earned schedule literature review - billy mowery schedule literature review...the analysis and...

Earned Schedule Literature Review

Bill Mowery MPM, PMP

7/20/2012

This document is presented as collateral and background material for the author’s paper “Earned Schedule: From Emerging Practice to Practical Application” and represents a partial literature review conducted in writing the paper. The literature review is not represented as comprehensive, nor are all subjects contained in the literature review directly represented in the final paper, but it is presented as a reference for the reader to use as desired. The analysis and opinions expressed herein are solely those of the author.

Bill Mowery MPM, PMP Earned Schedule Literature Review Page 1 of 36 20 July 2012

Table of Contents Cost/Schedule Control Systems Criteria: The Management Guide to C/SCSC .................................... 2

How Earned Value Got to Primetime: A Short Look Back and Glance Ahead .................................... 3

What's Your Project's Real Price Tag? ................................................................................................... 4

Earned Value Project Management Method and Extensions ................................................................ 5

Schedule is Different ................................................................................................................................ 6

Earned Schedule: A Breakthrough Extention to Earned Value Theory? A Retrospective Analysis of Real Project Data ..................................................................................................................................... 8

Earned Schedule in Action .................................................................................................................... 10

Practice Standard for Earned Value Management .............................................................................. 13

Earned Schedule and Its Possible Unreliability as an Indicator ......................................................... 14

Earned Schedule and Its Possible Unreliability as an Indicator: Correction Note ............................. 17

Is “Earned Schedule” an Unreliable Indicator? No, but It’s Not Necessarily the Premier Indicator for Assessing Schedule Performance .................................................................................................... 18

Applying Earned Schedule to Critical Path Analysis and More .......................................................... 24

A Simulation and Evaluation of Earned Value Metrics to Forecast the Project Duration ................ 26

Prediction of Project Outcome: The Application of Statistical Methods to Earned Value Management and Earned Schedule Performance Indexes .................................................................. 30

Works Cited .............................................................................................................................................. 34


Cost/Schedule Control Systems Criteria: The Management Guide to C/SCSC

This fundamental work (Fleming, 1988) serves as a history and foundation for Earned

Value Management, and is cited it seems by more papers than not on the topic of Earned Value

Management. While the entire volume serves a valuable reference, Chapter 11, Earned Value

Techniques, offer particularly relevant references for the current topic. Descriptions of the

considerations of how to segment a project into the Performance Measurement Baseline offer

insights into how the logical structure of a project contributes to efficient tracking and reporting

are as relevant now as ever. The concepts of how a “Level of Effort” work package, while

necessary for project execution, does not directly equate to a project’s product and therefore

should be accounted for differently in recording Earned Value is a concept that seems lost on

many contemporary project-based organizations. The various methods for crediting Earned

Value to project tasks, ranging from the “50/50” technique to allowing credit for a limited

percentage until task completion serve as a reference and foundation for more contemporary

works on EVM.

While this book may seem outdated to some, it belongs on the bookshelf of anyone

serious about the subject of EVM (particularly since it seems to be available on the used book

market for less than $10, delivered). While advances in EVM have superseded some of the

techniques espoused in this volume, there are plenty of ideas and concepts that to many

practitioners seem new but are revealed as ideas of the original pioneers and thinkers of EVM.


How Earned Value Got to Primetime: A Short Look Back and Glance Ahead

These conference proceedings, published in 2000 (Abba, 2000), provide an introduction

to the new College of Performance Management, formerly the Performance Management

Association, as part of the Project Management Institute. An overview of the historical

development of Earned Value Management (EVM) is traced from the recognition of the need for

new management techniques in the 1950s to evolution of PERT / PERT-COST methods,

Cost/Schedule Planning Control System” (C/SPCS) Specification, DoD Instruction 7000.2 which

contained the Cost/Schedule Control Systems Criteria (C/SCSC), and the essential elements of

EVM as practiced today. Several examples of EVM evolution are provided in the context of

large weapons systems developments and the need for control on major acquisition projects.

Several examples of the international adoption of a simplified EVM approach outside defense

programs, notably

• Australia pioneered payment by earned value. • Canada emphasized small project management, as opposed to the major. • Japan applied EVM in its Ministry of Construction.

For the “Glance Ahead” the author details the College of Performance Management’s

framework for future collaboration, citing the conferences and tracks sponsored for EVM

practice in the future.

This paper provides two contexts for the discussion of Earned Schedule, a concise review

of the evolution of Earned Value Management and a brief history of the Project Management

Institute’s College of Performance Management. The background information on the College of

Performance Management is particularly important since many of the initial fundamental papers

and references for Earned Schedule come from its publication The Measurable News.


What's Your Project's Real Price Tag?

This article (Fleming & Koppelman, 2003) provides an overview and justification for

Earned Value Management’s use for cost control and forecasting. The classic example of the

shortfalls of managing cost by planned work versus cost of work while ignoring the value of

work is covered, with a brief explanation and example of why ignoring the value of work

performed can yield a false picture of cost performance. The author references (but does not

provide citation for) recently completed studies that validate the accuracy of EVM cost

management techniques on 52 Department of Defense (DoD) contracts, and refers to this study

as supporting the fact that EVM cost management allows accurate prediction of final project

costs well in advance of completion. The Cost Performance Index (CPI) is defined, and a brief

illustration of its application in forecasting is given. The same DoD study is used as a reference

in stating that after the 20% completion point in a project that the CPI rarely changes more than

10% at the end of a project, and typically tends to get worse instead of better. Early calculations

of cost overrun are usually underestimated.

An explanation of the complexities of using EVM in the DoD projects is given,

indicating that full compliance with the original EVM practice methods and the associated

complexity and cost could inhibit some organizations from using EVM. A simplified version of

EVM is suggested for private industry companies, one free of considerable overhead demanded

by government contracts. Examples of private industry companies such as Computer Sciences

Corporation and Edison International are given as success stories in EVM adoption.

The article helps establish a background and framework for the discussion of Earned

Schedule and the challenges of EVM. In contrast to the known and emerging problems with

EVM schedule management, EVM cost management is shown to be a mature and reliable


method for cost measurement and control in 2003, the same year that Earned Schedule made its

debut. Definite studies are quoted to lend validity to the authors’ claims. The statement that CPI

does not tend to vary by more than 10% after the 20% completion point in a project is significant

and provides one yardstick of performance for any subsequent cost metrics. By extension it is

logical to look for a schedule management metric that offers at least the same degree of validity

as the CPI statistic quoted. The article also mentions Computer Sciences Corporation by name as

a successful adopter of EVM, lending some initial credence to future research based on CSC

historical project data.

Earned Value Project Management Method and Extensions

Anbari’s paper (Anbari, 2003) provides an excellent overview of Earned Value

application, covering fundamentals of cost and schedule calculation, performance indices and

derived variations, completion forecasting methods for budget and schedule, and

recommendations on how to quantify management by exception and expected responses to

project variances. It provides a solid background for discussions on Earned Schedule and other

research directions (e.g. Earned Duration). The components of this paper relating to EVM

schedule metrics are of primary interest for this literature review.

While the author provides detailed examples of EVM application for cost metrics, he

notes that “EVM has not been widely used to estimate the total time at completion, total project

duration, or schedule for an activity, work package, or project based on actual performance up to

a given point in the project” An example of schedule performance forecasting utilizing six

variations of a Time Estimate At Completion (TEAC) formula. Forecast end dates are calculated

by adding the Actual Time (current reporting analysis date) and a derived Time Estimate To


Completion (TETC). The calculation of the appropriate TETC is based on assumptions of future

schedule efficiency. The important point of note from these formulas is that in specific

instances, SPI is utilized as a factor as is common practice. The author declares no specific

caveats on the limitation of EVM schedule metrics and their performance.

An interesting point of note is the author’s reference to reading time variance directly

from a graph of EV and PV, crediting the idea of schedule variance in units of time to Fleming

and Koppelman (Fleming & Koppelman, 2000). The nature of this concept proves important in

the evolution of Earned Schedule.

Of particular interest in this paper is a description of a color-based “traffic light”

qualitative status based on performance index thresholds. This concept is currently in use in

business units of Computer Sciences Corporation as a routine project reporting technique. The

article should help to fill the knowledge gaps about the origins and development of the method.

Schedule is Different

This paper, Schedule is Different (Lipke, 2003), presents the introduction of Earned

Schedule by name into the practice of project management. The author begins with a concise

review of basic Earned Value Management (EVM) principles and illustrates the concepts with

the classic S-Curves of EVM metrics. EVM introduction then moves on to explain the

limitations of EVM in measuring and predicting project schedules, focusing on EVM’s

convergence at zero. Since EVM schedule performance is based on the difference between

Budgeted Cost of Work Performed and Budgeted Cost of Work Scheduled, it is easily

demonstrated that these two values converge at the same point – the Budget at Completion

(BAC) – at project completion, regardless of whether the project is early, on time, or late. The


author also states that there is a “gray area” before EVM loses it management value where

schedule predictors are unreliable. The author states an opinion that most project managers

manage costs more closely than schedule due to the unreliability of EVM schedule metrics.

Against the background of EVM the author presents the concept and illustration of

Earned Schedule. The basis of Earned Schedule is the determination of the difference between

the point in time where a given Budgeted Cost of Work Performed is accomplished and the point

on the Budgeted Cost of Work Scheduled (Performance Measurement Baseline) curve that the

amount of work should have been accomplished. This difference, in units of time, is the Earned

Schedule variance. Using notation analogous to EVM, Lipke uses the notation SV(t) and SPI(t) to

indicate Schedule Variance and Schedule Performance Index based on Earned Schedule

calculations. The author provides a concise and well-illustrated explanation of Earned Schedule,

followed by a comparison of Earned Value and Earned Schedule performance for two

hypothetical projects, one finishing early and one finishing late. The author concludes with the

assertion that Earned Schedule provides schedule indicators that behave correctly over the span

of the entire project.

The author presents a simple and elegant approach and explanation for a time-based

project schedule performance metric. While easy to follow with the simplified examples

presented, the paper must be considered a first installment in the development of the Earned

Schedule concept. Only limited prefabricated examples are presented, with little background to

indicate how Earned Schedule performs in a real world environment. One would think that if the

author had been employing this method that he could cite some basic statistics or facts in support

of the theory. The paper itself can give the reader pause for concern in that there are some

assertions of fact made without references or citations to indicate the validity of the facts stated.


As an example two statements of note in this category are made by the author, the first that EVM

is unreliable in the final third of a project and the other that a “gray area” of EVM unreliability

exists before project completion. Neither of these statements cites references for verification, and

the author doesn’t attempt to define the “gray area” referenced.

Earned Schedule: A Breakthrough Extention to Earned Value Theory? A

Retrospective Analysis of Real Project Data

This article from The Measurable News (Henderson, 2003) provides the results of the

author’s study of the application of Earned Schedule (ES) to six completed projects in order to

validate the performance of ES methods. The paper begins with a very concise overview of the

pertinent aspects of Earned Value Management (EVM) and refers the reader to multiple in-depth

references for details. The author also briefly addresses the foundations of ES and provides a

simplified discussion on calculating ES metrics while referring to Lipke’s original paper (Lipke,

2003) for background.

EVM metrics for the test projects are based on a simplified model that relies on project

task-level percent complete update data for direct labor collected weekly. EVM data is collected

and calculated via a Microsoft Excel spreadsheet that is used for EVEM analysis, graphing, and

reporting.

Six projects, representing three late finish and three early finish completions are included

in the study. Two projects in the study included work stoppages while in progress. For late finish

projects the author illustrates a strong correlation between EVM schedule variance (SV($)) and

ES schedule variance (SV(t)) in the early phases of the projects while demonstrating the EVM’s

failure to accurately portray degrading schedule performance during periods of work stoppage


and for the period after the project’s baseline completion date. Performance during work

stoppages is attributed to the static nature of Budgeted Cost of Work Performed (BCWP) while

the late finish degradation is attributed to the Budget at Completion (BAC) upper limit for SV($).

Data presented for ES metrics show a correct depiction of degrading schedule performance

during both the work stoppage and after the projects’ baseline end dates. Similar trends and

performance are shown for EVM’s Schedule Performance Index (SPI($)) and Earned Schedule’s

Schedule Performance Index (SPI(t)). For early finish projects a strong correlation between the

behavior of SV($) and SV(t) is shown for the duration of the projects, with each metric accurately

depicting a three week work stoppage during project execution. The author notes that while each

method portrays the variance, the ES calculations quantify the delay in terms of time. The

performance indices provide an even stronger correlation than the simple variance quantities.

The author next demonstrates the use of ES in determining an Independent Estimate of

Duration (IED) and an Independent Estimate of Completion Date (IECD) as described by Lipke.

The author notes that the ES calculations for both IECD and IED seem to demonstrate utility in

providing a “sanity check” for “real schedule” measures over time. Variations of completion

calculations as described by Fleming and Koppelman (Fleming & Koppelman, 2000) are shown,

with the author indicating that he has insufficient data to show that ES provides a method for

better project end state predictions and suggesting that this is a good area for further research.

This paper, published three months after Lipke’s initial paper describing Earned

Schedule, provides the first independent study of ES metrics applied to actual project data.

Demonstrated results are as Lipke’s seminal paper indicates, with demonstrable results based on

simplified EVM application. While an important first step, the study is limited to a set of only six

projects with data from the author’s own projects, which limits the broader validation of earned


schedule’s applicability. An important emphasis is given to ES’s applicability in potentially

providing more accurate calculations and manipulations of schedule completion forecasts,

particularly for IEAC and IED. The author’s relation of completion forecasts as elaborated by

Christensen for large DoD contracts (Christensen, 1998) to projects on a much smaller scale such

as those in the study would bear further research to determine whether an order of magnitude

project size statistically impacts the metrics shown.

Earned Schedule in Action

Henderson follows up his retrospective study on Earned Schedule by presenting a paper

on the application of ES to a project from inception to close (Henderson, 2005). The paper

provides the requisite references to both EVM and ES references as background. The basic

parameters are presented for a small project encompassing an initial 54 total tasks, 12 of them on

the critical path. Justification for modifications of the project schedule are presented (e.g. better

understanding of project task relationships and sequencing of work, added tasks, and adjustments

to improve delivery). During project execution normal schedule maintenance activities were

practiced to align the project schedule with the normal course of events. The describes what he

sees as two common occurrences, one aggressive schedule for internal project team management

and a second “realistic” schedule used for management reporting and the reluctance of

management to explicitly declare schedule contingency in a project plan. Discussion then turns

to the need for predictors of project performance.

Expanding on his initial paper, Henderson defines an Independent Estimate at

Completion formula for time as:

𝐼𝐸𝐴𝐶(𝑡) = 𝑃𝐷𝑆𝑃𝐼(𝑡)


and a more general form:

𝐼𝐸𝐴𝐶(𝑡) = 𝐴𝑇 + (𝑃𝐷 − 𝐸𝑆)

𝑃𝐹

And the related formula for Independent Estimate of Completion Date (IECD) as

𝐼𝐸𝐶𝐷 = 𝑃𝑟𝑜𝑗𝑒𝑐𝑡 𝑆𝑡𝑎𝑟𝑡 𝐷𝑎𝑡𝑒 + 𝐼𝐸𝐴𝐶(𝑡)

where

AT = Actual Time, the Earned Schedule performance period number

PD = Planned Duration

ES = Earned Schedule

SPI(t) = Earned Schedule’s Schedule Performance Factor for time

The author indicates that behavior these ES performance metrics are equivalent to the

EVM cost-based metric for Independent Estimate at Completion (IEAC).

The example project’s Performance Measurement Baseline (PMB) was derived from a

resource loaded Microsoft Project schedule and transferred to an Excel spreadsheet for

management and reporting. Weekly project updates were performed by utilizing cumulative

percentage of work complete from Microsoft Project and used to derive Earned Value, reviewing

the project’s critical path and making required schedule updates, and transferring actual cost data

from corporate finance accounting systems to the project tracking template.

Analysis of project data indicated that IECD provided a more pessimistic schedule

prediction than the critical path, with IECD predicting a late finish and improving over the life of

the project and critical path predicting an early finish and degrading. This indicator behavior

contradicted the author’s expectations. Detailed analysis of in progress project performance,

based on Earned Schedule variance, SV(t), and analysis of individual tasks indicated that IECD


was the more accurate metric. IECD and Critical Path trended together, with IECD starting with

a more pessimistic view of schedule performance as previously noted. Based on the IECD and

ES calculations the project team advised management that the project was suffering an

irrecoverable two week schedule slip after Week 8 of the project. The project completion date

was revised while project tracking against the original baseline was maintained. Actual Cost data

was not available for the last two weeks of project performance due to an infrastructure change

in accounting systems. The author notes that this emphasizes an additional advantage of the

Earned Schedule methodology, since ES is time based and does not rely on actual cost data for

schedule metrics computation. The project’s actual end date was one week beyond the latest

revised schedule date, with the additional delay attributed to causes external to project

performance. Final performance analysis of IECD metric shows that it predicted the actual

completion date within one week five weeks before project completion, and did not vary more

than nine days from actual project completion for the last nine weeks of project performance.

Critical Path prediction of the end date varied as much as eighteen days over the last nine weeks

of project performance.

The author concludes by stating “The ES metrics were found to be of considerable

assistance and benefit in analyzing and managing the schedule performance of time critical

Example Project #1” and indicates that further validation of ES theory is needed for larger

projects and programs.

This paper provides an incremental step in the evolution of ES by showing practical

application and utility of the methodology to real world projects. The combination of a

simplified EVM approach and ES techniques contribute to a method that can apply to a wide

range of projects. The beginnings of enhanced metrics validation and the integrations of ES into


more standard performance index measurements represents potential improvement of long

established methods and calculations. A review of recommended PMI performance analysis and

forecasting methods and associated formulas (Project Management Institute, 2005) illustrates

areas of possible improvement with the availability of more reliable and accurate schedule

performance metrics. An interesting point is the author’s explicit declaration that the project was

not re-baselined after the approved two week delay in project delivery. Re-baselining a project

due to a predicted two week schedule delay seems contraindicated by common baseline control

principles which prohibit baseline modification for variance relief. In this case the author

indicated no actual change in scope that would justify baseline modification. An additional point

of interest is the method used to determine Earned Value based on task level percent complete.

While certainly a valid technique, a bit more detail on how the percent complete was determined

for tasks would provide the reader a greater degree of comfort in the validity of actual project

performance.

Practice Standard for Earned Value Management

This Project Management Institute Practice Standard (Project Management Institute,

2005) provides a checkpoint and reference for the application of Earned Value Management. Of

particular interest for this review is section 3.1, Schedule Analysis and Forecasting. The familiar

formulas for Schedule Variance, SPI = EVPV

, and Schedule Performance Index, SPI = EVPV

, are used

to show a Time Estimate at Completion as

𝐸𝐴𝐶𝑡 = 𝐵𝐴𝐶 /𝑆𝑃𝐼𝐵𝐴𝐶/𝑃𝐷


Note that the actual formula in the Standard uses months where I have substituted PD, or

Planned Duration, to make a general form equation. Also note that Budget at Completion (BAC)

would also be expressed in the same time units as PD. The standard says in calculating EACt that

“Using . . . the average Planned Value (PV) per unit of time” is sufficient for calculations, as

opposed to recommending a more rigorous time phased PMB as is covered in later sections. No

detailed exposition on the performance characteristics of EVM Schedule metrics is provided,

other than a one sentence caveat in the later “Analyze and Forecast Cost/Schedule Performance”

section where it is noted “However, when performance data at higher levels of the work

breakdown structure are reviewed, caution should be exercised because compensating good

performance can mask poor performance at lower levels.” The only other detail is found in Box

3-1, titled “Time-Based Schedule Measures - An Emerging EVM Practice”.

Box 3-1 provides a very short and concise overview of the Earned Schedule concerns and

techniques pertaining to EVM schedule metrics, but does not mention ES by name. A single

paragraph and six simple example equations are deemed sufficient to cover the topic.

In an overall assessment of this practice standard it is interesting to note that of the 51

pages, the essential coverage of the topic is presented in only 25 pages, although 2 additional

pages provide glossary for reference. It strikes one as counterintuitive that a Practice Standard

should completely cover such a diverse and complex topic in such a short space.

Earned Schedule and Its Possible Unreliability as an Indicator

The author uses this paper (Book, 2006a) to present potential problems with the Earned

Schedule (ES) calculations and metrics. The author begins with a restatement of the fundamental

ES formulas, and demonstrates an algebraic manipulation by substitution of terms to provide a


slightly simplified calculation of ES schedule variance. A discussion of the actual ES quantities

is presented, with the author taking issue with the units of measure used. Consider the following

generic formula for computing Earned Schedule

𝐸𝑆 = 𝐶 + 𝐵𝐶𝑊𝑃 − 𝐵𝐶𝑊𝑆(𝐶)

𝐵𝐶𝑊𝑆(𝐶+1) − 𝐵𝐶𝑊𝑆(𝐶)

where C represents the greatest time period number of project performance where BCWS is less

than BCWP. The author contends that while the integer portion of ES, represented by C, is in

units of time, the fractional portion resolves to a simple ratio and is therefore without units.

Adding a quantity expressed in units and a unit-less ratio is an algebraic contradiction and results

in a meaningless quantity.

Two examples are presented; one for a project that is “behind” schedule and one that is

“ahead” of schedule. Example data is given for each project. By using different rates of progress

(Budgeted Cost of Work Performed, or BCWP) for tasks in the example, the author proposes that

progress on individual tasks may indicate ahead or behind schedule conditions, yet the variance

from plan overall (cumulative BCWP) does not vary from planned work (Budgeted Cost of

Work Scheduled, BCWS). The author makes the point that this is an inherent problem in both

conventional EVM metrics as well as ES. The author also notes that for the example project ES

can’t be calculated since the denominator of the equation relies on BCWS, which converges at

BAC at project completion.

The author concludes by with the assertion that there is as yet no way to accurately

portray schedule status by calculation based on Cost Performance Report (CPR) data alone, and

that accurate schedule status must include a factor for the project’s critical path.


I have two serious and fundamental issues and one minor issue with this paper. The

minor issue pertains to the author’s seeming insistence that EVM metrics must depend on cost.

The major issues include the failure to properly address units of measure used in ES calculations

and the demonstrated misunderstanding of ES calculations themselves.

In describing EVM and ES methods, the author consistently uses cost as the basis for

establishing and measuring planned and accomplished work, and in several instances relates

concerns and issues in using cost, or dollar, based units. While EVM is typically used and

measured in terms of monetary cost, this is not an absolute requirement for applying EVM

techniques. As Section 2.2 of PMI’s Practice Standard for Earned Value Management indicates

(Project Management Institute, 2005), “Tasks may be planned and measured in whatever

resource units are most suitable to the work, including labor hours, material quantities, and the

monetary equivalent of these quantities.” EVM works just as well when measuring a number of

units produced as it does with dollar costs for value. In examining the validity of EVM

techniques it is advisable not to be tied too closely with specific instances and examples.

In addressing the author’s note about algebraic inconsistency, it seems the author fails to

realize that Earned Schedule does not directly represent a unit of time, but instead a number of

time periods of performance. The actual time periods represented may be weeks, months, years,

or days, but the number itself is nevertheless simply a number. The integer component of the ES

calculation is simply a whole number of time periods based on the correlation of BCWP and

BCWS. Likewise, the fractional component of ES is, as the author points out, a ratio of BCWP to

BCWS, and as a ratio is simply a number. Contrary to the author’s assertion, there is no algebraic

inconsistency in adding the integer and fractional components to arrive at total Earned Schedule.


My initial reading of this paper was somewhat torturous until I realized that the author

was attempting to express and manipulate the ES calculations in contradiction to Lipke’s

methods. The formulas and example ES data in the article are simply incorrect and demonstrate

the author’s lack of full understanding of ES calculations. While the logic of the author’s

assertions seems to still hold true, an error of this magnitude clouds the author’s fundamental

points for the serious reader. Note that a subsequent correction article was published and is

included in this literature review.

Earned Schedule and Its Possible Unreliability as an Indicator: Correction Note

Published six months after the original article, this work (Book, 2006b) addresses

problems and inconsistencies in the original article. The author begins by extending thanks to

Walt Lipke and Kym Henderson “for pointing out to me that the algebraic formulas used to carry

out the earned-schedule computations that comprised the original version of this article were

erroneous and based on a misunderstanding of the method of calculating earned schedule and its

derivative metrics.” All tables and metrics shown in the original article were recalculated by

appropriate published Earned Schedule techniques. The author then presents examples from the

original article using updated calculations, and steps through the ES calculations with his own

notation. The formula review is followed by an abbreviated restatement and presentation of the

data from the original article. The author asserts that while the calculation of ES metrics was in

error, his fundamental concerns with ES metrics remain unchanged, and that reliable schedule

information can’t be derived by simple calculation, but that consideration of the critical path

status is essential for determining accurate schedule status.


This article introduces an unnecessary distraction as the author insists on developing his

own algebraic notation for Earned Schedule computations. A reader familiar with Earned

Schedule technique and previous published works is forced to analyze and digest calculations in

a new format to verify consistency in method. The reader would be better served if the author

had used Lipke’s original notation, which, to me, is much simpler and straightforward. Given

that this article was necessitated by a published misunderstanding of ES methods, one would

think that the author would be more than conscientious in maintaining consistency of

presentation.

Regardless of the noted distractions of this and the original article, the author’s

contention that ES can be subject to inaccuracies should not be overlooked, and should be

considered in the overall context of Earned Schedule methodology.

Is “Earned Schedule” an Unreliable Indicator? No, but It’s Not Necessarily the

Premier Indicator for Assessing Schedule Performance

Jacob’s paper (Jacob, 2006), appearing in the same edition of The Measureable News as

Book’s original paper correction (Book, 2006b) provides an alternative perspective on both

Earned Schedule (ES) and on Book’s original critique of ES (Book, 2006a). The author contends

that while Book is mistaken in his analysis of the utility of ES, he states “I am not a proponent of

the ES method, not because of its unreliability or lack of integrity, but because it is unnecessarily

complicated to use and requires more work without adding any tangible benefit to the user.” The

author contends that his method, known as Earned Duration, provides a simpler approach to

obtain essentially the same metrics as ES. The author then addresses in detail two issues with


Book’s paper, the assertion that the ES metrics SV(t) and SPI(t) are not good schedule indicators

and that the ES calculation is algebraically invalid.

The author’s main contention with ES metrics is that any schedule performance index

applied to a control account or other aggregated level for performance prediction is meaningless.

A dissection of Book’s original data (Book, 2006a) is presented to demonstrate how the

summation of EV for a set of tasks leads to a false SPI indication. A behind schedule condition is

postulated where critical path tasks are performing to plan, but by summing these tasks with the

remainder of a project, which may contain behind schedule tasks, an SPI of less than one is

calculated, although the end date is not at risk. Jacob concedes that “Although he [Book]

provides meaningful schedule data, he fails to apply them to reconcile his flawed conclusions.”

The solution presented relies on a well-developed schedule that identifies critical path

activities and available float for other tasks. After stating that “all SPIs are not alike” Jacob

asserts that:

“The CPI can be greater than, less than or equal to 1.0 (1.0 ≤ CPI ≥ 1.0) regardless of whether work is in progress or complete. On the other hand, if SPI is not equal to 1.0 (SPI ≠ 1.0) it can only mean that work is still in progress, either behind schedule (SPI < 1.0) or ahead of schedule (SPI >1.0). If SPI equals 1.0, it could mean one of two conditions: (1) that work is in progress and on plan (BCWP = BCWS)Time Now or (2) that work is complete (BCWP = BAC).”

The question of mathematical validity arises due to Book’s assertion that the integer

component of Earned Schedule is in units of time, while the fractional component is a unit-less

ratio and therefore adding the two quantities is mathematically invalid. Jacob makes the same

point contained in my analysis of Book’s paper, that the fractional component of Earned

Schedule (ES) is a ratio of partial period performance implicitly multiplied by a single time

period, thus ensuring that all components are in the same unit of measure. Jacob then proposes a


more algebraically general form of ES calculation by defining the implicit time period as a

generic quantity, Δt, to present

𝐸𝑆 = 𝑁 + 𝐵𝐶𝑊𝑃 − 𝐵𝐶𝑊𝑆𝑁

𝐵𝐶𝑊𝑆𝑁+ ∆𝑡 − 𝐵𝐶𝑊𝑆𝑁 ∆𝑡

and follows with a detailed application of the formula to three example scenarios for tasks in a

very late, late, and ahead of schedule condition, demonstrating the steps leading to Jacob’s

conclusion of a valid ES value. The author proposes a simpler method for obtaining essentially

the same data using Earned Duration.

Earned Duration is graphically represented in the paper as:

Figure 1 - Graphic Representation of Earned Duration

Using Figure 12 the author relies on geometry and the similarity of triangles to derive the

equation for Earned Duration by using Actual Duration (AD) in the ratios

𝐸𝐷𝐴𝐷

= 𝐸𝑉𝑃𝑉

and expressing the equation in terms of ED yields

𝐸𝐷 = 𝐴𝐷 𝑥 𝐸𝑉𝑃𝑉

= 𝐴𝐷 𝑥 𝑆𝑃𝐼


The equation above is applied to the same example scenarios used to illustrate Earned

Schedule calculations, and the author shows that the Earned Duration values are identical to the

Earned Schedule values in each instance. Jacob concludes the discussion by advising the reader

to pick either as a preferred method, while asserting that the Earned Duration formula is

mathematically simpler.

The author next provides a detailed example of ED applied at the task-level and analysis

of available slack to determine overall schedule status and to calculate an Estimate of Duration

of Completion (EDAC). Two formulas for EDAC are presented, one for an SPI = 1, the other for

a calculated SPI as

𝐸𝐷𝐴𝐶1 = 𝑃𝐷 +𝐴𝐷

1 − 𝑆𝑃𝐼

and

𝐸𝐷𝐴𝐶2 = 𝑃𝐷𝑆𝑃𝐼

The data for this example is included here for reference.

Figure 2 - Earned Duration Example, Jacob

After a detailed step through ED and EDAC based on the data in Figure 13 the author

concludes:


“Thus B would take 2.0 days beyond its originally planned completion (6 – 8), with 1 day of float remaining. Given this result, one can safely state that B’s lateness will not jeopardize the schedule. At present this project is slightly ahead of schedule — certainly not behind as would be indicated by the SPI of 0.93 at the CA level.”

The author proceeds to analyze Book’s original sample data (Book, 2006a) in detail, with

a focus on the sample data’s SPI calculations and structure of the project data. In view of the

overall validity of the original Book data as corrected by the author (Book, 2006b), no detailed

analysis is presented here.

Next follows a concession that a modification of the EDAC formula is required when the

project exceeds its planned end date, merely substituting Actual Duration (AD) for Planned

Duration (PD) in the previously quoted equations.

While this paper presents several interesting aspects on metrics derivation and

interpretation, there appear to be several areas of concern. Among these are a failure to detect the

erroneous Earned Schedule interpretations and calculations in the Book’s original article, the

unnecessary steps to generalize the Earned Schedule formula, the complexity of the Earned

Duration calculation versus Earned Schedule, and the author’s incomplete analysis of his own

example schedule.

In presenting his more than detailed analysis of Book’s original data, at no time did the

author infer that the ES calculations presented were in error. While taking great steps to explain

variances and application of ED, it seems that the data itself was never subject to baseline

verification before analysis. As has been indicated previously, Book published a correction to his

original article that appeared in the same journal as this one.

While purporting to algebraically generalize the calculation of ES, the author seems to

present an exercise of interest only to mathematicians. A fundamental understanding of ES


concepts leads the practitioner to know that proper application of ES depends on a uniformly

timed PMB, and thusly the algebraic concerns about the implied ratio of the ES fraction is, truly,

an academic exercise. It is easily demonstrated that Lipke’s original method, when applied to a

properly phased PMB, provides consistent and accurate results.

On the issue of the mathematical complexity of the Earned Duration versus Earned

Schedule calculations it appears that the author also overestimates his position. At the heart of

the Earned Duration calculations is the Schedule Performance Index (SPI), which the author uses

in its original EVM form asSPI = EVPV

. Since multiple sources have addressed the unreliability

both Schedule Variance (SV) and SPI (Lipke, 2003), (Fleming & Koppelman, 2000) and its

unreliability in the later stages of a project is surprising that the author uses SPI as a basis for a

more reliable metric. The need to adjust equations based on whether a project has exceeded its

baseline end date is only one symptom of problems that may be encountered with this technique.

In examining the example presented by the author for schedule analysis in Figure 13 and

Jacob’s conclusion an interesting situation appears. Based on the author’s calculations and

conclusion Task B will be delayed for two days and retain one day of its original slack, thereby

not jeopardizing the project’s end date. In the author’s words, “If the forecast shows that B’s

lateness would exceed its float of 3 days, a new critical path would result.” In this assertion the

author is only partially correct. The example also shows Task A, the parallel critical path task in

the example, has an SPI of 1.1, indicating an ahead of schedule condition. Using Jacob’s EDAC

equation to forecast Task A’s end date reveals that the task will complete one day early. The net

effect of the analysis would reveal that under the forecasted conditions Tasks B is a parallel

critical path in the project. It seems that unbeknownst to the author, his project’s critical path is

changing before his eyes, undetected by his own instrumentation.


As previously mentioned, this paper presents interesting concepts in Earned Duration,

and its full exploration is outside the bounds of this review. As the technique relates to the

emerging practice of Earned Schedule the author’s only validated point seems to be that applying

an SPI to an aggregate performance metric can be unreliable, a point already aptly made by Book

in his original paper.

Applying Earned Schedule to Critical Path Analysis and More

This paper (Lipke, 2006) was published in the same issue of The Measureable News as

the correction paper Earned Schedule and Its Possible Unreliability as an Indicator: Correction

Note (Book, 2006b). Lipke uses this paper to address concerns that Earned Schedule (ES)

doesn’t provide the granularity necessary for schedule analysis at less than the project level. Two

notable examples included in this paper are the application of ES to monitor the critical path and

to monitor performance of tasks related to an individual cost account.

The paper begins with the requisite brief overview of ES theory and calculation. After the

explanation of the derivation of the ES Schedule Performance Index, SPI(t), a brief review of the

ES-specific equation for Independent Estimate at Completion ( IEAC(t) ) is presented for

discussion of the paper’s examples.

Lipke presents a straightforward method of applying ES to a subset of a project’s tasks,

be they critical path only tasks or tasks for a particular cost account. The method creates a

Performance Measurement Baseline (PMB) comprised only of the tasks of interest. By

determining planned value by performance period for each task, totaling planned value for each

performance period, and summing each performance period successively to derive cumulative

planned value, a specific baseline is developed for Earned Schedule management. While ES


monitoring of a cost account is straightforward and mimics ES analysis at the project level, ES

evaluation of the critical path has an additional component.

For the critical path example a PMB as described above is created for the project and for

the critical path, and the data is presented together. The example metrics illustrate a ten month

duration project that completed in twelve months. In the example, the original critical path tasks

completed in the baseline ten months, a seeming contradiction with a twelve month actual

duration. Lipke compares the performance metrics for the project to the performance metrics for

the critical path. While ES and performance indices show that the critical path is on schedule,

overall project duration is forecasted as being late. The explanation is that due to project

performance and dynamics, the actual critical path shifted at some point in the project. The

contradictory message in these indicators serves as a warning flag to the project manager that a

detailed schedule analysis is in order to ensure proper representation of the critical path tasks.

Lipke indicates that this paper is a detailed response to often-asked questions about the

utility of Earned Schedule. Certainly the idea and concept are relatively simple and

straightforward. The astute reader, noting Henderson’s approach of transferring Microsoft

Project PMB data to an Excel spreadsheet for manipulation (Henderson, 2003) can easily see the

utility of a similar technique utilizing a specific subset of project tasks.

The timing of this article, coming on the heels of Book’s correction note cited earlier and

appearing in the same issue, provides an interesting overtone of academic debate left for the

reader’s analysis and reflection.


A Simulation and Evaluation of Earned Value Metrics to Forecast the Project

Duration

In this paper (Vanhouckel & Vandevoorde, 2007) an extensive set of simulated projects

is used to test three methods of schedule performance monitoring and prediction, Earned Value

(EV, referred to as Planned Value, or PV, in this study), Earned Duration (ED), and Earned

Schedule (ES) for forecast accuracy, effects of project network type, and the effect of the stage

of project completion on the behavior of forecast metrics. A set of 3,100 project networks each

with 30 activities was generated with controlled variation of network topology. Each project was

subjected to uncertainty during execution using Monte Carlo principles to create nine different

execution scenarios as shown in the matrix in Figure 14. In the matrix project activities are

defined as critical or not-critical and are shown as either behind (-), on plan (0), or ahead of

plan(+). The matrix columns indicate a behind schedule, on schedule, and ahead of schedule

project condition respectively.

Figure 3Vanhoucke and Vandevoorde, 9 Simulation Scenarios


The authors present three variations of duration estimation based on Anbari’s work

(Anbari, 2003) for use under different anticipated performance conditions for the remainder of a

project as

EAC(t)PV1 = PD − TV when the duration of remaining work is as planned

EAC(t)PV2 = PD/SPI when the duration of remaining work follows the current SPI trend

EAC(t)PV3 = PD/SCI when the duration of remaining work follows the current SCI trend

Where EAC(t) is the Time Estimate at Completion, PD is the Planned Duration, SPI is

the Schedule Performance Index as defined by the individual method, and SCI is the Schedule

Cost Ratio (the product CPI * SPI, more commonly referred to as the Critical Ratio).

Results for forecast accuracy are shown in Figure 15 as the calculation of the Mean

Absolute Percentage of Error (MAPE) for each overall simulation scenario. These results

indicate that the ES method provides the best predictive performance, supporting earlier research

(Vandevoorde & Vanhoucke, 2006).

Figure 4- Vanhoucke and Vandevoorde, Accuracy Simulation Results

When projects finish on schedule it is shown that the ED method provides the more accurate

prediction. For late finishing projects ES again provides the best results, except for in Scenario 7

where ED provides the best results. Note that this scenario is based on conditions that create a

false SPI, an error which influences the ED results for the better due to the well understood


inaccuracy of EVM schedule metrics in the latter stages of a project. The authors observe that

when SCI is used as a component of a predictor equation for any of the three methods that poor

results are obtained, and therefore the use of SCI (the Critical Ratio) is not warranted.

An important result of the study is the analysis of the effect of a project’s stage of

completion on the accuracy of schedule forecasts. The authors analyze two separate simulation

scenarios, one for early project finish (results shown in Figure 16) and one for late finish (shown

in Figure 17). The results again show that the ES method provides the best prediction in almost

all cases regardless of project completion point, with the accuracy of the ES method improving

in the latter stages project execution. Noteworthy again is the poor metrics performance when

SCI is used for forecasting.

Figure 5 - Vanhoucke and Vandevoorde, Percent Complete Simulation Results for Ahead of Schedule Projects


Figure 6 - Vanhoucke and Vandevoorde, Percent Complete Simulation Results for Late Projects

In analyzing the effects of project network topology on the performance measures the

authors find that there is a definite influence on forecast accuracy. As projects become more

serial (e.g. fewer parallel tasks) prediction accuracy improves for all methods examined. This

performance increase is attributed to the decreasing probability of non-critical path tasks

masking true schedule prediction. Once again the authors note that the use of SCI as a

forecasting metric is not warranted based on the demonstrated results of the study.

In summarizing their findings based on analysis of the data presented, the authors

conclude that “The results reveal that the ES method outperforms, on the average, all other

forecasting methods. The closeness of a network to a serial or parallel network directly

influences the activity slack and has an impact on the accuracy of the forecasts.”

The results of this study overall indicate both unexpected and what could be considered

as intuitively obvious findings. The apparent unreliability of forecast methods when using SCI

(Critical Ratio) provides an interesting observation. While SCI is used as an indicator of overall

project performance, the seemingly well-known performance problems with SPI, and thus its

inevitable contribution to inaccuracy in any derivative measure as noted in the literature review

of Jacob (Jacob, 2006) in this paper, certainly supports the findings of the current study. As a


counterpoint, the present study indicates that neither Jacob (Jacob, 2006) nor Lipke (Lipke,

2003) utilized SCI as a duration forecast equation as analyzed in the study, but the application of

the forecast technique to both Earned Duration and Earned Schedule were based on the author’s

implementation from Anbari’s EV approach (Anbari, 2003). Noteworthy too is the authors’

statements that detailed cost performance analysis was not the focus of this paper, so further

consideration to permutations of cost performance influence on these equations would be in

order.

When focusing on deriving a true time-based measure of schedule performance as the

key pursuit, a finding that Earned Schedule provides “on average” the best performing method to

forecast project duration is significant. The permutations of the test data presented in the current

study and the objective statistical analysis of performance of the methods examined provide a

measure of validation to multiple preceding studies.

Prediction of Project Outcome: The Application of Statistical Methods to Earned

Value Management and Earned Schedule Performance Indexes

The authors of this paper (Anbari, Lipke, Henderson, & Zwikael, 2009) apply statistical

methods with Earned Value Management (EVM) in an effort “to improve the capability of

project managers for making informed decisions by providing a reliable forecasting method of

the final cost and duration” of a project, noting that a reliable method to forecast schedule

duration at completion is a significant goal. An overview of EVM is presented, along with a

detailed introduction to Earned Schedule (ES). Attention next turns to an overview of statistical

methods.


A review of confidence intervals, the standard normal distribution (Z), and Student-t

distributions are briefly reviewed, followed by an overview of using the logarithms of the

periodic values of the performance indices (CPI or SPI) to approximate a normal distribution and

facilitate calculation of the indices’ standard deviation. Given the small sample size a standard

Finite Population Correction Factor was applied to the equations, which the authors refer to as an

“Adjustment Factor”. A summary of the equations is provided in Table 3.

𝜎 = ��(ln𝑝𝑒𝑟𝑖𝑜𝑑𝑖𝑐 𝑖𝑛𝑑𝑒𝑥 (𝑖) − ln 𝑐𝑢𝑚𝑢𝑙𝑎𝑡𝑖𝑣𝑒 𝑖𝑛𝑑𝑒𝑥)2

𝑛 − 1

Standard Deviation for Periodic Values • n = Number of Samples • i = Sample Number

𝐴𝐹𝑠 = �𝑃𝐷 − 𝐸𝑆

𝑃𝐷 − 𝐸𝑆𝑛 Finite Population Correction Factor(

“Adjustment Factor”) for Schedule

𝐶𝐿𝑠 = ln 𝑆𝑃𝐼𝑐𝑢𝑚 ± 𝑍 ∗ 𝜎√𝑛

𝐴𝐹𝑠

Resulting Confidence Level Equation (Schedule)

• Z = Normal Distribution Confidence Level of Interest

• σ = Computed Standard Deviation • Provides Upper and Lower Bound

IEACtX = PDℯCLs

Resulting Upper / Lower Bound Values • X = H for High Limit, L for Low

Limit • Computed Using the Two

Confidence Levels in Previous Step

Table 1 - Earned Schedule IEAC Confidence Levels

The equations in Table 3 provide the boundaries of interest for schedule Independent

Estimate At Completion values IEAC(t)H, the schedule predicted maximum duration, and

IEAC(t)L, the forecasted minimum schedule duration. These boundaries are tested with the

standard Earned Schedule value IEAC(t) to examine the following four hypotheses:

(1) H1 (cost-high bound): Final cost is less than IEACH. (2) H2 (cost-low bound): Final cost is greater than IEACL. (3) H3 (Schedule-high bound): Final duration is less than IEAC(t)H. (4) H4 (Schedule-low bound): Final duration is greater than IEAC(t)L.


Sample data for the study is taken from a collection of 12 high technology projects

considered to be low risk with little development effort. Planned costs ranged from $291,000 to

$6,077,000, with planned durations ranging from 17 to 50 months. The article contains detailed

metrics from each sample project and a discussion of actual project performance. Five scenarios

evaluated are summarized in Figure 18. Three scenarios were evaluated at a 90% confidence

level containing data starting from the 10%, 30%, and 60% project completion points, with two

scenarios with project data starting from the 10% complete point were evaluated at confidence

levels of 95% and 98%.

Figure 7 - Cost and Schedule IEAC Study Results

The authors conclude that based on these results that the 98% confidence level provides

the most reliable forecast at the risk of possibly overstating the limits of actual performance.

Another significant finding is the increased reliability of the forecasts when using data from the

60% project complete point, which indicates a high degree of confidence for all three confidence

levels. This implies that as a project moves to completion and the performance indices further

stabilize that a lower confidence level may be applied for reliable prediction, with the benefit of

reducing the total range of the prediction.

The authors conclude with a discourse that there are reliable calculations available for

predicting project duration as opposed to detailed schedule network and float analysis. The paper

is cited as providing a demonstration that calculated forecasts are possible and reliable. Previous


work by Henderson is cited (Henderson, 2005) as showing favorable results when comparing

Earned Schedule and Critical Path analysis to predict project completion.

In my opinion this article does provide an important step in determining the viability of

reliable predictions, although it should be viewed with caveats in mind, among them the limited

sample set of projects and single industry focus. The limited set of 12 projects, all from the same

organization, with a maximum single project value of $6M may not translate as readily to

projects in other industries, of much larger size, and to different organizational infrastructures.

Further research seems to be required to validate the study’s results for broader application.

Likewise, all 12 projects in the study were considered high technology projects. While the basic

tenets of the method should apply equally regardless of industry, a broader validation across

project types seems necessary.

While the authors focused on analyzing reliable predictions for both schedule and budget,

no specific independent focus on the performance of the schedule forecasts was apparent. The

results in Figure 18 indicate that the schedule forecast results outperformed cost forecast results

significantly, and that for all confidence levels and data sets (from 10%, 30%, and 60%

complete) reliable predictions are possible. Reliable schedule prediction from a project’s 10%

complete point seems to be a significant goal and the early performance of the ES completion

forecasts seem to merit additional study and analysis to determine its reliability. The authors do

note that in the sample data that schedule seems to have been a priority over cost, and this could

certainly contribute to the unusually reliable schedule predictions in the small sample set under

consideration.


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.

Anbari, F. T., Lipke, W., Henderson, K., & Zwikael, O. (2009). Prediction of project outcome:

The application of statistical methods to earned value management and earned schedule

performance indexes. International Journal of Project Management, 27(4), 400-407. doi:

10.1016/j.ijproman.2008.02.009

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.

Christensen, D. S. (1998). The costs and benefits of the earned value management process.

Defense Acquisition Quarterly, Fall 1998.

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. (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. (2009). Earned schedule (First ed.): Lulu Publishing.

Project Management Institute. (2005). Practice standard for earned value management.

Newtown Square, PA: Project Management Institute, Inc.


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

Vanhouckel, 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.

earned schedule literature review - billy mowery schedule literature review...the analysis and...

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