Issue Identification and Appraisal
Blog 15 discussed the tracking and reporting Cost Performance Index (CPI) and Schedule Performance Index (SPI) and the graphical choices, which brings out another dimension – the results and how close they are to the baseline, or was the baseline flawed. This week’s blog problem statement is, “With the CPI/SPI results obtained to week 15, what classification was the original baseline?”
Review of the Guild of Project Controls Compendium and Reference (GPC) Module 9.4 ‘Assessing & Interpreting Progress Data’ provides three feasible alternatives.
Develop the Outcomes for each
The GPCCAR Module 9.4 figure 9, demonstrates that there are several options regarding the feasible alternatives, and that a combination of two or better still all three would be beneficial.
Figure 1 – Precise
Figure 1 shows that the CPI/SPI points are consistently ‘hitting’ the target and within the acceptable range, which demonstrates that the baseline estimate was Accurate, Precise and Reliable to be used in any future projections.
Figure 2 – Imprecise
Figure 2 shows that the CPI/SPI points are scattered over the target and therefore imprecise, which demonstrates that the baseline estimate was not Accurate or Reliable and should not be used in any future projections.
Figure 3 – Precise and Accurate with some outliers
Figure 3 shows that many the CPI/SPI points are ‘hitting’ the target but a few of the outlying points need to be assessed as to why they were outside. In such cases the Project Controls professional could apply statistical process control chart analysis to discard these outlying points that are outside of the +/- 3 Sigma range as being “special and identifiable”. Use of these values without performing any statistical process control would be risky.
Figure 4 – Precise and Reliable but not Accurate
Figure 4 shows that the CPI/SPI points are ‘hitting’ the target in a precise group but outside of the bulls-eye. It shows that the data is precise and reliable, but needs to be adjusted to bring the points back to the center. If adjustments were made, then the data could be used in future projections. Adjustments would mean looking at the cost and time estimates and assessing what needs to be done to gain alignment.
Table 1: Summary of Data Point Dispersions
The criteria selection for both models will be taken from W05 through W15 weekly reports. Table 2 below shows the information to be used.
Table 2: CPI & SPI Data points for use in Analysis
Analysis and Comparison of the Alternatives
The data points from table 2, were input to the “Target” Cost/Schedule Trend chart to provide the following graphical output.
Figure 5: Cost/Schedule Trend Report
Review of the data, shows four data-points outside of the +/- Sigma 3 range. As mentioned in the figure 3 explanation these four points needs to be assessed to determine why they fell outside to ensure there is a valid explanation to discard them.
Figure 6: Zoom-in View
To understand the reasons for the outlying points we need to determine which week these occurred and check the report analysis for explanations.
Table 3: CPI & SPI Data points outside +/- 3 Sigma limits
Table 3 shows the four data-points that need reviewed to determine why they fell outside the limits. Of the four points, three are schedule related only, and one is to cost and schedule.
The first was in W06 and was due to missing final 3 activities in the Paper 1. In W08 the trend had reversed and schedule and cost were showing they were well ahead. This was due to the budget being inflated, this was subsequently revised in W09. In W10 schedule was showing it was ahead but due to the papers going better than anticipated because of working smart, and not following the template set out by Dr. Paul Giammalvo. W13 the schedule was back on the radar as paper 4 was progressing good due to research being done on blogs prior to commencing the paper thus reducing the development time.
Figure 7: Data Points without Outlying points
Therefore, our initial scenario was like the “Precise and Accurate, with Outliers. Once these outlying data points are discarded the other points are dispersed within the +/- Sigma 3 ranges.
Selection of Preferred Alternative
To provide a level of confidence in the data points, a statistical control chart will be used. As this requires one set of data points and a time scale, the data points will be based on CPI * SPI.
Table 4: CPI * SPI Data points for Control chart
Inputting the above points into the statistical control chart model provides the output in figure 8. Looking at the chart it tells us the following:
- Most points are close to the average
- There are a few points close to the limits
- There are two points beyond the control limits
Figure 8 : CPI*SPI Statistical Control Chart
As there are a couple of points outside of the control limits these are treated as a “special cause”. These occurred prior to the budget re-alignment, looking at figure 8 it shows that points from W09 onwards are in control and therefore generally acceptable.
With regards the Control budget, it appears it is both precise and accurate and can be used in evaluating future forecasts based on the data points provided.
Monitoring Post Evaluation Performance
As is the case with the other blogs, close monitoring of the period data allows regular evaluations to ensure that the data-point trends are keeping within the Statistical Control limits. If these limits are breached then re-evaluation of the data will be required.
- Guild of Project Controls. (2015, October 3). 9.4 Assessing and interpreting progress data | Project Controls – planning, scheduling, cost management and forensic analysis (Planning Planet). Retrieved September 2, 2017, from http://www.planningplanet.com/guild/gpccar/assessing-interpreting-progress-data
- 3.3.7 Multi-Attribute Decision Making – Guild of project controls compendium and reference (CaR) | Project Controls – planning, scheduling, cost management and forensic analysis (Planning Planet). (2015, October 3). Retrieved August 17, 2017 from http://www.planningplanet.com/guild/gpccar/managing-change-the-owners-perspective
- McNeese / BPI Consulting, LLC, W. (2004, April). Interpreting Control Charts | BPI Consulting. Retrieved from https://www.spcforexcel.com/knowledge/control-charts-basics/interpreting-control-charts