM metrics and calculations for schedule and budget

through month 22, indicating that the project has now completed four months after its originally

planned 18-month duration. Cost and schedule variance data are graphed in Figure 3. Note that in

month 18 schedule variance begins to improve as EV starts to converge on PV and BAC. Likewise,

forecasted duration (EACt) begins to improve from month 18 forward and indicates in month 21 that

the forecasted end of the project, at 18.32 months, should have been reached 2.68 months ago.

The preceding analysis shows that for a late project EVM schedule data is unreliable, which leads to

the conclusion that if the metrics are in error for a project that is late then the tendency toward

inaccuracy must begin at some point before the completion date.



Table 3. Late Project EVM Metrics Example

At what point, and to what degree, does this inaccuracy affect EVM metrics? Although the nature of

the problem makes determining the period and magnitude of the potential error difficult, we can see

the existence of inaccuracies in Figure 4.

Figure 4. SV / SPI Divergence for a Late Project

Typically, we display SPI with only two significant digits, but Figure 4 compares SPI with five

significant digits to a corresponding value of SV. Note that in month 14 an interesting trend begins as

Schedule Variance, in absolute terms, continues to show worsening performance while SPI begins to

show improving performance as it converges to PV/BAC. This contradiction clearly indicates that

EVM is providing inconsistent data beginning in month 14 of the project.

2.3 INTRODUCTION TO EARNED SCHEDULE

2.3.1 EARNED SCHEDULE CONCEPTS AND CALCULATIONS

The problems with Earned Value schedule metrics contributes to the tendency for practitioners to

focus on the cost management tools provided by EVM while acknowledging the limitations of EVM’s

schedule components. The search for better schedule performance metrics led to the development of

the Earned Schedule method. Based on Earned Value data, the goal of Earned Schedule is to have

“a set of schedule indicators which perform correctly over the entire period of project performance”

(Lipke, 2009a). Walter Lipke provided a formalized description of Earned Schedule and calculations

for its use in 2003 (Lipke, 2003) while noting that time-based analysis of the Performance



Measurement Baseline is not a new idea, citing Fleming’s foundation book on Earned Value

(Fleming, 1988).

By utilizing existing Earned Value data for time based measurements, Earned Schedule exhibits

consistent performance shown to deliver better results than previous EVM techniques in both a

retrospective analysis of a small set of projects (Henderson, 2003) as well as in a study of a broad

range of project conditions using computer generated schedule networks (Vanhoucke &

Vandevoorde, 2007). In their extensive simulation study Vanhoucke and Vandevoorde compared

Earned Schedule’s performance to Anbari’s Planned Value method (Anbari, 2003) and Jacob and

Kane’s Earned Duration method (Jacob, 2003) and found that on average Earned Schedule

outperforms either method.

The concept of Earned Schedule is relatively simple: derive a time based measurement of schedule

performance by comparing a project’s Earned Value today (Actual Time, AT) to the point on the

Performance Measurement Baseline (Planned Value curve, PV) where it should have been earned.

The difference between AT and PV represents a true time-based Schedule Variance, or in Earned

Schedule notation, SV (t). The derivation of Earned Schedule metrics is shown in Figure 5.

Figure 5. Earned Schedule, Adapted from Lipke (Lipke, 2003)

In Figure 5 at the end of July (Actual Time, or AT = 7) measured EV actually should have been

earned at some point in June, Period 6, seen by mapping Earned Value to its corresponding Planned

Value point on the Performance Measurement Baseline. In other words, the portion of the schedule

that is actually “Earned Schedule” consists of the work performed through all of the last full period

(May, period 5) and a portion of June. Earned Schedule is calculated by the formula



where C is the number of time increments on the PMB where EV ≥ PV, PVc is the value of PV at the

last full performance period, and PVc+1 is the value of PV at the end of the partial performance period.

Thus, Earned Schedule (ES) in Figure 5 becomes:

Note that an interpolation is required to determine ES for partial time periods, represented by the ratio

of the Earned Value to the Work Scheduled. It is an easily overlooked point that this ratio is implicitly

multiplied by one time unit and is therefore is in units of time and not a simple ratio. This fact has

resulted in some differing opinions in existing literature in interpretation of the algebra underlying

Earned Schedule (Book, 2006a). Once ES is calculated, Schedule Variance, SV(t), is found by the

equation

and expressed directly in units of time. A Schedule Performance Index based on time, SPI(t), is found

by

which is analogous to its EVM counterpart.

Returning to the example in Table 2 on page 8 we see that in month 12 work with a value of

$168,030 should have been accomplished per plan but only $158,380 worth of work is complete.

Let’s use Earned Schedule to answer the common questions of “What is the schedule status and

when will we finish?” Using the general form equation for ES

we find actual progress as

and applying the ES equation for Schedule Variance we find that the project is behind schedule by

Although calculation of project duration can take different forms depending on the anticipated future

performance of project work (Anbari, 2003), (Vanhoucke & Vandevoorde, 2007) most depend on a

performance index. For Earned Schedule in this example we find a performance index by

Applying this performance factor with the ES equation for Independent Estimate at Completion for

Time (IEAC(t)), the total forecasted project duration is

Another metric easily calculated with ES is the To Complete Schedule Performance Index, TSPI.

TSPI corresponds to EVM’s cost To Complete Performance Index, TCPI, and is used to determine

efficiency required to complete according to either a plan or desired target duration. Like TCPI, if < 1



the target is considered achievable, if > 1.10 it is generally believed that the schedule estimate is not

achievable, and of course intermediate values would bear close scrutiny (Lipke, 2009a). The equation

to determine TSPI for the planned duration is

Calculating TSPIP for the example project yields

And so it would seem that our example project is facing serious challenges in completing within its 18

month planned duration. The alternate form of the equation given by

where ED is the Estimated Duration in question, is used to evaluate the probability of completing a

project within an alternate planned duration. Examining the viability of completing the project with an

additional month added to the schedule we find

This implies an achievable goal, since TSPIe, the minimum performance level required, is less than

the current value of 0.95.

Comparing schedule status derived from EVM and ES for the example provides the data shown

Table 4.

Metric Earned Value Earned Schedule

Schedule Variance (SV) -$9,650 -0.57 Months

Schedule Performance Index (SPI) 0.94 0.95

Forecast Duration (IEAC) 19.10 Months 18.89 Months

Table 4. Comparison of Earned Value and Earned Schedule Metrics

The elegance of Earned Schedule is its calculations based on Actual Time, and unlike Earned Value,

will yield a valid result regardless of whether the project has exceeded its planned duration. An

additional aspect of ES is that it does not suffer inaccuracies in the latter stages of a project, and in

fact it has been demonstrated that as a project progresses the accuracy of ES forecasts improves

(Vanhoucke & Vandevoorde, 2007). For comparison with EVM performance, we first revisit the final

months of our sample project with Earned Schedule metrics included as shown in Table 5.



Table 5. Metrics Comparison for Late Project

Unlike EVM’s SPI and derived metrics, Earned Schedule’s SPI(t) portrays a steadily declining

performance trend from month 12 until project completion in month 22.

2.3.2 EARNED SCHEDULE CRITIQUES AND CONCERNS

While Earned Schedule methods offer promising advances project management, as with any new

technique caution is required. Considerations for the potential practitioner include apparent slow

industry adoption, viewpoints on alternative methods, and the general caveats pertaining to Earned

Value methods in general.

Deemed an “emerging practice” by the Project Management Institute (Project Management Institute,

2005), Earned Schedule is a relatively new method that is not yet supported by the broad base of

research and practical use that EVM enjoys. Despite the promising indications provided by existing

data (Henderson, 2003), (Henderson, 2005), (Vanhoucke & Vandevoorde, 2007) continuing research,

particularly with real world project data, will be required to establish Earned Schedule comfortably

among proven project management techniques. Just as it has supporters, Earned Schedule also has

its critics. In one of the better known critiques, Book (Book, 2006a), (Book, 2006b) offers some minor

criticisms of Earned Schedule calculation that are overcome with a bit of further analysis (Lipke,

2006), but more importantly offers a general observation on Earned Value analysis. Book observes

that when analyzing aggregate EVM data for a project individual tasks may be early or late, but

cumulative values will not indicate a behind schedule condition. While Lipke’s rebuttal to Book’s

objections (Lipke, 2006) resolves these questions the issues raised emphasize the importance of fully

understanding the state of the art in Earned Schedule. As with any method, the practitioner would be

well advised to evaluate Earned Schedule’s applicability in a particular project environment.

Earned Schedule is not the only proposed improvement to EVM methods. Jacob’s Earned Duration

method (Jacob, 2006) also presents an alternative to EVM schedule methods. Figure 6 shows

Earned Duration calculated from the Performance Measurement Baseline.



Figure 6. Graphic Representation of the Earned Duration Method (Jacob, 2006)

There is considerable similarity between Earned Schedule and Earned Duration. Both methods rely

on creating a “measurement yardstick” by projecting current Earned Value to a point on the PMB

where that amount of Earned Value should occur. The difference between the methods is that while

Earned Schedule calculates variance from the present period to the yardstick, Earned Duration

measures from the beginning of the project to the “yardstick” and the present performance period to

calculate variances. Computer simulation has shown that Earned Duration provides valid data, but is

not as robust as the Earned Schedule method for all project conditions (Vanhoucke & Vandevoorde,

2007). The Earned Duration method should not be dismissed, but evaluated in the context of a

particular project environment and the data available for analysis.

In evaluating the application of Earned Schedule, it is important to remember that calculations and

metrics are derived from basic Earned Value data, and therefore results are subject to some of the

same considerations and dependencies as any EVM method. Section 2.4 discusses some common

cautions and considerations with Earned Value methods.

2.4 EVM AND ES: CAVEATS AND COMMENTS

2.4.1 BASELINE DEFINITION AND CONTROL

Regardless of the tracking and forecasting method selected, an accurate basis for measurement and

comparison is required. Earned Value Management’s Performance Measurement Baseline provides

the intersection of two essential components, Planned Value and the period of performance.

Planned Value at the task level is considered the measure of work to be accomplished. While most

often expressed in monetary terms such as dollars, measures may be established for other units of

work as appropriate to the task or project. The units of work must reflect and directly translate to the

work defined for the task. For example, a project to wire a facility with network access might include a

task to install 1,000 network jacks, at a cost of $80 per jack for a total cost of $80,000. While we can

translate installation of jacks to cost terms, it is the installation of the jacks, not the expenditure of the

money that determines the Earned Value for the task. If we find the actual cost of installing a jack is

$90 each after installing 250 jacks for a total expenditure of $22,500, the budget expended is %28.1

of plan, while the measure of target work, the number of jacks installed, is only %25 complete.



The planned value of a task is scheduled for a period of performance with assigned start and end

dates within the project network and project schedule, contributing to the overall PMB. Continuing

with the network installation example in the previous paragraph, the installation of 1,000 network

jacks might be evenly distributed across four workweeks to plan for installation of 250 per week, or 50

per day for a five-day workweek. Practical circumstances may not allow updating the task’s Earned

Value on a daily basis, but work accomplished may be measured at a very detailed level for any

period of performance desired. The task could also be resource dependent, and planned for

installation of 300 jacks in each of the first two weeks and the installation of 200 jacks in each of the

remaining weeks. Again, total Planned Value will be accomplished in the required overall

performance period but at a different point in time. Ensuring the proper understanding and definition

of both time and work in the Performance Measurement Baseline are essential for reliable schedule

metrics. Not all project types allow straightforward measurement of Earned Value. For example,

software development projects can prove notoriously difficult to measure in the software coding and

testing phases, requiring care in defining and measuring actual work accomplished.

In misplaced enthusiasm for Earned Value techniques project work can be inappropriately structured

in ways that can distort the view of true project performance. Creation of actual value, as viewed by

the customer of the project, is through the realization of the project’s product. While administrative,

overhead, and project management functions certainly contribute to project delivery they are ancillary

items that should not affect proper assessment of true project deliverables. Proper classification of

Level of Effort and Apportioned type tasks (Project Management Institute, 2005) should be used to

provide the proper structure of a project’s work.

2.4.2 MEASUREMENT OF EARNED VALUE

Once the project is underway progress toward objectives, the Earned Value, must be measured. As

noted in the example in Section 3.4.1 care is required in defining and measuring project work versus

cost. In the installation of network jacks in that example, it is clear to see the implications of

measuring progress strictly in terms of cost versus work accomplished, and the importance of

accurate EV measurement and reporting cannot be overemphasized. Perhaps the easiest problems

to avoid are those created by the lack of understanding of Earned Value Management fundamentals

among project team members. Careful planning and a pristine measurement baseline are all for

naught if project team members do not understand and appreciate the fundamentals of progress

reporting. Simply asking team members “what percent complete is task x” without an appreciation for

how such data is used is an almost certain path to failure. Team members do not have to be Earned

Value Management experts, but should understand Earned Value, remaining work, and actual work

in order to provide sufficient information to fulfill the project management plan’s requirements for

reporting. Every project should include team familiarization with reporting requirements as part of its

startup plan, and EVM measurement techniques should be clearly and unambiguously defined in the

project’s governance plan.

Without proper familiarization and training, team members are subject to multiple reporting errors and

among these is the “work equals progress” syndrome. In its simplest form the team member’s logic

runs thus: “My new project task is allocated 200 total hours, I was supposed to work on it for 20 hours

this week, and I did . . . therefore my task is 10% complete.” This variety of reporting indicates perfect

performance until either the task is abruptly completed ahead of schedule or until the task is late and

the team member attempts to report 110% complete.

2.4.3 NETWORK TOPOLOGY AND SCHEDULE PREDICTION RELIABILITY

In their simulation study on the reliability of schedule prediction metrics Vanhoucke and Vandevoorde

(Vanhoucke & Vandevoorde, 2007) found that the structure of the project schedule network affects

forecast accuracy. The study shows that as a project becomes more “serial,” or has fewer parallel

tasks, the better schedule prediction metrics perform. Intuitively it easy to appreciate that a project



completed a single step or task at a time in sequence is easier to analyze than a project with multiple

tasks in progress simultaneously. As a simple example of the potential effects of network topology,

consider Figure 7 and project critical path. A project’s critical path is the longest sequence of steps

through a project’s tasks, and thus determines the overall project duration. The path is considered

“critical” since any delay of the component tasks of the critical path results in a corresponding delay in

project completion. The six task Gantt chart in Figure 7 shows Tasks A & B as critical path tasks,

while Tasks C – G are sets of parallel non-critical tasks. Alternative points of view suggest that the

project is not behind schedule if the non-critical tasks are delayed but the critical path is not affected.

While EVM metrics would indicate that the project is falling behind schedule if the Planned Value for

tasks C – F is not accomplished at the planned time, nonetheless the project can finish on its

baseline end date and therefore no real overall negative schedule variance exists as long as the

critical path remains the same.

Figure 7. Simple Network Topology

The question of when a non-critical path task becomes a problem is an issue. If Tasks D through F

are sufficiently delayed they could replace Task B as critical path tasks. This scenario has led to

debate around the proper way to apply any EVM-based methods, from recommendations that EVM

be applied at the discrete task level (Jacob, 2006) to the application of metrics to a subset of the PMB

(Lipke, 2006). In either case, it is important to have an understanding of this area of concern, as well

as to understand the measurement limitations in each project design.

2.4.4 AGGREGATE EARNED VALUE AND CRITICAL PATH DETECTION

Another problem in accurate EVM schedule prediction concerns tasks completed ahead of schedule

or out of sequence. Tasks completed ahead of schedule add Earned Value to the project’s total,

precluding detection of lagging critical path tasks since schedule performance is calculated based on

aggregate Earned Value without regard to which tasks contribute that value (Vanhoucke &

Vandevoorde, 2007). Tasks completed ahead of schedule but out of sequence are possibly

completed without complete data provided by predecessor tasks and have greater potential for future

rework (Lipke, 2009b). A relatively new technique called Schedule Adherence (Lipke, 2009b), (Lipke,

2011) offers methods to address these issues.

3 EARNED SCHEDULE IN THE REAL WORLD

3.1 OVERVIEW

The introduction to Earned Schedule has shown the potential for significant improvement in schedule

monitoring and forecasting in its extension to Earned Value Management. This section presents the

retrospective application of Earned Schedule metrics to a real world completed project in order to

demonstrate some of the practical applications of Earned Schedule. Of particular interest is the

application of Earned Schedule to a project selected in a pseudo-random manner from a large

portfolio of actual projects. While simulated data or carefully managed and controlled projects can

provide “pure” data for evaluation, it is often the case in real project environments that variations in

data collection and recording, accuracy of schedule metrics and maintenance, and the experience

level of staff involved, among other factors, can result in less than textbook perfect analysis. A



measure of the utility of any method is how well it can perform in less than ideal conditions, and how

it applies to “real world” situations. The following example compares Earned Schedule to the Earned

Value metrics as actually recorded and used to manage the example project.

3.2 PROJECT DESCRIPTION

The example project’s objective was to provide user interface enhancements for a commercial

software product. Project metrics were collected weekly via a proprietary system used in conjunction

with Microsoft Project©. Actual labor hours were collected from an employee time accounting system,

transferred directly to the Microsoft Project plan, and translated to Earned Value with an established

algorithm. The project plan had an original baseline duration of 73.6 weeks and 14,757 labor hours.

To coordinate with other projects competing for testing infrastructure and resources, during week 40

of project execution the baseline duration was reduced to 55.3 weeks without a reduction in planned

effort. Typical projects in the performing organization employ a Project Control Officer (PCO) who

works with the project manager to collect, analyze, and report on project metrics. The PCO was

interviewed to gain insight into the basis of the decision to reduce project duration. The PCO related

that the decision to reduce duration was based on analysis of Earned Value schedule metrics and the

consensus of the project team that the shorter schedule was achievable.

3.3 PROJECT DATA ANALYSIS

Figure 8 shows schedule metrics through week 40 / 51.9% complete, the point at which the decision

to reduce the project’s duration was made. The graph depicts SPI and SPI(t) schedule performance

indices as well as the schedule variance in days calculated with each metric in addition to the To

Complete Schedule Performance Index for the planned duration (TSPI(p)). Note that in week 40 SPI

has a value of 1.18, producing a forecasted 55 days ahead of schedule. While showing very good

project performance, Earned Schedule metrics do not present a picture as favorable, with SPI(t) =

1.08 and forecasting 16.09 days ahead of schedule. The TSPI(p) = .90 metric indicates a high degree

of confidence in an on time finish for the original baseline duration when compared to the SPI(t)

current value of 1.08. While both metrics show good project performance to date, the trend of the

data from week 31 reveals cause for concern. While SPI is trending positive despite the range of

variation displayed, SPI(t) declines steadily over the 9-week period from week 31 to week 40,

indicating declining project performance. Recall from the overview of Earned Schedule that the TSPI

equation can be used to evaluate the likelihood of completion within estimated project duration.

Applying the equation to the metrics shown yields

which produces a value well above the 1.0 level that indicates a schedule is reasonably achievable

and the SPI(t) current value of 1.08.



Figure 8. Schedule Performance through Period 40

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38.6% Complete 51.9% Complete



After week 40 the baseline duration was reduced to 55.4 weeks. Comparing Figure 9 with Figure 8

shows that rebaselining the project resulted in a proportional shift in Earned Value, and while the

magnitude of the performance curves shifted, the overall trend and data indications remained intact.

For example, in the original baseline the value of SPI(t) at week 40 is 1.08 while after rebaselining

SPI(t) at week 40 is 1.03. This curious effect is the result of shifting Earned Value to past periods

rather than distributing remaining work to future periods within the reduced baseline duration.

Following project performance in Figure 9 from week 40 shows a continuing negative performance

trend to week 48, where TSPI(p) exceeds 1.10, indicating that the schedule has virtually reached the

point of no recovery only 8 weeks after replanning and rebaselining. At week 53 we see SPI exhibit

its classical unreliability as it levels off and begin its convergence to BAC, while Earned Schedule’s

SPI(t) continues to show deteriorating schedule performance. TSPI(p) also provides an additional

visual status at the project baseline duration as its value assumes a negative value and appears as a

vertical marker. Perhaps the most significant display of project performance is the steadily declining

values of SPI(t) from week 31 through week 63. Although brief periods of improvement can be seen

over the course of 2 to 3 weeks, the long-term trend clearly indicates worsening schedule

performance over half the project’s duration and clearly provides a better indication of true project

performance than EVM’s SPI counterpart provides.



Figure 9. Schedule Performance through Project Completion

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4 EARNED SCHEDULE ADOPTION IN CSC

4.1 BACKGROUND

CSC has been recognized as a successful adopter of Earned Value cost management methods in

information technology projects (Fleming & Koppelman, 2003). Through commitments in internal

training programs, establishment of consistent processes, and development of custom tools and

systems CSC continues to enhance organizational maturity in project management and Earned Value

Management. It is the environment and culture of continuous evolution that provides CSC the

opportunity to adopt, quickly and efficiently, Earned Schedule methods into regular practice by

building on an established foundation of tools and methods.

4.2 INTEGRATION VIA THE CSC TOOLKIT

The CSC Toolkit is a proprietary custom extension to Microsoft Project that supports tracking,

reporting, auditing, and exporting project performance data. The CSC Toolkit used in conjunction with

the Earned Schedule Workbook, a Microsoft Excel workbook developed by the author, can provide

Earned Schedule data for any CSC project manager using Microsoft Project with the CSC Toolkit

extension. The Earned Schedule Workbook calculates not only Earned Schedule values and metrics

but also conventional Earned Value budget and schedule metrics, including budget TCPI indicators.

The Workbook provides graphs for schedule and budget to allow easy interpretation of project

performance and to compare EVM and ES metrics. More reliable schedule metrics provided by

Earned Schedule provide the basis for advanced schedule and budget control tools as described in

the following sections.

Figure 10. CSC Toolkit Earned Value Export

Earned Schedule information is available by 3 simple steps:

1. Using the CSC Toolkit’s Analyze Earned Value function, export actual performance data to an

Excel spreadsheet. The appropriate data fields as documented in the Earned Schedule

Workbook are selected and the CSC Toolkit exports data to Excel.

2. Paste the exported data sheet to the Earned Schedule Workbook

3. Enter the project’s baseline start, baseline finish, current status date and Budget at Completion in

the Earned Schedule Workbook. Optionally an Estimate at Completion (EAC) may be entered for

calculation of the TCPIe metric.



Following the three steps above Earned Schedule and Earned Value metrics are presented in a

summary page, shown in Figure 11. The summary page includes a comments section, allowing the

project manager to prepare a single sheet overview of project status that includes both technical

performance data and a narrative.

Figure 11. Earned Schedule Workbook Summary Page

The Earned Schedule Workbook provides graphs for both budget (as shown in Figure 12) and

schedule (as shown in Figure 13) to aid in project performance analysis. Since Earned Schedule is

an emerging practice, the schedule performance graphs include standard Earned Value performance

metrics to allow easy data comparison.

Figure 12. Earned Schedule Workbook Budget Performance Graph



Figure 13. Earned Schedule Workbook Schedule Performance Graph

4.3 FROM PERFORMANCE MONITORING TO PERFORMANCE ADJUSTMENT

Calculating and interpreting reliable project performance metrics is certainly a challenge, and

determining an appropriate course of action for effective project control is an important step.

Schedule often seems to be the driving factor among the “triple constraints” of schedule, scope, and

budget. How do the metrics presented provide practical help in finishing a project on time? Once a

more reliable schedule metric via Earned Schedule is achieved to complement the CPI cost metric,

multiple techniques based on balancing performance indicators become available. Lipke provides a

good overview of project recovery techniques (Lipke, 2009c) and his work serves as a basis for

examining some proposed project recovery data that can be useful in CSC’s project portfolio.

Recall from Section 2.1 that Earned Value uses the Schedule Performance Index, SPI, and from

Section 2.3 that Earned Schedule uses the Schedule Performance Index for Time, SPI(t), as

indicators of project performance, with a value of 1.0 indicating on time performance while values less

than 1.0 show that a project is behind schedule. We have also seen that the Earned Schedule metric

To Complete Schedule Performance Index, TSPIp, indicates the target value that SPI(t) must achieve

for on time performance. While it is unlikely that any equation will ever provide a universal method for

schedule recovery under all circumstances, it is possible to gauge a relative level of resources

required for schedule recovery based on present resource usage and efficiency. The author

developed the Schedule Recovery Level metric, SRL, for integration into the Financial Services

Group’s project reporting methods for trial and evaluation. SRL defines the percentage of the

project’s planned resources required to achieve on time performance.

Note that Figure 11 shows a calculated value for Resource Ratio. Resource Ratio is defined as

Given the CSC convention of measuring cost in true labor hours instead of actual dollars we have a

ratio of actual labor hours expended to labor hours planned. We deduce that the actual progress

efficiency against planned schedule (SPI(t)) was achieved using this proportion of planned. This

interrelationship between these metrics serves as the basis for computing Schedule Recovery Level.

We achieve on time schedule performance when the Schedule Performance Index, SPI(t), is greater

than or equal to TSPIp. Therefore, the goal of driving the schedule toward on time completion is to

apply sufficient resources to transform SPI(t) from its current level to the value of TSPIp. Using the

equation for the Schedule Performance Index for Time



and TSPIp

we can use the ratio of the normalized values of the equations multiplied by the Resource Ratio to

define Schedule Recovery Level as the percentage of planned labor hours required to achieve on

time performance.

Obviously, the SRL metric only offers guidance to the project manager, since applying the calculated

resource level may prove too expensive to improve the project’s schedule to be practical given the

project’s budget. Conversely, SRL may help in avoiding unnecessary project expense. In the

circumstance where project metrics indicate a significantly earlier than required finish it is possible

that the project may be overspending for no benefit. Rather than finish early with a budget overrun,

SRL may guide the project manager to a better conclusion through on time, on budget completion.

Evaluating SRL in the example data in Figure 11 we find that the project is 1.8 weeks ahead of

schedule with a comfortable SPI(t) of 1.08. CPI shows good cost performance with a value of 1.02.

The indicators suggest that the project could spend less and still finish on time, with SRL showing we

could use 98% of originally planned resources to reduce expense. The prudent project manager

would not reduce resources in a single step, but gradual reduction in resources while monitoring the

resulting metrics could result in a better balance between schedule and budget.

The logical extension of the Schedule Recovery Level metric is a corresponding measure for budget.

Using the same rationale, a Budget Recovery Level (BRL) is found by

With the foundation of the Earned Schedule technique and simple algebraic manipulation of the

metrics presented, CSC practitioners can easily add valuable tools to their project management

repertoire. As an example, substituting TSPIe in the SRL equation allows evaluation of the resource

level required to achieve a revised forecasted project duration.

5 CONCLUSIONS AND FURTHER RESEARCH

5.1 CONCLUSIONS

This paper has presented an overview of the history and application of Earned Value Management

(EVM) methods in predicting project schedule performance. An introduction to the theory and

application of Earned Schedule has been presented along with a simple example of its application.

Earned Schedule was applied retroactively to analyze its predictive capabilities when compared to

classical Earned Value schedule metrics, showing that ES and its associated metrics provide both a



more reliable present status and forecast of project performance. From the theory, example, and

foundation of the literature cited it appears that Earned Schedule offers significant potential

improvement in project schedule reporting and forecasting.

Reviewing the application of standard Earned Value techniques shows converting budget variance to

time, lack of metrics for projects past their planned baseline, and the unreliability of EVM schedule

data in the final third of a project’s execution present serious limitations. While formulas and

techniques exist and have been used to convert dollars to time, the counterintuitive nature of the

method illustrates the need for a more straightforward technique. When considering a standard

normal distribution of possible project outcomes it is inevitable that some projects are destined to

finish later than planned, and for these projects, Earned Value schedule methods provide no useable

data when project status and forecasting are essential to minimize adverse outcomes. Likewise, as

projects move into the final third of their planned duration, Earned Value perhaps does a greater

disservice to the project based organization by providing data that, while available and theoretically

useable, is shown to be inaccurate with errors that are difficult to quantify. As discussed, the

inaccuracies of Earned Value are amplified in late projects, again, failing to provide dependable

information at the very point when accurate data can assist in effecting project recovery. These

limitations have motivated the search for other methods, resulting in the evolution of Earned

Schedule.

Earned Schedule provides a simple and straightforward method for schedule analysis that overcomes

the limitations in Earned Value methods. The evolution of commodity computer technology in

conjunction with ever more common project scheduling tools in the desktop environment are

providing the automated analytical platforms required for widespread adoption of Earned Schedule.

This paper shows that Earned Schedule extensions are easily applied to existing tools, techniques,

and data to provide an enhanced dimension to project schedule analysis and management.

As shown in the retroactive project analysis provided, Earned Schedule can offer not only a better

view of a project’s current status, but perhaps more importantly it offers predictive methods that can

help avoid missteps in project planning as well as assist in developing project recovery plans. Using

the To Complete Schedule Performance Index (TCPI) equations to analyze the probability of success

provides valuable insight in evaluating alternative project completion scenarios. Reliable performance

indices calculated by CPI and SPI(t) allow derivative metrics that may include, among others,

Schedule Recovery Level and Budget Recovery Level, to enable better control and more predictable

results in project execution.

The advantages of Earned Schedule methods are readily available to the CSC project management

community. Leveraging the corporate investment in the CSC Toolkit with the author’s Earned

Schedule Workbook enables evaluation and practical application of advanced project metrics. The

infrastructure of collaboration and training that CSC applies throughout the organization is well suited

to promote quick evaluation and adoption of these promising new methods.

5.2 FURTHER RESEARCH

Research and validation of ES metrics have been conducted retrospectively on individual projects

(Vandevoorde & Vanhoucke, 2006), a small sample of six actual projects (Henderson, 2003) as well

as on an extensive set of generated project data (Vanhoucke & Vandevoorde, 2007). Between these

boundaries of existing research lies the need and opportunity to evaluate the performance of Earned

Schedule on a larger sample set of actual projects. While time and coordination challenges precluded

conducting this broader based research as the subject of this paper, the research and findings

presented here indicate that further analysis based on available project data is warranted, with the

objective of validating the application of Earned Schedule in an established project environment. Just



as a single design for a project organization or a single method can’t be used for all scenarios, tools

and metrics must be tailored for and perform in real world environments.

In considering project environments, it can’t be assumed that what works in one industry,

geographical region, or technical infrastructure will work in another. Differences in organizational

maturity, infrastructure, tools, and reporting methods can have a significant impact on the viability of

any given method. Based on the research and findings presented here CSC’s Financial Services

Group is currently working to integrate Earned Schedule metrics into its normal project reporting

methods. Given the significant size of FSG’s project portfolio, collecting and analyzing resulting data

will provide an opportunity not only to advance CSC’s best practices in project management but also

to make a significant contribution to the project management community at large by contributing to

the Earned Schedule body of knowledge with a large sample of real world data. The results of this

effort should be presented in a subsequent paper.

As demonstrated via the derivation of Schedule Recovery Level, reliable schedule metrics provided

by Earned Schedule facilitate the creation of advanced budget and schedule metrics and techniques.

Enhancements of existing tools to automate the calculation of optimal resource levels to achieve a

desired schedule outcome using Monte Carlo simulation could provide a family of performance

curves at any point during project execution, allowing the project manager to evaluate multiple project

recovery strategies. Earned Schedule as an emerging practice is not an end goal, but rather a

building block to improve CSC’s core competency in project management.



6 WORKS CITED

Abba, W. (2000). How Earned Value Got to Primetime: A Short Look Back and Glance Ahead. Paper

presented at the PMI Seminars & Symposium. Proceedings, 2000, 20436.PDF, Houston, TX.

Anbari, F. T. (2003). Earned Value Project Management Method and Extensions. [Article]. Project

Management Journal, 34(4), 12-23.

Book, S. A. (2006a). Earned Schedule and Its Possible Unreliability as an Indicator. The Measurable

News, 7.

Book, S. A. (2006b). Earned Schedule and Its Possible Unreliability as an Indicator: Correction Note.

The Measurable News, 3.

Fleming, Q. W. (1988). Cost/Schedule Control Systems Criteria : The Management Guide to

C/SCSC. Chicago, Ill.: Probus Pub. Co.

Fleming, Q. W., & Koppelman, J. M. (2000). Earned Value Project Management (2nd ed.). Newton

Square, Pa., USA: Project Management Institute.

Fleming, Q. W., & Koppelman, J. M. (2003). What's Your Project's Real Price Tag? Harvard Business

Review, 81(9), 20.

Henderson, K. (2003). Earned Schedule: A Breakthrough Extention to Earned Value Theory? A

Retrospective Analysis of Real Project Data. The Measurable News, 6.

Henderson, K. (2005). Earned Schedule in Action. The Measurable News, 7.

Jacob, D. (2003). Forecasting project schedule completion with earned value metrics. The

Measureable News, 2003(3), 3.

Jacob, D. (2006). Is “Earned Schedule” an Unreliable Indicator? No, but It’s Not Necessarily the

Premier Indicator for Assessing Schedule Performance. The Measurable News, 6.

Lipke, W. (2003). Schedule is Different. The Measureable News, March 2003, 5.

Lipke, W. (2006). Applying Earned Schedule to Critical Path Analysis and More. The Measurable

News, 3.

Lipke, W. (2009a). Earned Schedule (First ed.): Lulu Publishing.

Lipke, W. (2009b). Schedule Adherence…A Useful Measure for Project Management. The

Measureable News, 2009(3), 6.

Lipke, W. (2009c). The TCPI Indicator Transforming Project Performance. Projects & Profits, March

2009.

Lipke, W. (2011). Schedule Adherence and Rework. The Measureable News, 2011(1), 6.

Project Management Institute. (2005). Practice Standard for Earned Value Management. Newtown

Square, PA: Project Management Institute, Inc.

Project Management Institute. (2008). A Guide to the Project Management Body of Knowledge

(PMBOK Guide), Fourth Edition (4th ed.). Newtown Square, Pa.: Project Management Institute.



Vandevoorde, S., & Vanhoucke, M. (2006). A Comparison of Different Project Duration Forecasting

Methods Using Earned Value Metrics. International Journal of Project Management, 24(4), 289-302.

doi: 10.1016/j.ijproman.2005.10.004

Vanhoucke, M., & Vandevoorde, S. (2007). A Simulation and Evaluation of Earned Value Metrics to

Forecast the Project Duration. The Journal of the Operational Research Society, 58(10), 1361-1374.

DISCLAIMER

The information, views and opinions expressed in this paper constitute solely the authors’ views and

opinions and do not represent in any way CSC’s official corporate views and opinions. The authors

have made every attempt to ensure that the information contained in this paper has been obtained

from reliable sources. CSC is not responsible for any errors or omissions or for the results obtained

from the use of this information. All information in this paper is provided “as is,” with no guarantee by

CSC of completeness, accuracy, timeliness or the results obtained from the use of this information,

and without warranty of any kind, express or implied, including but not limited to warranties of

performance, merchantability and fitness for a particular purpose. In no event will CSC, its related

partnerships or corporations, or the partners, agents or employees thereof be liable to you or anyone

else for any decision made or action taken in reliance on the information in this paper or for any

consequential, special or similar damages, even if advised of the possibility of such damages.

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