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Quantifying the Cumulative Impact of Change Orders

Quantifying the Cumulative Impact of Change Orders. Rich Camlic Chair Cumulative Change Order Impacts Research Team. 2000 CII Annual Conference Nashville, Tennessee. Quantifying the Cumulative Impact of Change Orders. Cumulative Change Order Impacts Research Team RT 158.

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Quantifying the Cumulative Impact of Change Orders

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  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 33 mechanical contractors. • 59 projects were solicited from 35 electrical contractors. • 116projects in database. • Industrial and institutional projects make up majority of database.

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

  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 = 1 + 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 = 0.36866 + 0.11957 percent change - 0.08065 PM % time on project - 0.16723 % owner-initiated CO - 0.09147productivity - 0.05213overmanning + 0.022345 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. Data Lower CI Upper CI 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% 20% 10% 0% 0% 50% 100% 150% % Change

  22. Data Lower CI (95%) Upper CI (95%) 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% 10% 0% 0% 50% 100% 150% % Change

  23. Data Lower CI (95%) Upper CI (95%) 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% 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.

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