Abhishek Soni
September 17, 20126
This article introduces the concept of earned value management (EVM) indexes, a project assessment technique, and control charts, a statistical tool for monitoring variation in a process, and describes how both may be used in tangent to capture more insight from project performance.
EVM is a project management technique for measuring project performance and progress; although not specifically a tool for process improvement, its capabilities make it a good addition to a Lean Six Sigma practitioner’s tool belt. EVM integrates scope, cost and schedule to assess the performance of a project. After baseline costs and a schedule are developed, actual performance is measured with regard to the baseline values. The following are the key metrics and performance indexes of the EVM technique.
SPI = EV / PV
SPI < 1 means the project is behind schedule
SPI = 1 means the project is on schedule
SPI > 1 means the project is ahead of schedule
CPI = EV / AC
CPI < 1 means the project is over budget
CPI = 1 means the project is on budget
CPI > 1 means the project is under budget
CR = CPI * SPI
CR < 1 means poor project performance
CR = 1 means project performance is on target
CR > 1 means good project performance
EVM indexes are point estimates; they represent the performance of the project at a given reporting instance. They do not provide information about project performance over a period of a time and thus do not capture the trend of project performance.
Consider a project to deliver 10 widgets in 10 months at an estimated project cost of $10,000. It is assumed that budget usage and widget production are expected to proceed uniformly over the duration of the project. Also assume that, to measure project performance, the EVM index is calculated at the third and seventh months of the project.
On the third month, the project status is as follows:
The planned value is calculated as:
| PV | = | (total cost of the project apportioned equally over the project duration) * (actual duration of project to date) |
| = | ($10,000 / 10) * 3 | |
| = | $3,000 |
Thus, the actual amount of work delivered (expressed in dollars) is:
| EV | = | (number of widgets delivered/total number widgets to be delivered) * (total cost of the project) |
| = | (2 / 10) * $10,000 | |
| = | $2,000 |
On the seventh month, the project status is as follows:
The planned value is calculated as:
| PV | = | (total cost of the project apportioned equally over the project duration) * (actual duration of project to date) |
| = | ($10,000 / 10) * 7 | |
| = | $7,000 |
The actual amount of work delivered (expressed in dollars) is:
| EV | = | (number of widgets delivered/total number widgets to be delivered) * (total cost of the project) |
| = | (8 / 10) * $10,000 | |
| = | $8,000 |
The table below summarizes these calculations, as well as provides the SPI, CPI and CR for the project.
| Project Performance Snapshot | |||||||
|
Month |
PV |
EV |
AC |
SPI = |
CPI = |
CR = |
Project Performance |
|
Three |
$3,000 |
$2,000 |
$4,000 |
0.66 |
0.5 |
0.33 |
Bad |
|
Seven |
$7,000 |
$8,000 |
$6,000 |
1.14 |
1.33 |
1.52 |
Good |
In contrast to EVM indexes, control charts display process data over time against process control limits. Control limits define the area of three standard deviations (by default) on either side of the centerline, or mean, of data plotted on a control chart wherein expected variation is observed. There are three basic components of control charts:
Figure 1: Standard Control Chart

A control chart is used to determine whether a process is stable (contains only common cause variation) or if it is subject to special cause variation. Common cause variation is the predictable and expected variation present in the process due to its inherent nature. Special cause variation is variation introduced in the process by nonrandom events or factors external to the process. If special cause variation is present in the process, then the process is said to be in an unstable state.
Among all of the EVM indexes, the CR is the only metric that captures the essence of both cost and schedule measures of the project and succinctly describes the project status at any point in time. Ideally the CR of the project should be one, but in practice the CR varies over the project duration, as was seen in the widget example above. A control chart of the CR will capture the variation in the ratio, as well as:
Critical ratios in conjunction with control charts can be used to gather greater insight into project performance. Critical ratios are used as a performance measure index across industries. The following example of using control charts to monitor the CR is from the software industry.
A typical software development lifecycle project consists of the following phases: requirements gathering, design, development, testing, implementation and support. One way to group the project performance data is by project phase (another option would be to group the data by month or quarter). Use Xbar-S control charts to plot the CRs (continuous data) over the project duration. Interpret the outcome of the control chart as follows:
Figure 2: Critical Ratio Control Chart

For example, the Xbar-S chart in Figure 3 shows an unexpectedly high CR (data point outside UCL) during the design phase of Project A. This suggests that there might be some best practices being followed by project management in the design phase that have resulted in an excellent critical ratio. Those practices should considered for dissemination throughout all phases of the project.
Figure 3: Project A – High Critical Ratio

Conversely, the Xbar-S chart for Project B shows an abnormally low value of the CR (data point outside LCL) during the design phase (Figure 4). This suggests that project management was not efficient during that phase of the project and corrective action needs to be taken.
Figure 4: Project B – Low Critical Ratio

EVM can be used to monitor the progress of a project – any type of project including Lean Six Sigma improvement projects – in particular the use of the key measure of CR. But because the CR only provides a snapshot of performance at a given time, combining the use of a control chart with the CR allows project teams to monitor the variation of the CR over time and identify problems to solve or high points to capitalize on.
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Comments
Interesting use of ratios….Is this as relevant for projects that delivered in 3 months and less?
Hello Chris,
Thanks for your interest in my article. The project with longer duration will certainly be in better position to employ this tool for project tracking because of the abundance of data points .But in case projects of very short durations I would leave the decision of employing this tool for project tracking to the wisdom of individual project managers. One needs to keep a balance between the net project worth vis a vis cost of tracking the project.
Regards,
Abhishek Soni
Dear Author
Hello,
Thank you for providing the paper entitled “Manage Project Performance with EVM and Control Charts”. I read the paper carefully, and I would like to draw your attention to a few points.
Using statistical process control to improve earned value project management is an attractive and practical area, so, this aspect of your work is appreciated. But some significant problems is seen in your paper as follows:
1. Normality
As we know, Xbar-S control chart uses for data that have a Normal distribution. If this is not taken into account, control limits are strongly influenced. Many studies (especially Walter Lipke’s studies) have shown that EVM indices including SPI, CPI, SPI(t), CR and etc. do not follow a normal distribution and have a right-skewed distribution! For more detail you can refer to Earnedschedule.com. Therefore we can
2. Sample Size
As we know, projects are finite, they have a start and an end. During the project control using earned value management method, for each period we have only an index! i.e. n=1, so, how did you use Xbar-S control chart, while there is only one observation, for example at period 2 we have just one SPI, one CPI, and etc.
Of course there are other significant problems. Therefore it is not really simple to incorporate SPC and EVM.
I am a graduate student working on my thesis entitled “Using statistical process control to improve earned value project management”, pleases feel free to contact me if I am not correct.
Regards,
Taha Mortaji
Mortaji@ymail.com
Taha, I am interested in hearing about your recommendations for use of statistical process control to improve EVM for project management.
Dear Taha,
I appreciate your interest in my article and I would like to clarify your concerns raised in above comment.Issue of Normality: The assumption of normal data is relevant only to individual moving (I-MR) range control chart while control charts which have sample size greater than 10 such as Xbar and S does not mandate that the data should be normal. Hence I have deliberately suggested to utilize Xbar & S control chart to plot CR (critical ratio) so that even if the CR data is not normal there is no impact on control limits.Further Walter Lipke’s article does make references to SPI,CPI but it does not talk about the behavior of critical ratio (CR) .In Walter’s article the Cost Ratio is referred to as CR while in this article CR refers to critical ratio and not to cost ratio.
Sample Size: The sample size for the Xbar and S chart depicted is not 1.The article suggests that a set of critical ratios should be collected for each phase of the project (Please refer the following line from the article “One way to group the project performance data is by project phase (another option would be to group the data by month or quarter). Use Xbar-S control charts to plot the CRs (continuous data) over the project duration”) Data point for each phase in control chart does not represent individual critical ratio but represents central value for each sample of critical ratios ( i.e for a set of critical ratios collected for a particular project phase) . Example the single data point in the Xbar chart for design phase represents average value of critical ratios collected for design phase and similarly data point in S chart represents standard deviation of critical ratios collected for design phase.I hope the above explanation would have addressed your concerns.
Regards,
Abhishek Soni
Dear Taha,
I appreciate your interest in my article and I would like to clarify your concerns raised in above comment.
Issue of Normality: The assumption of normal data is relevant only to individual moving (I-MR) range control chart while control charts which have sample size greater than 10 such as Xbar and S does not mandate that the data should be normal. Hence I have deliberately suggested to utilize Xbar & S control chart to plot CR (critical ratio) so that even if the CR data is not normal there is no impact on control limits.
Further Walter Lipke’s article does make references to SPI,CPI but it does not talk about the behavior of critical ratio (CR) .In Walter’s article the Cost Ratio is referred to as CR while in this article CR refers to critical ratio and not to cost ratio.
Sample Size: The sample size for the Xbar and S chart depicted is not 1.The article suggests that a set of critical ratios should be collected for each phase of the project (Please refer the following line from the article “One way to group the project performance data is by project phase (another option would be to group the data by month or quarter). Use Xbar-S control charts to plot the CRs (continuous data) over the project duration”)
Data point for each phase in control chart does not represent individual critical ratio but represents central value for each sample of critical ratios ( i.e for a set of critical ratios collected for a particular project phase) . Example the single data point in the Xbar chart for design phase represents average value of critical ratios collected for design phase and similarly data point in S chart represents standard deviation of critical ratios collected for design phase.
I hope the above explanation would have addressed your concerns.
Regards,
Abhishek Soni