1. Quantifying the Cumulative Impact of Change Orders
Rich Camlic
Chair
Cumulative Change Order Impacts Research Team
2000 CII Annual Conference
Nashville, Tennessee

2. Quantifying the Cumulative Impact of Change Orders
Cumulative Change Order Impacts Research Team
RT 158

3. Cumulative Change Order Impacts Research Team
Tripp Ahern J. F. Ahern Company
George Armenio General Motors Corporation
Rich Camlic U.S. Steel, Chair
Edward Gibble McClure Company
Brian Griffiths Electrical Corp. of America
Hanford Gross Gross Mechanical Contractors
Awad Hanna University of Wisconsin-Madison
Kevin Hughes FPL Energy
Kam Kamath Black & Veatch
Chris Lloyd-Jones Bechtel
Joe Loftus Sr. Terminal-Andrae Inc.
Wayne Montgomery Kvaerner Process
Greg Thomas Fisk Electric Company

4. Problem Statement
Administration boards and courts recognize that effects of cumulative impact can go beyond the initial change itself.
It is difficult for owners and contractors to agree that cumulative impact exists, let alone come to an equitable adjustment for it.

5. Research Objectives
1. Investigate how change orders impact productivity over entire project.
2. Isolate specific, measurable characteristics of impacted projects.
3. Develop a model capable of identifying projects impacted by cumulative change.
4. Develop a model to predict the magnitude of cumulative impact with a reasonable level confidence.

6. Results of Research Two models (tools) developed
Determine the probability of impact within a range of possible outcomes.
Predict the probable magnitude of impact within a range of possible outcomes.
Strong correlation found between the number of change items and some loss of labor productivity.

7. Recommendations to Owners
The most common reasons for change orders are Additions, Design Changes and Design Errors, therefore you should do more up-front engineering.
Reduce change order processing time to decrease the likelihood of impact.
Require contractors to submit a manpower loading curve with proposal.

8. Recommendations to Contractors
Integrate any changes into the work flow as efficiently as possible.
Use project software to track productivity:
% complete by earned value
% complete by actual earned work-hours
% complete by actual installed quantities

9. Recommendations to Contractors
Resource loading relationships (ratios):
Actual peak over actual average manpower
Estimated peak over actual peak manpower
Actual manpower loading curve versus estimated manpower loading curve

10. Methodology
Developed a comprehensive questionnaire based on “influencing factors” that we felt could cause change on a project.
Used a pilot study to gather data, to determine how easily the questionnaire could be answered, and if it would be useful in achieving our objectives.
The study was based on work-hours.

11. Contractor Data
57 projects were solicited from mechanical contractors.
59 projects were solicited from electrical contractors.
116 projects in database.
Industrial and institutional projects make up majority of database.

12. Evolution of the Impact Model
Need to develop a definition for “DELTA” (productivity loss/gain) associated with change orders.
Total Actual Labor Hours
(Estimated Hours + Change Order Hours)
X 100
Total Actual Labor Hours

13. Hypothesis Development
75 variables were investigated using hypothesis testing and analysis of variance techniques to determine if they had an impact on projects. All 116 projects were tested.
Logistic regression techniques then identified the eight most significant variables that impact a project.

14. Significant Impact Variables
Mechanical or electrical project
Percent change
Estimated/actual peak labor
Change order processing time
Overmanning
Overtime
Peak/average work-hours
Percent change orders related to design issues

15. The Impact Model (Simplified logistic regression)ex
Probability Y = ex
where X is the sum of the eight significant “influencing factors” (variables) times their coefficients plus a constant

16. Confidence of Impacted Project
.5 does not indicate 50% chance of impact
0.0
0.25
0.5
0.75
1.0
No Evidence
Some Evidence
Good Evidence
Strong Evidence

17. Significant Variables for Magnitude of Impact
Percent change order work-hours
Project Manager percent time on project
Percent owner-initiated change items
Productivity (tracked or not tracked)
Overmanning
Change order processing time

18. The Quantification Model
% Delta = percent change
PM % time on project
% owner-initiated CO
productivity
overmanning
CO processing time
This equation predicts the most likely % Delta (loss/gain of productivity) within a range of possible outcomes.

19. Additional Validation of Model
Seven new projects were solicited after close of research for additional validation of linear regression model:
All 7 within ± 15 percent of actual % Delta
5 of 7 within ± 10 percent of actual % Delta
4 of 7 within ± 5 percent of actual % Delta
This is an indication that our model is a good predictor of the magnitude of impact.

20. What Does All This Mean? Is this an exact science?
Can you use these models with confidence?
What evidence do I have to back this up?

21. PM %Time on Proj & %OwnerInitCO = Ave
No Productivity Tracking & Poor CO Process Time at 95% Confidence Level
PM %Time on Proj & %OwnerInitCO = Ave
Productivity=0, Overman=0, Processing=5
70%
60%
50%
40%
% Delta
30%
Data
Lower CI
Upper CI
20%
10% 0% 0%
50%
100%
150%
% Change

22. Productivity Tracking & Poor CO Process Time at 95% Confidence Level
PM %Time on Proj & %OwnerInitCO = Ave
Productivity=1, Overman=0, Processing=5
60%
50%
40%
% Delta
30%
20%
Data
Lower CI (95%)
Upper CI (95%)
10% 0% 0%
50%
100%
150%
% Change

23. Productivity Tracking & Good CO Process Time at 95% Confidence Level
PM %Time on Proj & %OwnerInitCO = Ave
Productivity=1, Overman=0, Processing=1
45%
40%
35%
30%
25%
% Delta
20%
15%
Data
Lower CI (95%)
Upper CI (95%)
10% 5% 0% 0%
50%
100%
150%
% Change

24. Final Comments
We do not claim, nor should you expect, the “absolute” correct answer, but rather a most likely answer that fits within a range of possible outcomes both above and below our predicted value.
Each project is unique and requires that project-specific data be used when applying these models.

25. Final Comments
We suggest the owner and contractor agree, before a contract is signed, to use these models as a conflict resolution tool, should the need arise, at the end of a project.
Owner and contractor should track actual work-hours against estimated work-hours to detect negative trends early so steps can be taken to correct them before they become a major problem.

26. Implementation Session
Find out how this project works out.
See a demonstration of the model.
Research team members will answer questions.