IEAC

Incorporating IMS Information Directly into Independent Estimate at Completion (IEAC) Formulas

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Incorporating IMS Information Directly into Independent Estimate at Completion (IEAC) Formulas

“When you need to discuss the schedule, look at the schedule.”

– A Scheduler’s Lament

There are many existing formulas for calculating an Independent Estimate at Complete (IEAC) from earned value data. A recent study of a sample of projects found that the calculated IEACs analyzed at the 25%, 50%, and 75% complete points were not accurate when compared to the final actual cost of work performed (ACWP). The following table lists the thresholds used to assess the accuracy of the IEACs at the different complete points for the sample projects.

Percent CompleteAccuracy Threshold
25%Within +/- 10% of final ACWP
50%Within +/- 7% of final ACWP
75%Within +/- 5% of final ACWP

While working on that study of the accuracy of commonly applied IEAC formulas as well as on a small project as an analyst for a customer, the idea for using data directly from the integrated master schedule (IMS) in conjunction with the cost performance data to create a new IEAC formula emerged.

Using Data Directly from the IMS to Calculate an IEAC

It should be noted that none of the generally used IEAC formulas use data directly from the IMS. The IEAC formulas use data found in the cost performance portion of the earned value monthly reports to customers.

IMS data is only used indirectly in the IEAC formulas. When a task is started and progress updated, the earned value (the budgeted cost for work performed or BCWP) is developed from the progress reported. This is measured against the cost baseline (the budgeted cost for work scheduled or BCWS).

At the same time, in the IMS environment, the schedule analysts are calculating the Baseline Execution Index (BEI) for task completions/finishes. BEI (for finishes) measures how many of the tasks baselined to be completed by the cut-off date were completed. If all the tasks were done (BEI = 1), their value would have been earned. Of course, other tasks could have started, progressed, and maybe even finished. For this example, the Schedule Performance Index (SPI) calculated at that point (BCWP/BCWS) should be at least 1 and potentially higher. The SPI reflects the baseline value of completed tasks plus the in-process claimed baseline value. The in-process claimed value can be subjective in some cases.

The argument, if there were one, might be there is no need to try and include BEI or similar schedule measures in the IEAC formulas since they already include SPI.

However, there is a whole different and unique set of information coming from the IMS that is not currently used in the IEAC formulas. That information is what we chose to call “Duration Performance” and “Realism Ratio.” These are measures of the actual duration for completed tasks and the forecast duration for future tasks.

Calculating Duration Performance

The IMS data includes the baseline number of days assigned to each task as well as the actual number of days to complete each task. If a task is baselined to take 10 days (Baseline Duration = 10) and the task took 15 days to complete (Actual Duration = 15) then it is taking 150% of baseline to do the work.

This is similar to the Cost Performance Index (CPI) that uses the BCWP and the ACWP to determine how efficient the work performance has been. The formula BCWP/ACWP shows how the work accomplished compares to the cost of that work performed.

If we assume, for labor at least, that taking longer to complete a task often leads to costing more than baselined, we can use the Duration Performance to develop an IEAC.

To develop the Duration Performance, we would use the IMS from the month being analyzed to perform the following actions:

  1. Filter out all summary tasks and look only at real work tasks.
  2. Decide what to do with level of effort (LOE) – keep it or ignore it.
  3. Filter for all tasks that are completed (100% complete).
  4. Add up the baseline duration in days for all these completed tasks.
  5. Add up the actual duration days for these same completed tasks.
  6. Compare the actual duration days used to the baseline duration days.

An example would be:

  • 100 completed tasks
  • Total baseline days duration = 1,000
  • Total actual days duration = 1,500
  • Duration Performance = 1,000 / 1,500 = .67

One of the common IEAC formulas is the “SPI times CPI” that is calculated like this: ACWP + Budgeted Cost of Work Remaining (BCWR) / (CPI x SPI) where BCWR = Budget at Completion (BAC) – cumulative to date BCWP.

Now that we have a duration performance factor, we can develop a new IEAC. The Duration Performance IEAC would be done using the CPI from the same month as the IMS where ACWP + BCWR / (CPI x Duration Performance Index).

Using some actual data from a project for a single month we see:

  • Duration Performance Index = .82
  • BEI = .72
  • CPI = .92
  • SPI = .94 (significantly higher than the BEI)
  • ACWP = $9.2M
  • BCWR = $18.3M
  • IEAC using standard formula with CPI x SPI = $9.2 + $18.3 / (.92 x .94) = $30.3M
  • IEAC (Duration Performance) = $9.2 +$18.3 / (.92 x .82) = $33.5M

Assessing the Realism Ratio

When we look at the remaining tasks to be completed, we can use the Realism Ratio to assess how the future forecast durations compare to the performance so far.

The data needed are the baseline duration and the forecasted duration for all tasks that have not been started. This concept excludes in-process tasks. In our example from before, the data we created looked like this:

  • 100 completed tasks
  • Total baseline days duration = 1,000
  • Total actual days duration = 1,500
  • Duration Performance = 1,000 / 1,500 = .67

We would use the same IMS to do this:

  1. Filter out all summary tasks and look only at real work tasks.
  2. Decide what to do with LOE – keep it or ignore it.
  3. Filter for all tasks that are not started.
  4. Add up the baseline duration in days for all these tasks not started.
  5. Add up the forecasted duration days for these same tasks not started.
  6. Compare the forecasted duration days to the baseline duration days.

Let’s say there were 100 tasks not started. If the forecasted days were 1,000 and the baseline days were 1,000 that would yield 100%. When we did the example, the Duration Performance was .67. This means that performance to date was .67 but the future will be 100% or 1. You can see the disconnect. That disconnect we call the Realism Ratio (in this example, .67/1).

Data from the actual project for the same month as discussed earlier shows:

  • Duration Performance = 122% of baseline
  • Future Performance = .86 or 86% of baseline.

This means that the future durations are cut significantly.

We would use this data to develop a factor called a Realism Ratio (86/122 = .70) and that would be used to develop an IEAC using this formula: IEAC (Realism Ratio) = ACWP + BCWR / (CPI x Realism Ratio).

Using the same sample project data from above and adding in an assessment of the forecasted durations for the remaining work, we see:

  • Duration Performance = .82
  • BEI = .72
  • CPI = .92
  • SPI = .94 (significantly higher than the BEI)
  • ACWP = $9.2M
  • BCWR = $18.3M
  • Realism Ratio = .70
  • IEAC using standard formula with CPI x SPI = $9.2 + $18.3 / (.92 x .94) = $30.3M
  • IEAC (Duration Performance) = $9.2 +$18.3 / (.92 x .82) = $33.5M
  • IEAC (Realism Ratio) = $9.2 +$18.3 / (.92 x .70) = $37.6M

The project is not complete, so the final ACWP position is not known. There is a dramatic difference between the three IEACs. The difference between BEI and SPI indicates that in-process tasks and other factors such as LOE are potentially affecting SPI.

What can we learn from this sample project?

In this example, additional investigation is warranted. There are potential issues with the realism of the baseline and current schedule that are signaling a cost growth issue is likely to occur. Relying on just the time-phased cost data for IEAC calculations may not be sufficient to assess whether a contractor’s range of EACs included in their monthly cost performance reports are realistic. For more discussion, see the blog on Maintaining a Credible Estimate to Completion (EAC) and the blog on Using EVM Performance Metrics for Evaluating EACs.

Are there lurking cost growth surprises in your projects? You may want to consider revisiting your estimate to complete (ETC) and EAC process to verify there is an integrated assessment of the schedule and cost data to identify potential disconnects. H&A earned value consultants can provide an independent assessment of the quality of the data as well processes and procedures to help you verify your EACs are realistic. Call us today at (714) 685-1730.

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Comparing the Efficacy of Independent Estimate at Completion (IEAC) Methods Using Real Project Data

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Comparing the Efficacy of Independent Estimate at Completion (IEAC) Methods Using Real Project Data

“Data! Data! Data!” he cried impatiently. “I can’t make bricks without clay.”

-Sherlock Holmes, The Adventure of the Copper Beeches

There are many discussions about EACs and evaluating EACs including using Independent EAC (IEAC) formulae to compare with the contractor EACs. With good reason, we should wonder how accurate are those IEACs that we use so often and sometimes make decisions based on them. Are we misjudging contractor’s EACs based on formulae that are weak or inappropriate?

Humphreys & Associates has initiated a study to determine how accurate IEACs are, and we would like your help. The study will compare different IEAC formulae against the Program Manager (PM) most likely EAC at the 25, 50, and 75 percent complete point for completed projects. The objective is to assess how closely the IEACs and PM most likely EAC were able to predict the final cost outcome for the project.

How Accurate are IEAC Formulae?

Many formulae exist for using recorded data from an earned value management system (EVMS) to make independent estimates of the final cost at completion (EAC) for the element in question. The element might be a control account, a Work Breakdown Structure (WBS) element, or even an entire project.

What is not known is how accurate these methods are at forecasting the final actual cost for the project. This study hopes to determine that answer.

Real World IEAC Data

This study was initiated by collecting earned value data from 12 completed projects. We need projects that are completed because, on a completed project, the final actual outcome is known. We collected project data at the 25, 50, and 75 percent complete points. At each of these points, the IEAC formulae were applied to determine how closely they were able to predict the final actual cost outcome for the project. The quest is to learn how the various IEACs performed. Is any one of them more accurate than the others?

From this investigation, any indication of the relative efficacy of the formulae would be used to inform future use of the IEAC methods.

Our Method for Testing IEACs

In general, the IEAC approach is to use existing recognized formulae. We have chosen these IEACs as a starting point:

  • IEAC 1 = BAC/CPIe at the percent point reported. This formula can be stated in words as “the entire project is performed at the same efficiency as experience to date.”
  • IEAC 2 = ACWP + [BCWR/CPI (.5) + SPI (.5)]. This formula uses weighted SPI and CPI which theoretically allows for sensitivity to both cost and schedule historical performance. The weights used in this application are even at .50 and .50.
  • IEAC 3 = ACWP + [BCWR/CPI x SPI]. This formula uses the SPI and CPI multiplied together which theoretically allows for sensitivity to both cost and schedule performance to date.
  • IEAC 4 = ACWP + BCWR. This formula assumes the remaining work will be done as budgeted with no factoring.

One additional non-traditional IEAC will be used.

IEAC 5 = Use of IEAC 2 weighted SPI and CPI but decreasing the proportion applied to the SPI as the percent of project completion increases. In other words, the impact of schedule performance diminishes as the project becomes closer to completion.

We will also take the average of all the formulae to see how that works.

Initial Data Set

One aerospace contractor and one US Government agency have provided the required data for 12 completed projects with an interest in the outcome of the study. The source of the data and the specific projects will not be disclosed in the study.

These real-world projects did not have an exact 25%, 50%, or 75% dataset. The closest dataset to each of those completion percentages was used. One example dataset looks like this (color coding should be ignored):

Example Product Data

How can you help?

We need more project data to gather enough varying project outcomes to make the test realistic. We do not plan to keep the types of projects or products separate but will take all the data we can get and look at them all.

Please consider providing data for the study. We have created an Excel spreadsheet template to help gather project data in a common format for analysis. You can download this template here. Add as many tabs as needed for each project. Send your completed spreadsheet to humphreys@humphreys-assoc.com.

In a separate blog we will outline other help we need to complete the study and to analyze the results.

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