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Applying Earned Schedule
to Critical Path Analysis and More
By Walt Lipke, Retired Deputy Chief, Software Division, Tinker Air Force Base
Earned Schedule is a fairly new method for analyzing schedule performance; it is a derived applica-
tion of Earned Value Management (EVM) data. Created three years ago, the method has propagated
to several countries and been used for various types of work spanning a large range of project sizes.
During this period of infancy, a misperception may have emerged that ES is only applicable to the
total project and thus is limited for schedule performance analysis. As has been shown in the article,
“Connecting Earned Value to the Schedule,” ES can be used for much more. It facilitates the ability
to identify constraints, impediments, and the possibility of rework at the task level . This informa-
tion is very useful for management purposes, but it does not provide performance indicators below the
project level. This paper describes how ES can be applied to sub-levels of the project. Using this capa-
bility, the project manager can analyze schedule performance at virtually any level desired – control
accounts, work packages, and critical path activities.
During presentations about Earned Schedule
(ES) I am sometimes asked, “Can Earned
Schedule be used to analyze the critical
path?” My response is always, “Certainly,
simply treat the critical path as the project.” I notice then
that the person doesn’t say anything else, but has the look
of someone who didn’t have his question answered. This
question is asked often enough …with the same result
…that it is apparent a more complete response is needed.
Hopefully this paper will satisfy all those who have
voiced the question in the past, as well as those who are
seeking a way to use ES as a “drill-down” tool.
In this paper, it is assumed the reader has good
understanding of Earned Value Management (EVM) and
that Earned Schedule is being used as one of the project
management tools. Although it is likely the reader has a
working knowledge of ES, a review of the concept, the
time-based indicators and the forecasting calculation is
needed to establish a common foundation for the remain-
der of the paper.
The ES idea is simple: identify the time at which the
amount of earned value (EV) accrued should have been
earned . By determining this time, time-based indica-
tors can be formed to provide schedule variance and per-
formance efﬁciency management information.
Figure 1, Earned Schedule Concept, illustrates how the
ES measure is obtained. Projecting the cumulative EV
onto the project management baseline (PMB), as shown
by the diagram, determines where planned value (PV)
equals the EV accrued. This intersection point identi-
ﬁes the time that amount of EV should have been earned
in accordance with the schedule. The vertical line from
the point on the PMB to the time axis determines the
“earned” portion of the schedule. The duration from the
beginning of the project to the intersection of the time
axis is the amount of earned schedule (ES).
With ES determined, time based indicators can be cre-
ated. It is now possible to compare where the project is
time-wise with where it should be in accordance with the
PMB. “Actual time,” denoted AT, is the duration at which
the EV accrued is recorded. The time-based indicators
are easily formulated from the two measures, ES and AT.
Schedule Variance becomes SV(t) = ES – AT, and Sched-
ule Performance Index is SPI(t) = ES / AT.
The graphic and the box in the lower right of ﬁgure 1
portray how ES is calculated. While ES could be deter-
mined graphically as described previously, the concept
becomes much more useful when facilitated as a calcula-
tion. As observed from the ﬁgure, all of the PV through
May has been earned. However, only a portion of June
has been completed with respect to the baseline. Thus the
duration of the completed portion of the planned schedule
is in excess of 5 months. The EV accrued appears at the
end of July, making actual time equal to 7 months. The
method of calculation to determine the portion of June to
credit to ES is a linear interpolation. The amount of EV
extending past the cumulative PV for May divided by the
incremental amount of PV planned for June determines
the fraction of the June schedule that has been earned.
The creation of ES and the derivative time-based
schedule performance efﬁciency, i.e. SPI(t), facilitates
forecasting the duration of the project and its completion
Fall 2006 27
date. Two formulas are presently in use; one is termed
the “short form” and the other the “long form.” The short
form is IEAC(t) = PD / SPI(t), where IEAC(t) is the In-
dependent Estimate at Completion (time) and PD is the
planned duration for the project . The long form is not
needed in the subsequent discussion and, consequently, is
Why the Question?
In the previous discussion of the concept, it is established
that the determination of ES requires the Project Man-
agement Baseline (PMB), and is a cumulative measure.
It is the cumulative “earned” portion of the schedule.
The cumulative nature of ES is, also, emphasized in the
seminal paper, “Schedule is Different” . And, the in-
structions for using the ES calculator also stress that the
complete PMB of the project must be entered to expect
correctly calculated results .
My conjecture is the emphasis of these statements
could cause the misunderstanding that the method is only
applicable to the total project. With the repeated overtures
to use the project PMB along with the statements that ES
is a cumulative measure, it is a reasonable deduction.
However, ES is not limited to only the total project. It
is much more applicable. I intend to dispel the perception
of the limitation and show how the ES method can be ap-
plied at any level of interest within a project, including
work packages, control accounts, and critical path activi-
ties. In other words, the capability is available for “drill-
down” schedule analysis by applying the ES method to
the project EVM data.
What’s the Trick?
To broaden the applicability of ES to detailed schedule
performance analysis is not difﬁcult. All that is required
is to view the subject of the analysis as if it is the total
project. This response is very similar to my answer, earli-
er in the paper, to the question concerning applying ES to
analyze the critical path. It should be. But, just as it was
for the persons who posed the question during my pre-
sentations, the answer is incomplete. Thus, the question
becomes, “How do I make a portion of a project appear
like a total project?”
To answer this question, let us view Table 1, Project
Plan and Performance Measures by Task. The table de-
picts the time-phased plan, earned value (EV), and actual
costs (AC) for each of ten tasks comprising the notional
project. The time-phased plan, the performance baseline,
is created from the planned value (PV) amounts. All
amounts are in units of cost and are periodic, not to be
understood as cumulative. The diamond symbol indicates
the initiation of the task. At project completion, the sum
of the periodic EV amounts for each task equals the sum
of its PV quantities.
To construct the PMB, ﬁrst sum the periodic PV
amounts across all tasks by performance period (PP).
Then, ﬁnalize the PMB by creating the cumulative quan-
tities by PP. This is accomplished by adding, successive-
ly, the periodic PV determined from the summing across
tasks. For example, add the total periodic PV in period
two to period one to obtain the cumulative PV for pe-
riod two; then for period three add its periodic PV to the
cumulative PV for period two. This process is repeated
through period ten. The resultant cumulative values for
periods one through ten is the PMB. This is the project
performance baseline from which ES for the total project
To analyze tasks (work packages), control accounts
or critical path a performance baseline must be created
speciﬁc to the analysis area; i.e., if a cost account is to
FIGURE 1. EARNED SCHEDULE CONCEPT
be analyzed, its comprising tasks must be segregated
and grouped. Then the process for creating the project
PMB is applied to this set of tasks to create a PMB for
the cost account, PMBa. Having PMBa allows ES to be
calculated speciﬁcally for the cost account. In turn, the
determination of ES facilitates the calculations of SPI(t)
and IEAC(t) for the speciﬁc evaluation of cost account
Critical Path Example
With the methods in place, the process for applying ES
to critical path analysis can be described. As discussed in
the previous section of the paper, segregate and group the
critical path tasks, and create a PMB representing them,
PMBc. For this example, the critical path for the notional
project includes tasks one, four, eight, and ten (1-4-8-10).
Table 2, Performance Baselines and Earned Value Mea-
sures, aggregates the data for both the total project and
the critical path.
The project management baselines for the total proj-
ect (PMB) and critical path (PMBc) are their respective
PVcum rows. The PVper rows represent the summation
of the planned values of the representative tasks for the
speciﬁc performance period. As described previously,
the PVcum is obtained from the PVper values. For ex-
ample the calculation for PVcum for period three of the
Task Nr Measure
0 1 2 3 4 5 6 7 8 9 10 11 12
5 5 5
10 10 10
8 13 9
10 15 10
3 4 3
5 5 5
5 5 5
5 3 5 2
5 5 5 2
10 10 10 10 10
8 9 7 13 8 5
10 10 10 15 10 5
5 10 5
5 5 5
TABLE 1. PROJECT PLAN AND PERFORMANCE MEASURES BY TASK.
Fall 2006 29
total project is determined by adding PVper for period
three to PVcum of period two: PVcum(3) = PVcum(2) +
PVper(3). Using the values from the Table 2, the calcula-
tion can be performed: PVcum(3) = 10 + 35 = 45.
The remainder of Table 2 contains the performance
data, earned value (EV) and actual costs (AC) for the
total project and for the speciﬁc critical path tasks. The
accumulation of this information allows analysis and pre-
diction to occur for the total project and the critical path.
The EVM performance indicators and duration fore-
casts calculated from the Table 2 data are aggregated in
Table 3, Performance Indicators and Duration Forecasts.
The “p” and “c” appended to the indicators shown in the
table indicate period and cumulative, respectively.
From Table 2 it can be determined that the project
is planned to complete in 10 time periods, but actually
completes in 12. Similarly, Table 2 indicates the tasks
on the critical path completed at the planned time, 10
periods. For comparison purposes both SPI and SPI(t)
indicators are shown in Table 3. The familiar behavior of
the two indicators is seen for the critical path and total
project. The values for SPI(t) compare favorably to those
for SPI for the critical path which completes as planned.
However for the total project, the values for the two in-
dicators are signiﬁcantly different. The corresponding
values for SPI(t)c and SPIc noticeably begin departing in
period eight, and conclude as expected for late ﬁnishing
projects; at project completion, SPI(t)c is a valid number
(0.8333) truly depicting the actual schedule performance
efﬁciency, while SPIc illogically equals 1.0.
By inspection of Table 2, it is obvious the critical path
changed during project execution. Knowing this begs the
TABLE 2. PERFORMANCE BASELINES AND EARNED VALUE MEASURES.
�� � Performance Period � ��
Measure 0 1 2 3 4 5 6 7 8 9 10 11 12
0 5 5 35 30 40 30 20 5 10 5 0 0
0 5 10 45 75 115 145 165 170 180 185 185 185
0 0 4 16 43 27 18 31 16 9 15 3 3
0 0 4 20 63 90 108 139 155 164 179 182 185
0 0 5 20 52 35 20 37 22 10 20 5 3
0 0 5 25 77 112 132 169 191 201 221 226 229
0 5 5 5 5 5 5 10 5 5 5 0 0
0 5 10 15 20 25 30 40 45 50 55 55 55
0 0 4 8 10 3 0 12 8 0 10 0 0
0 0 4 12 22 25 25 37 45 45 55 55 55
0 0 5 10 12 5 0 15 12 0 14 0 0
0 0 5 15 27 32 32 47 59 59 73 73 73
TABLE 3. PERFORMANCE INDICATORS AND DURATION FORECASTS
�� � Performance Period �� �
Indicator 0 1 2 3 4 5 6 7 8 9 10 11 12
xxx xxx 0.8000 0.8000 0.8269 0.7714 0.9000 0.8378 0.7273 0.9000 0.7500 0.6000 1.0000
xxx xxx 0.8000 0.8000 0.8182 0.8036 0.8182 0.8225 0.8115 0.8159 0.8100 0.8053 0.8079
xxx 0.0000 0.8000 1.4857 1.3143 0.7750 0.4500 0.9750 0.7000 0.4500 1.9500 0.5000 0.6000
xxx 0.0000 0.4000 0.7619 0.9000 0.8750 0.8042 0.8286 0.8125 0.7722 0.8900 0.8545 0.8333
xxx 0.0000 0.8000 0.4571 1.4333 0.6750 0.6000 1.5500 3.2000 0.9000 3.0000 xxx xxx
xxx 0.0000 0.4000 0.4444 0.8400 0.7826 0.7448 0.8424 0.9118 0.9111 0.9676 0.9838 1.0000
xxx xxx 25.00 13.13 11.11 11.43 12.44 12.07 12.31 12.95 11.24 11.70 12.00
xxx xxx 0.8000 0.8000 0.8333 0.6000 xxx 0.8000 0.6667 xxx 0.7143
xxx xxx 0.8000 0.8000 0.8148 0.7813 0.7813 0.7872 0.7627 0.7627 0.7534
Critical Path SPI(t)p
xxx 0.0000 0.8000 1.6000 2.0000 0.6000 0.0000 1.7000 1.3000 0.0000 2.0000
xxx 0.0000 0.4000 0.8000 1.1000 1.0000 0.8333 0.9571 1.0000 0.8889 1.0000
xxx 0.0000 0.8000 1.6000 2.0000 0.6000 0.0000 1.2000 1.6000 0.0000 2.0000
xxx 0.0000 0.4000 0.8000 1.1000 1.0000 0.8333 0.9250 1.0000 0.9000 1.0000
xxx xxx 25.00 12.50 9.09 10.00 12.00 10.45 10.00 11.25 10.00 xxx xxx
question, “Does the simultaneous application of ES to
both the critical path and total project provide advance
warning of this condition?” If the answer is “Yes,” then
applying ES has been shown to yield analysis advantages
to project managers.
One signiﬁcant implication of a response of “Yes” is
detailed schedule performance information can be ob-
tained solely from the EVM data …without the laborious
bottom-up analysis performed by skilled schedulers.
Another point is detailed schedule analysis takes time
and considerable effort by sometimes several people, of-
tentimes becoming a distraction to those performing proj-
ect work. For any size, but especially for larger projects,
performing a detailed schedule analysis involving several
subcontractors in the time-frame the ES schedule forecasting
calculations can be made is virtually impossible.
The time advantage offered by IEAC(t) does not mean
to imply that detailed bottom-up schedule analysis should
never be performed. Certainly, just as the ﬁnal cost esti-
mate obtained from IEAC is not relied on solely at criti-
cal points without a detailed cost analysis, a bottom-up
schedule estimation should be performed for conﬁrmation
of the ES forecast.
By reviewing and comparing the IEAC(t) numbers
for the total project and critical path we can answer the
question posed a few paragraphs earlier. The answer is
“Yes,” it can be observed that the critical path is likely
to have changed. From examining Table 3 it is seen very
early, beginning with period 3, that the forecast duration
for the total project is always greater than the forecast for
the critical path. It is reasonably clear that the critical path
changed early in the project execution from tasks 1-4-8-
10. From this example, it may be said that ES can provide
advance warning that the critical path has changed.
Without going into the detail, the project duration fore-
casting method from ES can further be used to identify
the longest duration path; i.e., the changed critical path.
By forecasting duration for each task and inserting the
forecasts into the network structure of the schedule, the
actual critical path can be determined as well as condi-
tions for ﬂoat. With some technical ingenuity, this analy-
sis could be completely automated.
The Earned Schedule analysis method is demonstrated in
this paper to be applicable to more than the total project.
Segregating and grouping EVM data for a speciﬁc por-
tion of the project is the technique by which ES is made
applicable to the total project and any sub-level desired.
Speciﬁcally, the technique is shown to be capable of ana-
lyzing the schedule performance for the critical path. By
employing the same techniques to analyze critical path,
schedule performance by individual tasks can be evalu-
ated, which then allows identiﬁcation of the longest dura-
tion path for the project (actual critical path) along with
1. Lipke, Walt. “Connecting Earned Value to the
Schedule,” CrossTalk, June 2005: On-line (http://www.
2. Lipke, Walt. “Schedule is Different,” The Measurable
News, March 2003: 10–15.
3. Henderson, Kym. “Further Developments in Earned
Schedule,” The Measurable News, Spring 2004: 15–22.
About the Author
Walt Lipke recently retired as the deputy chief of the
Software Division at the Oklahoma City Air Logistics
Center. The division employs approximately 600 people,
primarily electronics engineers. He has over 35 years of
experience in the development, maintenance, and man-
agement of software for automated testing of avionics and
jet engines as well as automation of industrial processes.
During his tenure, the division achieved several software
process improvement milestones:
• 1993 — ﬁrst Air Force activity to achieve Level 2 of
the Software Engineering Institute’s Capability Ma-
turity Model® (CMM®)
• 1996 — ﬁrst software activity in federal service to
achieve CMM® Level 4 distinction
• 1998 — division achieved ISO 9001/TickIT registration
• 1999 — division received the SEI/IEEE Award for
Software Process Achievement
Mr. Lipke has published several articles and presented
at conferences, internationally, on the beneﬁts of software
process improvement and the application of earned value
management and statistical methods to software proj-
ects. He is the creator of the technique Earned Schedule
(Copyright© 2003 Lipke), which extracts schedule infor-
mation from earned value data. Mr. Lipke is a graduate
of the USA DoD course for Program Managers. He is a
professional engineer with a master’s degree in physics,
and is a member of the physics honor society, ΣΠΣ. Lipke
achieved distinguished academic honors with the selec-
tion to ΦΚΦ.