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

Cumulative Change Order Impacts Research Team Tripp AhernJ. F. Ahern Company George ArmenioGeneral Motors Corporation Rich CamlicU.S. Steel, Chair Edward GibbleMcClure Company Brian GriffithsElectrical Corp. of America Hanford GrossGross Mechanical Contractors Awad HannaUniversity of Wisconsin-Madison Kevin HughesFPL Energy Kam KamathBlack & Veatch Chris Lloyd-JonesBechtel Joe Loftus Sr.Terminal-Andrae Inc. Wayne MontgomeryKvaerner Process Greg ThomasFisk Electric Company

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

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.

Results of Research Two models (tools) developedTwo 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.Strong correlation found between the number of change items and some loss of labor productivity.

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.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.Reduce change order processing time to decrease the likelihood of impact. Require contractors to submit a manpower loading curve with proposal.Require contractors to submit a manpower loading curve with proposal.

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

Recommendations to Contractors Resource loading relationships (ratios):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

Methodology Developed a comprehensive questionnaire based on “influencing factors” that we felt could cause change on a project.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.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.The study was based on work-hours.

Contractor Data 57 projects were solicited from 33 mechanical contractors.57 projects were solicited from 33 mechanical contractors. 59 projects were solicited from 35 electrical contractors.59 projects were solicited from 35 electrical contractors. 116 projects in database.116 projects in database. Industrial and institutional projects make up majority of database.Industrial and institutional projects make up majority of database.

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) Total Actual Labor Hours X 100

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.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.Logistic regression techniques then identified the eight most significant variables that impact a project.

Significant Impact Variables Mechanical or electrical projectMechanical or electrical project Percent changePercent change Estimated/actual peak laborEstimated/actual peak labor Change order processing timeChange order processing time OvermanningOvermanning OvertimeOvertime Peak/average work-hoursPeak/average work-hours Percent change orders related to design issuesPercent change orders related to design issues

where X is the sum of the eight significant “influencing factors” (variables) times their coefficients plus a constant The Impact Model (Simplified logistic regression) e x e x Probability Y = 1 + e x

Confidence of Impacted Project.5 does not indicate 50% chance of impact No Evidence Some Evidence Good Evidence Strong Evidence

Significant Variables for Magnitude of Impact Percent change order work-hoursPercent change order work-hours Project Manager percent time on projectProject Manager percent time on project Percent owner-initiated change itemsPercent owner-initiated change items Productivity (tracked or not tracked)Productivity (tracked or not tracked) OvermanningOvermanning Change order processing timeChange order processing time

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

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 % DeltaAll 7 within ± 15 percent of actual % Delta 5 of 7 within ± 10 percent of actual % Delta5 of 7 within ± 10 percent of actual % Delta 4 of 7 within ± 5 percent of actual % Delta4 of 7 within ± 5 percent of actual % Delta This is an indication that our model is a good predictor of the magnitude of impact.

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

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

0% 10% 20% 30% 40% 50% 60% 0%50%100%150% % Change % Delta 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

0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 0%50%100%150% % Change % Delta 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

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.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.Each project is unique and requires that project-specific data be used when applying these models.

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.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.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.

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