Want to Enhance Your Project Performance Metrics? Guide to EVM and Control Charts

Abhishek Soni

Want to know how your process improvement project is progressing? Here’s how to use Earned Value Management and Control Charts to monitor how well you’re doing.

Earned Value Management (EVM) is a project management technique for measuring project performance and progress. It integrates scope, cost and schedule measures to assess the performance of a project. Based on the three measures a baseline cost and schedule is developed and actual performance is measured w.r.t to the baseline values.

The following are the key measures and performance indexes of the EVM technique:

Planned Value (PV): This is the authorized budget allocated to the work to be accomplished for an activity. It is also known as budgeted cost of work scheduled (BCWS)

Earned Value (EV): This is the value of work performed expressed in terms of the approved budget assigned to work for an activity. It is also known as budgeted cost of work performed (BCWP).

Actual Cost (AC): This is the actual cost incurred in accomplishing the work performed for an activity. It is also known as Actual Cost of Work Performed (ACWP).

Schedule Variance (SV): Schedule variance is calculated as SV = EV –PV

Cost Variance (CV): Cost variance is calculated as CV = EV-AC

Schedule Performance Index (SPI): This represents efficiency of the time utilized on the project. It is calculated as measure of progress achieved compared to progress planned for a project.

• SPI < 1 means project is behind schedule
• SPI = 1 means project is on schedule
• SPI > 1 means project is ahead of schedule

Cost Performance Index (CPI): This represents the efficiency of the resource (cost) utilized on the project. It is calculated as measure of value of work completed compared to the actual cost or progress made on the project.

• CPI < 1 means project is over budget
• CPI = 1 means project is on budget
• CPI > 1 means project is under budget

Critical Ratio (CR): This indicator combines both the cost performance index (CPI) and schedule performance index (SPI) to represent the project status. This indicator takes care of cost and schedule trade-offs.

• CR < 1 means poor project performance
• CR = 1 means project performance is on target.
• CR > 1 means good project performance

Limitation of EVM indexes

EVM indexes are point estimates; they represent the performance of the project at a particular reporting instance. They do not provide information about project performance over a period of a time. They do not capture the trend of project performance.

Control Charts

Control Charts are a quality tool which displays process data over time against the process control limits. Control limits define the area three standard deviations (by default) on either side of the centerline, or mean, of data plotted on a control chart where expected variation is observed.

There are three basic components of a control chart:

  1. Centerline representing the mean value of the data points
  2. Horizontal border lines, Upper Control Limit (UCL) and Lower Control Limit (LCL) that define the limits of common cause variations
  3. Data points plotted over time

A control chart is used to determine whether a given process is stable (contains only common cause variation) or it is subjected to special cause variation. See below for an example of a control chart:

Figure 1: A control chart displays process data over time

There are two types of variation:

  1. Common Cause Variation (or Controlled Variation): These variations are present in the process due to its inherent nature. These are predictable and expected variations.
  2. Special Cause Variation (or Uncontrolled Variation): These variations are introduced in the process by non –random events /factors external to process. If special cause variation is present in the process then process is said to be in unstable state.

EVM indexes and control charts

Among all the EVM indexes, critical ratio is the only metric which captures the essence of both the cost and schedule measures of the project and succinctly describes the project status at any point of time.

Critical ratio, when plotted as control chart over project duration, will reveal following additional insights:

  • Indicates whether project performance is stable or not over project duration.
  • Predicts future performance from a stable project performance.
  • Indicates whether project has experienced special cause variation during the project duration.
  • Indicates any special trend or pattern observed in project performance.

Example of EVM index and Control Charts in Practice

Critical ratio in conjunction with a control chart can be used to gather greater insight into project performance. The critical ratio is used as a performance measure index for different kinds of project across industry.

The illustration below is from software industry.

A typical software development lifecycle project consists of the following phases: Requirement gathering, Design, Development, Testing, Implementation and Support. One possible way of grouping the project performance data is by project phase (other possible alternatives could be grouping the data by month or quarter). Use X bar and S control chart to plot the critical ratios (continuous data) over the project duration.

Interpret the outcome of the control chart as follows:

If all the data points are within the control limits and no special data trend/pattern is observed then the project performance is said to be stable across different phases of the project and future project performance can be predicted.

Figure 2: Control chart showing a stable process

If data point(s) lie outside the control limits or unexpected data trend/pattern is observed then special cause variation is present in the process and this variation will result in varying performance between different phases of the project.

Conduct root cause analysis to identify the sources of special cause variation; if these sources are boosting the critical ratio for certain phase(s) of the project then replicate the best practices of this phase to entire project or else if it is deteriorating the critical ratio then make provisions to remove the sources of special cause variations from the project.

For example, control chart of sample mean for project A shows an unexpected high critical ratio (data point outside upper control limit) for data sample 2 (i.e. during design phase of the project) .This suggests that there might be some best practices followed for project management during design phase of the project which has resulted in excellent critical ratio.

Figure 3: Control chart showing positive variation in the process

The control chart of sample mean for project B, shows an abnormal low value of critical ratio (data point outside lower control limit) for data sample 2 (i.e. during design phase of the project). This suggests that project management was not efficient during the design phase of the project B and corrective action needs to be taken.

Figure 4: Control chart showing negative variation in project performance


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