- Email: [email protected]
RETRACTED: Development of automated change order impact detection and quantification system
Automation in Construction 18 (2009) 525–535
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Automation in Construction j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / a u t c o n
Review
Development of automated change order impact detection and quantification system Min-Jae Lee a, Boong Yeol Ryoo b,⁎, Kenneth T. Sullivan c, Awad S. Hanna d
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a
Dept. of Civil Engineering, Chungnam National University, 220 Gung-dong, Yuseong-gu, Daejeon, 305-764, South Korea Dept. of Construction Science, Texas A&M University, 3137 TAMU, College of Architecture, Texas A&M University, College Station, Texas 77843-3137, United States c Del E. Webb School of Construction, Arizona State University, Tempe, Arizona 85287, United States d Dept. of Civil and Envir. Engrg., Univ. of Wisconsin-Madison, 2314 Engrg. Hall, 1415 Engrg, Dr., Madison, WI 53706, United States
i n f o
Article history: Accepted 5 March 2009 Keywords: Change orders Productivity impact Detection and quantification system
Contents
When change orders occur, adjustments in contract price and duration are usually agreed upon through negotiation or stipulated by contract terms and conditions. There are many pricing methods for change orders impact on productivity loss available, but negotiations using these methods do not always result in a contractor and client agreement. The most difficult issue to agree upon is how to quantify the cumulative impact of change orders on overall construction project. Cumulative impact claims imply that the disruption caused by a single change can have a ripple effect on the entire project, beyond the change itself. In order to recover cumulative impact damages, by law, the contractor is required to prove three basic components: liability, causation, and resultant injury (quantum). Of these elements, causation and resultant injury are the most difficult to prove. There has been some research performed to quantify the cumulative impact of change orders on labor productivity. CII and the University of Wisconsin have performed the most recent and extensive study related to this issue. This study used previous CII-Hanna research models to develop an automated system for the detection and quantification of change order impacts. In addition, a project demonstration is included to allow easy understanding for new system users. The developed system provides a simple guide for stakeholders to expedite the resolution of change order related disputes between contractors and owners by providing causation and resultant injury(quantum) analysis on troubled projects. © 2009 Published by Elsevier B.V.
Introduction . . . . . . . . . . . . . . . . . . . . . Literature review . . . . . . . . . . . . . . . . . . Change order impact detection model . . . . . . . . . Change order impact quantification model . . . . . . Automated impact detection and quantification system Case study for automated system application . . . . . 6.1. Was this project impacted by change orders? . . 6.2. How much is the impact loss? . . . . . . . . . 7. Summary and conclusion . . . . . . . . . . . . . . . Acknowledgement . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . .
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1. Introduction
Numerous project influences can initiate change orders such as: additions or deletions in project scope; changes in codes, laws, or standards; design errors, or optimization; project planning deficien-
⁎ Corresponding author. E-mail addresses: [email protected] (M.-J. Lee), [email protected] (B.Y. Ryoo), [email protected] (K.T. Sullivan), [email protected] (A.S. Hanna). 0926-5805/$ – see front matter © 2009 Published by Elsevier B.V. doi:10.1016/j.autcon.2009.03.002
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cies; incomplete design documents; workers or material availability limitations; unknown site conditions; schedule compression; or unexpected weather problems. In addition, construction contracts include change clauses that authorize the project owner to alter the work performed by the contractor with an equitable adjustment mechanism to alter the contract price and duration [1]. The adjustments in contract price and duration are usually agreed upon through negotiation or stipulated by contract terms and conditions. There are many pricing methods for change order impact on productivity loss available, but negotiations using these models do
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between contractors and owners by providing causation and resultant injury (quantum) analysis on troubled projects, specifically in regards to electrical and mechanical construction.
2. Literature review A number of studies have attempted to quantify the impact of change orders on project costs and schedule. Even though many were undertaken by contractors' organizations to advocate their interests, courts have been accepting of their use when applied in an appropriate manner to assist with an expert's analysis. One popular study is the MCAA (Mechanical Contractors Association of America) manual. This work identified 16 potential factors that could affect productivity and provide a possible productivity loss range. The NECA (National Electrical Contractor Association) developed similar studies. The Army Corps of Engineers published the “Modification Impact Evaluation Guide,” attempting to provide an analysis from a more neutral party. This study identified four major factors related to labor productivity: (1) disruption, (2) crowding, (3) acceleration, and (4) morale. Despite its intentions, it was not successfully applied to cumulative impact claims and, as a result, did not gain popularity. The first academic research was performed by Leonard [5] in “The Effect of Change Orders on Productivity.” This investigation used 90 case studies from 57 projects that involved disputes between owners and contractors. In this study, change order impact models were developed for two categories: electrical/mechanical projects and civil and architectural projects. Graphical results showed the relationship between percent change and loss of efficiency for each impact category. In addition, this research summarized the major causes of productivity losses including loss of rhythm, schedule deterioration, unbalanced crew and manpower fluctuation, acceleration, and increased site administration. Even though several dispute cases applied this method and were published in manuals, the method has been criticized because of biased samples and methodology. Because of these drawbacks, the use of this method was severely limited and criticized by several courts. Next, CII conducted a study titled Construction Changes and Change Orders: Their Magnitude and Impact at the University of California-Berkeley [6]. This research attempted to identify and discuss the real impact of changes on project costs and schedule. The researchers examined four case studies and gave recommendations to industry. This research developed fundamental concepts of change orders and gave helpful recommendations to industry, but failed to quantify the impact of changes. Three years later, the CII and The Pennsylvania State University research team conducted a change order study titled The Effects of Changes on Labor Productivity: Why and How much [7]. After collecting data from 4 projects, the researchers examined the quantitative effect of changes on labor productivity. They concluded that, on average, there is a 30% loss of efficiency when changes are being performed, and argued that timing of the change is a major factor. In addition, this research emphasized that there are strong relationships between low labor performance, presence of changes, disruption, and rework. Even though this research found a correlation between changes and productivity losses, it failed to quantify the impacts of change orders. Also, four case studies were not sufficient to develop a statistically valid model. The investigators argued that average loss for the projects impacted by change orders was around 30% of the total work hours. Because of the small sample size, this research was difficult to apply in the industry. The CII then commissioned a study titled Quantitative impact of Project Change at the University of California-Berkeley [8]. This research gathered a total of 89 case studies to verify assumed ideas that the timing of a change, number of changes, and the ripple effect of changes can affect the amount of productivity losses. Even though this study adopted an advanced (statistical modeling) methodology, it had several limitations, including low quality of model. This study also
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not always result in an agreement. To handle disputes, contract documents frequently define dispute resolution techniques; however, in some cases claims are unavoidable. It is well recognized by both project owners and the legal system that contractors have a right to an adjustment in contract price, that includes the cost associated with materials, labor, lost profit, and increased overhead related to a change. The most difficult issue to agree upon is how to quantify the cumulative impact of change orders on overall construction project. The disruption caused by change can have a ripple effect on the entire project, beyond the change itself. For example, a change order driven by schedule acceleration can require the contractor to use overmanning, shift work, and overtime, to recover the lost time. In addition, the work might now need to be performed out of sequence, with outdoor work being shifted to winter months instead of summer months. This significantly impacts the productivity on the changed work, as well as on the entire project. Also, whenever the crew has to wait, perform out of sequence work, or perform rework, the change has a cumulative impact on the project's productivity. In the highly competitive construction industry, there is little room for budget contingencies to cover the impact of change. In order to secure an adequate profit, the contractor needs to recover all costs associated with each change. However, without sophisticated productivity tracking systems, it is difficult to identify the impact of change in a timely manner in order to ensure that all costs are captured and communicated to the project owner for a fair and equitable adjustment. An added complication is that most contracts require the contractor to price the change order before the work is done. Fast-track schedules often do not allow the contractor a sufficient amount of time to facilitate the development of a comprehensive impact assessment. Often the impact of change is never realized until the project is complete. Many courts and administrative boards recognize that there is cumulative impact associated with change orders. However, determining the impact that change can have on contract price and time can be difficult due to the interconnected nature of the construction work. As a result, it is difficult for owners and contractors to agree on equitable adjustments for the loss of labor productivity. There are two general views held by the opposing sides regarding this issue. Contractors view change orders as detrimental to their profitability because the cumulative impact of change orders are difficult to quantify and to prove. On the other hand, owners may interpret change orders as an opportunity for contractors to recover losses from other parts of the job or to increase their overall profitability. The loss in productivity is attributed to poor management or low initial bids. Also, some owners feel that the impact should be covered under the contingency included in the bid. In order to allow for the objective determination of contractor loss, it is proposed that a reliable, unbiased model be used to identify and calculate the loss of productivity (cost) caused by the cumulative impact of change orders. Of the various research inquiries completed to quantify the cumulative impact of change orders on labor productivity, the CII (Construction Industry Institute) and University of Wisconsin research team has performed the most recent and extensive study. Their research investigated how change orders impact labor efficiency at the macro productivity level, determining and isolating specific measurable characteristics of projects that are impacted by cumulative change for electrical and mechanical projects. In addition, this study developed a quantitative model for detecting projects impacted by change orders [2,3], and a second model to predict the cumulative impact of project change [4]. Even though there are some researches related with change order impact, it still has difficulties to apply developed models to their actual cases easily. The purpose of this study is to develop an automated system for the detection and quantification of change order impact, based on Hanna's previous research models. The developed system will serve as a guide for change order related dispute resolution
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Table 1 Calculation of the probability of impact for case project. Value (2) 1 1 0.35 0.7
Coefficient (3)
Product (4)
− 6.99 −1.09
− 6.99 − 1.09
3.89
1.362
−1.03
− 0.721
4
0.63
2.54
1
2.64
2.644
1
1.19
1.194
1.21
2.783
2.3
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Constant X1. Mechanical or electrical 1 if mechanical, 0 if electrical X2. Percent change × (Mech_or_Elec) Percent change as a decimal multiplied by 1 or 0 X3. Estimated / actual peak manpower Estimated peak manpower = 35 Actual peak manpower = 50 Estimated / actual peak manpower = 0.7 X4. Processing time Average period of time between initiation of the change order and the owner's approval. 1 = 1–7 days; 2 = 8–14 days; 3 = 15–21 days; 4 = 22–28 days; 5 = greater than 28 days X5. Overmanning (est. peak / act. peak manpower) = 0.25 1 if overmanning occurred on project (b0.77), otherwise 0 X6. Overtime 1 if overtime was used to complete change order work on project, otherwise 0 X7. Actual peak manpower / actual average manpower Actual peak manpower = 50 Actual average manpower = 22 Therefore actual peak / actual average manpower = 2.3 X8. Change orders related to design changes and errors Design issues related change orders as a percent e3:4 = 0:9677 ProbabilityðY = 1Þ = 1 + e3:4
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Factor (1)
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failed to support the assumed concept that the timing of changes had an effect on labor efficiency. The most recent and extensive study was conducted by the CII in conjunction with the University of Wisconsin-Madison. The title of the study is “Quantifying the Cumulative Impact of Change Orders for Electrical and Mechanical Contractors [9].” This study included an extensive survey of contractors and considered approximately 150 case studies. In addition, over 70 factors related to productivity impact were investigated. The two regression models developed determined whether a project was impacted by change orders or not, and if a project was impacted by change orders, how large the productivity loss was. Because of these characteristics, one lawyer explained the usefulness of this research as follows: “CII models can be used to prove or disprove the three elements of a cumulative impact claims; liability, causation, and damages [10].” Some of the above studies attempted to develop regression models to quantify the impact loss. However, regression analysis has shortcomings in dealing with highly nonlinear input–output functions. Also, field personnel encountered difficulties in applying the technical models of regression to their projects. Moreover, regression analysis showed limited success when dealing with many qualitative and noisy input variables. In order to overcome these difficulties, AI (Artificial
100
0.01725
1.725 3.4
Intelligence) based modeling techniques were identified as powerful tools that could be used to model these complicated problems. As a result, a research team applied artificial intelligence based models such as the Statistical-Fuzzy model [2,3], Decision Tree model (Lee, 2004), Artificial Neural Network models [11], improving the quality of the models significantly. However, it was also recognized that AI based models were difficult to apply in the industry, as they were difficult for field personnel to understand, use, and trust. When the acceptable level of model accuracy and applicability to users was considered, authors concluded that regression models are the most appropriate method to quantify change order impact from their experience. However, regression models still give difficulties to stakeholders to apply their actual cases easily. In order to simplify the technical complexities of regression, an automated system for the detection and quantification of change orders impact based on previous research models has been developed. 3. Change order impact detection model
In a previous study [2,3] a logistic regression model was developed using eight factors (X1– X8), which predicted the probability that a project has been impacted by change orders. A detailed explanation of
Table 2 Calculation of the impact loss for case project. Factor (1)
Value (2)
Coefficient (3)
Product (4)
Constant Z1. Percent change: Percent of change on project in terms of original budgeted work hours Z2. PM%TimeOnProject: Percent of time the Project Manager spends on the project Z3. %OwnerInitCI: (number of owner initiated change items / total change items) Z4. Productivity: was productivity tracked? (Yes = 1, No = 0) (Input [work hours] / output [units installed]) The Contractor could use one of the following: Track % complete by earned value Track % complete by actual earned work hours Track % complete by actual installed quantities Z5. Overmanning (Est. peak / act. peak manpower) = 0.25 1 if overmanning occurred on project (b0.77), otherwise 0 Z6. Processing time Average period of time between initiation of the change order and the owner's approval. 1 = 1–7 days; 2 = 8–14 days; 3 = 15–21 days; 4 = 22–28 days; 5 = greater than 28 days Sum
1 0.35 1 1 0
0.37 0.12 − 0.08 −0.17 − 0.09
0.37 0.042 − 0.08 − 0.17 0
1
− 0.05
− 0.05
4
0.02
0.08
0.19
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each factor is displayed in Table 1, column 1. The model is based on two equations: ProbabilityðY = 1Þ =
eY 1 + eY
ð1Þ
Y = − 6:997 − 1:0939X1 + 3:889X2 − 1:0371X3 + 0:6342X4 + 2:6433X5 + 1:1933X6 + 1:2048X7 + 0:017154X8 :
ð2Þ
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Logistic regression is similar to linear regression; however, the model takes the general form shown in Eq. (1), while linear regression is based upon the model shown in Eq. (2). In Eq. (1), the “Y” term has been transformed by the log function. To provide a better understanding of how the model works, an example is shown below in Table 1. The data values or the variables are applied to Eq. (2), to obtain the Y value. The Y value is then used in Eq. (1) to calculate the probability that the project is impacted by change orders. The separate parameters in the model should not be interpreted individually. Due to the high correlation or collinearity between the factors, it is not possible to vary one factor without impacting the
other variables. It is for this reason that some of the coefficient signs appear to go against natural intuition. For example, based on the model, there is a negative relationship between “percent change” and the “ratio of estimated to actual peak manpower.” For electrical projects the negative coefficient for “percent change” may seem inherently incorrect; however, this could be caused by the interaction involving the other variables. The model may also be used as a forecasting tool. During construction, the various factors could be applied to the model to determine if the project has a high probability of being impacted. Based on the results, management could decide whether more aggressive steps should be taken to decrease the impact. For instance, if a change order has a large probability of impacting the project cost, management could grant a project schedule extension so that overtime is not necessary and overmanning is limited. Furthermore, a schedule extension will impact the variables for “estimated/actual peak manpower ratio” and “peak to average manpower ratio.” This model identifies how management can minimize the impact of change. For instance, even though a change order will affect some aspect of the
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Fig. 1. Step I in process of determining impact from change orders.
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Fig. 2. Step II in process of determining amount of impact from change orders.
project, the manager can have some control over the balance of schedule, costs, and productivity. An efficient change order method can also decrease processing time and adjustments to the project. 4. Change order impact quantification model
From another study [4], a multiple linear regression model was developed using six factors (Z1 –Z6), to predict the magnitude of the impact of change orders on labor productivity. Detailed explanation of factors is shown at in Table 2, column 1. The model is as follows: Y = 0:36866 + 0:11957Z1 − 0:08065Z2 − 0:16723Z3 − 0:09147Z4 ð3Þ − 0:05213Z5 + 0:022345Z6 : To provide a better understanding of how the model works, an example is shown in Table 2. The data values or variables are applied to Eq. (3) to compute Y. In this case, the Y value (dependent variable) is the variable %Delta, with Delta being the quantitative measure of all lost work hours as a result of change orders and %Delta being the percentage of the total actual hours expended on the project [4].
The model can be used in a pro-active manner by the project stakeholders as an assessment tool before the conclusion of a project. The model can also be used as an anti-litigation tool at the conclusion of a project. In the case of dispute resolution, the predicted value for % Delta obtained can be used as a benchmark for negotiation between contractors and owners. 5. Automated impact detection and quantification system This study developed an automated detection and quantification system for change order impact based on previous research results. The automation of the model was a key component leading to broad application, as many users are unfamiliar with the technical models and complicated statistical methodologies employed by their academic designers. The automated model is meant to be applied to cases where insufficient project data exists to either prove or disprove the extent that a project has been impacted. Ideally the contractor and owner should be able to work together to limit the impact of change orders on the overall project productivity and to identify potential cumulative effects. The cumulative effects could then be tracked and
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labors. Since the contractor had reserved the right to make a claim for the loss of productivity caused by the cumulative impact of change orders, the contractor requested reimbursement of the additional impact cost from the owner. This amount was 3735 work hours (Total Actual Hours (17,235) − Budgeted Hours (10,000) − Approved Change Order Hours (3500)). The owner agreed that the changes may have caused the PM a loss of productivity, but also argued that there was no clear proof that the project had been affected by cumulative impacts of the changes. In addition, the owner did not agree with the additional funding that the contractor requested. The owner suspected that some loss could be caused by the contractor's own inefficiency or by a low initial (Bid) estimate. Since the parties did not agree on the “cumulative impact” and “quantity of loss,” they decided to apply the automated system (program) developed in this study. A summary of this case study project data and program inputs (questions) are given, with the answers displayed in bold. Q1) Was your project “Mechanical” or “Electrical”?: Mechanical Q2) Was your project type “Industrial” or “Others”?: Industrial Q3) The contract budgeted man-hours at the notice to proceed for the project: 10,000 h Q4) The actual man-hours utilized at completion of the project, including change orders: 17,235 h Q5) The total owner approved change order man-hours: 3500 h Q6) The total credit change order hours: 0 h Q7) The estimated project duration at contract award: 20 calendar weeks Q8) The actual project duration at completion: 20 calendar weeks Q9) Did you track productivity (input [man-hours]/output [units installed]) for the project?: No Q10) The peak number of craftsmen used for the project: 50 workers Q11) The “processing time” (the period of time between initiation of the change order and the owner's approval) for the majority of change items experienced on this project was: 22 –28 days Q12) Total number of change items: 100 items Q13) Number of owner initiated change items: 100 items Q14) Change orders related to design issues (design errors, changes, and coordination): 100% Q15) Was overtime experienced on this job?: Yes Q16) Project manager's percentage time spent on this project?: 100%
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an agreement could be reached on the cumulative damages. However, in projects where time and budget constraints do not allow for this documentation on all aspects of a project or the ripple effect of the change orders are unclear, this system can serve as a tool to a more rapid settlement. The process to resolve cumulative impact problems is diagramed in Figs. 1 and 2. By executing the actions listed in Step I (Fig. 1) and Step II (Fig. 2), contractors and owners can be guided in the method that best applies to the existing problem and decide whether there is adequate evidence to support the application of the detection and quantification models. Step I assists in determining whether the change orders on the project had a negative impact on the contractor's productivity. Not every project with change orders is impacted. In multiple case studies, numerous projects were characterized by a large percentage of change orders without a substantial impact on productivity. However, Eqs. (1) and (2) are not a fool proof method, and should be applied to project data with some caution. While the model correctly predicts whether a project has been impacted 88 percent of the time, there is still a chance that an impacted project has a probability of impact less than 0.50. In this case, an investigation of the project records may identify why the project is impacted or how the project became impacted (Hanna et al., 2001). Once it has been determined whether a project has been impacted by the change orders, Step II can be used to determine the amount of the impact. Project data is always the best source for determining the productivity loss associated with the change orders. The model shown in Eq. (3), can be used to substantiate the project data or as a starting place for negotiations. This model could be used in conjunction with many of the techniques for calculating damages presented in the literature review. The flow charts in Fig. 3 illustrate the automated system structure and how it is related to the detection and quantification models. The user interface system, developed with “C++” program language software, asks 16 questions regarding the project characteristics. Details of the 16 questions and sample answers demonstrate an example of how the detection and quantification system could be applied to a project. The presented system does not give the exact result of the productivity losses caused by changes. What they do calculate is an estimation or prediction of the productivity losses based upon a number of project characteristics. The predictions can be used to support a contractor's calculations for the productivity losses, or in cases where there are no data, can be used in place of the lost calculations.
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6. Case study for automated system application
For stakeholders interested in applying the model, this section provides a demonstration of the system on one recently completed project. Badger Mechanical Construction Company entered into a contract to install the mechanical requirements for a commercial building for UW Incorporation in Wisconsin. The contractor (Badger Mechanical) estimated that the project would take 20 weeks with 10,000 work hours to complete. Throughout the project, the owner initiated several change orders which led to the approval of 3500 additional work hours. The Project Manager (PM) for Badger Mechanical was aware that the job had exceeded the original labor hours planned, but did not realize the extent, as he was not monitoring productivity during construction. At the end of the project when the PM was recapping the project, he realized that in order to finish on schedule, 3735 additional work hours (beyond the original change order for 3500 work hours) had been expended to over-man and over-time the project. The PM presented the project recap sheet to the owner and explained that the owner initiated project change orders had impacted his labor a great deal, accounting for over 35% of the total
6.1. Was this project impacted by change orders? Determination of whether the project was impacted or not was very important for this case, as the two parties held different opinions. Calculation results suggest that there is a strong chance (96.77%) that the project was impacted by change orders (see Table 1). Based on the results, the owner agreed that a cumulative impact had been caused by the multiple changes. 6.2. How much is the impact loss? Once the owner agreed that change impact was present, both parties were interested in estimating the loss incurred. However, as in this example, they did not agree on the amount. Obviously, contractors tend to have high expectations in recovering their losses, while owners tend to minimize their responsibility. After some argument, the parties involved in this example decided to apply the quantification model to determine the predicted value of productivity loss. To apply the impact quantification model, the users determined the necessary information (values) from the project information summary above and applied it to the multiple regression model. Table 2 shows the calculation process and result. The calculation result predicted that the impact loss was 19% of the actual hours, amounting to 3275 work hours (0.19 ⁎ 17,235 = 3275). Based on this result, both parties were able to negotiate a proper
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Fig. 3. System structure and relationships with models.
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7. Summary and conclusion A number of studies have attempted to quantify the impact of change orders on project costs and schedule, including those done by CII, NECA, and MCAA. Some of them attempted to develop regression models to quantify the impact loss while others tried to develop
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compensation for the cumulative impact loss caused by multiple changes. Fig. 4 (Appendix) displays the screen-shots and user interface of the automated system implementation. More details of model effectiveness verification and sensitive analysis could be found in previous study [2–4].
Fig. 4. Appendix—automated system application example.
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Fig. 4 (continued).
Artificial Intelligence (AI) based models. However, AI based models are difficult for field personnel to understand and apply. When considering the acceptable level of model accuracy and applicability to users, regression models were identified as the most appropriate method to quantify change order impact. This study developed an automated system for detection and quantification of change orders impact based on previous research using regression models.
The automated system developed in this research was designed to be used in cases where there is not sufficient project data to prove that the project has been impacted or to quantify the amount of the impact. Ideally, the contractor and owner are able to work together to limit the impact of change orders and to properly identify potential cumulative effects. The presented system does not claim to calculate the exact productivity losses caused by changes, but is intended to work as a guide.
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Fig. 4 (continued).
When little information is available, it is able to provide an estimation or prediction of the productivity losses based upon a number of project characteristics. The predictions can be used to support a contractor's calculations for the productivity losses, or in cases where there are no data, can be used in place of the loss calculations. For future examination, the scope of this research needs to be expanded to include other construction disciplines, project delivery
types, and contract types. Expansion of the research scope will allow more contractors and owners to understand how changes affect the productivity on their construction projects. Furthermore, development of web-based automated interface applications is recommended for simplified dissemination of the technology, thereby allowing stakeholders easy access for application to project cases. Also, a web-based interface system will make it possible to
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Acknowledgement This paper was developed based on the data collection of the Construction Industry Institute (CII) and the University of WisconsinMadison research group, and special thanks go to the electrical and mechanical contractors who supplied the data for this study. References
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[1] Robert Cushman, Stephen Butler, Construction Change Order Claims, John Wiley and Sons, Inc., Somerset, NJ, 1994. [2] A.S. Hanna, W.B. Lotfallah, Min-Jae Lee, Statistical-fuzzy approach to quantify cumulative impact of change orders, Journal of Computing in Civil Engineering, American Society of Civil Engineers (ASCE) 16 (4) (October 2002) 252–258. [3] A.S. Hanna, R. Camlic, P.A. Peterson, E.V. Nordheim, Quantitative definition of projects impacted by change orders, Journal of Construction Engineering and Management, American Society of Civil Engineers (ASCE) (January/February 2002) 57–64.
[4] A.S. Hanna, R. Camlic, P.A. Peterson, M.J. Lee, Cumulative effect of project changes for electrical and mechanical construction, Journal of Construction Engineering and Management, American Society of Civil Engineers (ASCE) 130 (6) (November/ December 2004) 762–771. [5] C. Leonard, O. Moselhi, P. Fazio, Impact of change order on construction productivity, Canadian Journal of Civil Engineering 18 (1991) 484–492. [6] Hester, Kuprenas, Chang (1991). Construction Changes and Change Orders: Their Magnitude and Impact. Construction Industry Institute (CII), Source Document 66, Austin, TX. [7] H. Randolph Thomas, Carmen L. Napolitan, (1994). The Effects of Changes on Labor Productivity: Why and How much. Source Document 99, Construction Industry Institute (CII). August, Austin, TX. [8] C.W. Ibbs, Walter E. Allen. (1995). Quantitative Impacts of Project Change. Source Document 108, May, Construction Industry Institute, University of Texas at Austin, Austin, TX. [9] Awad S. Hanna, (2001). Quantifying The Cumulative Impact of Change Orders for Electrical and Mechanical Contractors. Research Report 158-11, February, Construction Industry Institute (CII), University of Texas at Austin, Austin, TX. [10] R.M. Jones, Lost productivity: claims for the cumulative impact of multiple change orders 31 Publication, Construction Law Journal 1 (2001). [11] Min-Jae Lee, (2005), Classify and Quantify Cumulative Impact of Change Orders On Productivity Using ANN Models, Korean Journal of Construction Engineering and Management, Vol. 6, No. 5, pp.69–77, 2005.10, Korean Institute of Construction Engineering and Management.
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Automation in Construction j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / a u t c o n
Review
Development of automated change order impact detection and quantification system Min-Jae Lee a, Boong Yeol Ryoo b,⁎, Kenneth T. Sullivan c, Awad S. Hanna d
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a
Dept. of Civil Engineering, Chungnam National University, 220 Gung-dong, Yuseong-gu, Daejeon, 305-764, South Korea Dept. of Construction Science, Texas A&M University, 3137 TAMU, College of Architecture, Texas A&M University, College Station, Texas 77843-3137, United States c Del E. Webb School of Construction, Arizona State University, Tempe, Arizona 85287, United States d Dept. of Civil and Envir. Engrg., Univ. of Wisconsin-Madison, 2314 Engrg. Hall, 1415 Engrg, Dr., Madison, WI 53706, United States
i n f o
Article history: Accepted 5 March 2009 Keywords: Change orders Productivity impact Detection and quantification system
Contents
When change orders occur, adjustments in contract price and duration are usually agreed upon through negotiation or stipulated by contract terms and conditions. There are many pricing methods for change orders impact on productivity loss available, but negotiations using these methods do not always result in a contractor and client agreement. The most difficult issue to agree upon is how to quantify the cumulative impact of change orders on overall construction project. Cumulative impact claims imply that the disruption caused by a single change can have a ripple effect on the entire project, beyond the change itself. In order to recover cumulative impact damages, by law, the contractor is required to prove three basic components: liability, causation, and resultant injury (quantum). Of these elements, causation and resultant injury are the most difficult to prove. There has been some research performed to quantify the cumulative impact of change orders on labor productivity. CII and the University of Wisconsin have performed the most recent and extensive study related to this issue. This study used previous CII-Hanna research models to develop an automated system for the detection and quantification of change order impacts. In addition, a project demonstration is included to allow easy understanding for new system users. The developed system provides a simple guide for stakeholders to expedite the resolution of change order related disputes between contractors and owners by providing causation and resultant injury(quantum) analysis on troubled projects. © 2009 Published by Elsevier B.V.
Introduction . . . . . . . . . . . . . . . . . . . . . Literature review . . . . . . . . . . . . . . . . . . Change order impact detection model . . . . . . . . . Change order impact quantification model . . . . . . Automated impact detection and quantification system Case study for automated system application . . . . . 6.1. Was this project impacted by change orders? . . 6.2. How much is the impact loss? . . . . . . . . . 7. Summary and conclusion . . . . . . . . . . . . . . . Acknowledgement . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . .
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1. Introduction
Numerous project influences can initiate change orders such as: additions or deletions in project scope; changes in codes, laws, or standards; design errors, or optimization; project planning deficien-
⁎ Corresponding author. E-mail addresses: [email protected] (M.-J. Lee), [email protected] (B.Y. Ryoo), [email protected] (K.T. Sullivan), [email protected] (A.S. Hanna). 0926-5805/$ – see front matter © 2009 Published by Elsevier B.V. doi:10.1016/j.autcon.2009.03.002
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cies; incomplete design documents; workers or material availability limitations; unknown site conditions; schedule compression; or unexpected weather problems. In addition, construction contracts include change clauses that authorize the project owner to alter the work performed by the contractor with an equitable adjustment mechanism to alter the contract price and duration [1]. The adjustments in contract price and duration are usually agreed upon through negotiation or stipulated by contract terms and conditions. There are many pricing methods for change order impact on productivity loss available, but negotiations using these models do
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between contractors and owners by providing causation and resultant injury (quantum) analysis on troubled projects, specifically in regards to electrical and mechanical construction.
2. Literature review A number of studies have attempted to quantify the impact of change orders on project costs and schedule. Even though many were undertaken by contractors' organizations to advocate their interests, courts have been accepting of their use when applied in an appropriate manner to assist with an expert's analysis. One popular study is the MCAA (Mechanical Contractors Association of America) manual. This work identified 16 potential factors that could affect productivity and provide a possible productivity loss range. The NECA (National Electrical Contractor Association) developed similar studies. The Army Corps of Engineers published the “Modification Impact Evaluation Guide,” attempting to provide an analysis from a more neutral party. This study identified four major factors related to labor productivity: (1) disruption, (2) crowding, (3) acceleration, and (4) morale. Despite its intentions, it was not successfully applied to cumulative impact claims and, as a result, did not gain popularity. The first academic research was performed by Leonard [5] in “The Effect of Change Orders on Productivity.” This investigation used 90 case studies from 57 projects that involved disputes between owners and contractors. In this study, change order impact models were developed for two categories: electrical/mechanical projects and civil and architectural projects. Graphical results showed the relationship between percent change and loss of efficiency for each impact category. In addition, this research summarized the major causes of productivity losses including loss of rhythm, schedule deterioration, unbalanced crew and manpower fluctuation, acceleration, and increased site administration. Even though several dispute cases applied this method and were published in manuals, the method has been criticized because of biased samples and methodology. Because of these drawbacks, the use of this method was severely limited and criticized by several courts. Next, CII conducted a study titled Construction Changes and Change Orders: Their Magnitude and Impact at the University of California-Berkeley [6]. This research attempted to identify and discuss the real impact of changes on project costs and schedule. The researchers examined four case studies and gave recommendations to industry. This research developed fundamental concepts of change orders and gave helpful recommendations to industry, but failed to quantify the impact of changes. Three years later, the CII and The Pennsylvania State University research team conducted a change order study titled The Effects of Changes on Labor Productivity: Why and How much [7]. After collecting data from 4 projects, the researchers examined the quantitative effect of changes on labor productivity. They concluded that, on average, there is a 30% loss of efficiency when changes are being performed, and argued that timing of the change is a major factor. In addition, this research emphasized that there are strong relationships between low labor performance, presence of changes, disruption, and rework. Even though this research found a correlation between changes and productivity losses, it failed to quantify the impacts of change orders. Also, four case studies were not sufficient to develop a statistically valid model. The investigators argued that average loss for the projects impacted by change orders was around 30% of the total work hours. Because of the small sample size, this research was difficult to apply in the industry. The CII then commissioned a study titled Quantitative impact of Project Change at the University of California-Berkeley [8]. This research gathered a total of 89 case studies to verify assumed ideas that the timing of a change, number of changes, and the ripple effect of changes can affect the amount of productivity losses. Even though this study adopted an advanced (statistical modeling) methodology, it had several limitations, including low quality of model. This study also
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not always result in an agreement. To handle disputes, contract documents frequently define dispute resolution techniques; however, in some cases claims are unavoidable. It is well recognized by both project owners and the legal system that contractors have a right to an adjustment in contract price, that includes the cost associated with materials, labor, lost profit, and increased overhead related to a change. The most difficult issue to agree upon is how to quantify the cumulative impact of change orders on overall construction project. The disruption caused by change can have a ripple effect on the entire project, beyond the change itself. For example, a change order driven by schedule acceleration can require the contractor to use overmanning, shift work, and overtime, to recover the lost time. In addition, the work might now need to be performed out of sequence, with outdoor work being shifted to winter months instead of summer months. This significantly impacts the productivity on the changed work, as well as on the entire project. Also, whenever the crew has to wait, perform out of sequence work, or perform rework, the change has a cumulative impact on the project's productivity. In the highly competitive construction industry, there is little room for budget contingencies to cover the impact of change. In order to secure an adequate profit, the contractor needs to recover all costs associated with each change. However, without sophisticated productivity tracking systems, it is difficult to identify the impact of change in a timely manner in order to ensure that all costs are captured and communicated to the project owner for a fair and equitable adjustment. An added complication is that most contracts require the contractor to price the change order before the work is done. Fast-track schedules often do not allow the contractor a sufficient amount of time to facilitate the development of a comprehensive impact assessment. Often the impact of change is never realized until the project is complete. Many courts and administrative boards recognize that there is cumulative impact associated with change orders. However, determining the impact that change can have on contract price and time can be difficult due to the interconnected nature of the construction work. As a result, it is difficult for owners and contractors to agree on equitable adjustments for the loss of labor productivity. There are two general views held by the opposing sides regarding this issue. Contractors view change orders as detrimental to their profitability because the cumulative impact of change orders are difficult to quantify and to prove. On the other hand, owners may interpret change orders as an opportunity for contractors to recover losses from other parts of the job or to increase their overall profitability. The loss in productivity is attributed to poor management or low initial bids. Also, some owners feel that the impact should be covered under the contingency included in the bid. In order to allow for the objective determination of contractor loss, it is proposed that a reliable, unbiased model be used to identify and calculate the loss of productivity (cost) caused by the cumulative impact of change orders. Of the various research inquiries completed to quantify the cumulative impact of change orders on labor productivity, the CII (Construction Industry Institute) and University of Wisconsin research team has performed the most recent and extensive study. Their research investigated how change orders impact labor efficiency at the macro productivity level, determining and isolating specific measurable characteristics of projects that are impacted by cumulative change for electrical and mechanical projects. In addition, this study developed a quantitative model for detecting projects impacted by change orders [2,3], and a second model to predict the cumulative impact of project change [4]. Even though there are some researches related with change order impact, it still has difficulties to apply developed models to their actual cases easily. The purpose of this study is to develop an automated system for the detection and quantification of change order impact, based on Hanna's previous research models. The developed system will serve as a guide for change order related dispute resolution
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Table 1 Calculation of the probability of impact for case project. Value (2) 1 1 0.35 0.7
Coefficient (3)
Product (4)
− 6.99 −1.09
− 6.99 − 1.09
3.89
1.362
−1.03
− 0.721
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0.63
2.54
1
2.64
2.644
1
1.19
1.194
1.21
2.783
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Constant X1. Mechanical or electrical 1 if mechanical, 0 if electrical X2. Percent change × (Mech_or_Elec) Percent change as a decimal multiplied by 1 or 0 X3. Estimated / actual peak manpower Estimated peak manpower = 35 Actual peak manpower = 50 Estimated / actual peak manpower = 0.7 X4. Processing time Average period of time between initiation of the change order and the owner's approval. 1 = 1–7 days; 2 = 8–14 days; 3 = 15–21 days; 4 = 22–28 days; 5 = greater than 28 days X5. Overmanning (est. peak / act. peak manpower) = 0.25 1 if overmanning occurred on project (b0.77), otherwise 0 X6. Overtime 1 if overtime was used to complete change order work on project, otherwise 0 X7. Actual peak manpower / actual average manpower Actual peak manpower = 50 Actual average manpower = 22 Therefore actual peak / actual average manpower = 2.3 X8. Change orders related to design changes and errors Design issues related change orders as a percent e3:4 = 0:9677 ProbabilityðY = 1Þ = 1 + e3:4
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failed to support the assumed concept that the timing of changes had an effect on labor efficiency. The most recent and extensive study was conducted by the CII in conjunction with the University of Wisconsin-Madison. The title of the study is “Quantifying the Cumulative Impact of Change Orders for Electrical and Mechanical Contractors [9].” This study included an extensive survey of contractors and considered approximately 150 case studies. In addition, over 70 factors related to productivity impact were investigated. The two regression models developed determined whether a project was impacted by change orders or not, and if a project was impacted by change orders, how large the productivity loss was. Because of these characteristics, one lawyer explained the usefulness of this research as follows: “CII models can be used to prove or disprove the three elements of a cumulative impact claims; liability, causation, and damages [10].” Some of the above studies attempted to develop regression models to quantify the impact loss. However, regression analysis has shortcomings in dealing with highly nonlinear input–output functions. Also, field personnel encountered difficulties in applying the technical models of regression to their projects. Moreover, regression analysis showed limited success when dealing with many qualitative and noisy input variables. In order to overcome these difficulties, AI (Artificial
100
0.01725
1.725 3.4
Intelligence) based modeling techniques were identified as powerful tools that could be used to model these complicated problems. As a result, a research team applied artificial intelligence based models such as the Statistical-Fuzzy model [2,3], Decision Tree model (Lee, 2004), Artificial Neural Network models [11], improving the quality of the models significantly. However, it was also recognized that AI based models were difficult to apply in the industry, as they were difficult for field personnel to understand, use, and trust. When the acceptable level of model accuracy and applicability to users was considered, authors concluded that regression models are the most appropriate method to quantify change order impact from their experience. However, regression models still give difficulties to stakeholders to apply their actual cases easily. In order to simplify the technical complexities of regression, an automated system for the detection and quantification of change orders impact based on previous research models has been developed. 3. Change order impact detection model
In a previous study [2,3] a logistic regression model was developed using eight factors (X1– X8), which predicted the probability that a project has been impacted by change orders. A detailed explanation of
Table 2 Calculation of the impact loss for case project. Factor (1)
Value (2)
Coefficient (3)
Product (4)
Constant Z1. Percent change: Percent of change on project in terms of original budgeted work hours Z2. PM%TimeOnProject: Percent of time the Project Manager spends on the project Z3. %OwnerInitCI: (number of owner initiated change items / total change items) Z4. Productivity: was productivity tracked? (Yes = 1, No = 0) (Input [work hours] / output [units installed]) The Contractor could use one of the following: Track % complete by earned value Track % complete by actual earned work hours Track % complete by actual installed quantities Z5. Overmanning (Est. peak / act. peak manpower) = 0.25 1 if overmanning occurred on project (b0.77), otherwise 0 Z6. Processing time Average period of time between initiation of the change order and the owner's approval. 1 = 1–7 days; 2 = 8–14 days; 3 = 15–21 days; 4 = 22–28 days; 5 = greater than 28 days Sum
1 0.35 1 1 0
0.37 0.12 − 0.08 −0.17 − 0.09
0.37 0.042 − 0.08 − 0.17 0
1
− 0.05
− 0.05
4
0.02
0.08
0.19
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each factor is displayed in Table 1, column 1. The model is based on two equations: ProbabilityðY = 1Þ =
eY 1 + eY
ð1Þ
Y = − 6:997 − 1:0939X1 + 3:889X2 − 1:0371X3 + 0:6342X4 + 2:6433X5 + 1:1933X6 + 1:2048X7 + 0:017154X8 :
ð2Þ
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Logistic regression is similar to linear regression; however, the model takes the general form shown in Eq. (1), while linear regression is based upon the model shown in Eq. (2). In Eq. (1), the “Y” term has been transformed by the log function. To provide a better understanding of how the model works, an example is shown below in Table 1. The data values or the variables are applied to Eq. (2), to obtain the Y value. The Y value is then used in Eq. (1) to calculate the probability that the project is impacted by change orders. The separate parameters in the model should not be interpreted individually. Due to the high correlation or collinearity between the factors, it is not possible to vary one factor without impacting the
other variables. It is for this reason that some of the coefficient signs appear to go against natural intuition. For example, based on the model, there is a negative relationship between “percent change” and the “ratio of estimated to actual peak manpower.” For electrical projects the negative coefficient for “percent change” may seem inherently incorrect; however, this could be caused by the interaction involving the other variables. The model may also be used as a forecasting tool. During construction, the various factors could be applied to the model to determine if the project has a high probability of being impacted. Based on the results, management could decide whether more aggressive steps should be taken to decrease the impact. For instance, if a change order has a large probability of impacting the project cost, management could grant a project schedule extension so that overtime is not necessary and overmanning is limited. Furthermore, a schedule extension will impact the variables for “estimated/actual peak manpower ratio” and “peak to average manpower ratio.” This model identifies how management can minimize the impact of change. For instance, even though a change order will affect some aspect of the
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Fig. 1. Step I in process of determining impact from change orders.
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Fig. 2. Step II in process of determining amount of impact from change orders.
project, the manager can have some control over the balance of schedule, costs, and productivity. An efficient change order method can also decrease processing time and adjustments to the project. 4. Change order impact quantification model
From another study [4], a multiple linear regression model was developed using six factors (Z1 –Z6), to predict the magnitude of the impact of change orders on labor productivity. Detailed explanation of factors is shown at in Table 2, column 1. The model is as follows: Y = 0:36866 + 0:11957Z1 − 0:08065Z2 − 0:16723Z3 − 0:09147Z4 ð3Þ − 0:05213Z5 + 0:022345Z6 : To provide a better understanding of how the model works, an example is shown in Table 2. The data values or variables are applied to Eq. (3) to compute Y. In this case, the Y value (dependent variable) is the variable %Delta, with Delta being the quantitative measure of all lost work hours as a result of change orders and %Delta being the percentage of the total actual hours expended on the project [4].
The model can be used in a pro-active manner by the project stakeholders as an assessment tool before the conclusion of a project. The model can also be used as an anti-litigation tool at the conclusion of a project. In the case of dispute resolution, the predicted value for % Delta obtained can be used as a benchmark for negotiation between contractors and owners. 5. Automated impact detection and quantification system This study developed an automated detection and quantification system for change order impact based on previous research results. The automation of the model was a key component leading to broad application, as many users are unfamiliar with the technical models and complicated statistical methodologies employed by their academic designers. The automated model is meant to be applied to cases where insufficient project data exists to either prove or disprove the extent that a project has been impacted. Ideally the contractor and owner should be able to work together to limit the impact of change orders on the overall project productivity and to identify potential cumulative effects. The cumulative effects could then be tracked and
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labors. Since the contractor had reserved the right to make a claim for the loss of productivity caused by the cumulative impact of change orders, the contractor requested reimbursement of the additional impact cost from the owner. This amount was 3735 work hours (Total Actual Hours (17,235) − Budgeted Hours (10,000) − Approved Change Order Hours (3500)). The owner agreed that the changes may have caused the PM a loss of productivity, but also argued that there was no clear proof that the project had been affected by cumulative impacts of the changes. In addition, the owner did not agree with the additional funding that the contractor requested. The owner suspected that some loss could be caused by the contractor's own inefficiency or by a low initial (Bid) estimate. Since the parties did not agree on the “cumulative impact” and “quantity of loss,” they decided to apply the automated system (program) developed in this study. A summary of this case study project data and program inputs (questions) are given, with the answers displayed in bold. Q1) Was your project “Mechanical” or “Electrical”?: Mechanical Q2) Was your project type “Industrial” or “Others”?: Industrial Q3) The contract budgeted man-hours at the notice to proceed for the project: 10,000 h Q4) The actual man-hours utilized at completion of the project, including change orders: 17,235 h Q5) The total owner approved change order man-hours: 3500 h Q6) The total credit change order hours: 0 h Q7) The estimated project duration at contract award: 20 calendar weeks Q8) The actual project duration at completion: 20 calendar weeks Q9) Did you track productivity (input [man-hours]/output [units installed]) for the project?: No Q10) The peak number of craftsmen used for the project: 50 workers Q11) The “processing time” (the period of time between initiation of the change order and the owner's approval) for the majority of change items experienced on this project was: 22 –28 days Q12) Total number of change items: 100 items Q13) Number of owner initiated change items: 100 items Q14) Change orders related to design issues (design errors, changes, and coordination): 100% Q15) Was overtime experienced on this job?: Yes Q16) Project manager's percentage time spent on this project?: 100%
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an agreement could be reached on the cumulative damages. However, in projects where time and budget constraints do not allow for this documentation on all aspects of a project or the ripple effect of the change orders are unclear, this system can serve as a tool to a more rapid settlement. The process to resolve cumulative impact problems is diagramed in Figs. 1 and 2. By executing the actions listed in Step I (Fig. 1) and Step II (Fig. 2), contractors and owners can be guided in the method that best applies to the existing problem and decide whether there is adequate evidence to support the application of the detection and quantification models. Step I assists in determining whether the change orders on the project had a negative impact on the contractor's productivity. Not every project with change orders is impacted. In multiple case studies, numerous projects were characterized by a large percentage of change orders without a substantial impact on productivity. However, Eqs. (1) and (2) are not a fool proof method, and should be applied to project data with some caution. While the model correctly predicts whether a project has been impacted 88 percent of the time, there is still a chance that an impacted project has a probability of impact less than 0.50. In this case, an investigation of the project records may identify why the project is impacted or how the project became impacted (Hanna et al., 2001). Once it has been determined whether a project has been impacted by the change orders, Step II can be used to determine the amount of the impact. Project data is always the best source for determining the productivity loss associated with the change orders. The model shown in Eq. (3), can be used to substantiate the project data or as a starting place for negotiations. This model could be used in conjunction with many of the techniques for calculating damages presented in the literature review. The flow charts in Fig. 3 illustrate the automated system structure and how it is related to the detection and quantification models. The user interface system, developed with “C++” program language software, asks 16 questions regarding the project characteristics. Details of the 16 questions and sample answers demonstrate an example of how the detection and quantification system could be applied to a project. The presented system does not give the exact result of the productivity losses caused by changes. What they do calculate is an estimation or prediction of the productivity losses based upon a number of project characteristics. The predictions can be used to support a contractor's calculations for the productivity losses, or in cases where there are no data, can be used in place of the lost calculations.
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6. Case study for automated system application
For stakeholders interested in applying the model, this section provides a demonstration of the system on one recently completed project. Badger Mechanical Construction Company entered into a contract to install the mechanical requirements for a commercial building for UW Incorporation in Wisconsin. The contractor (Badger Mechanical) estimated that the project would take 20 weeks with 10,000 work hours to complete. Throughout the project, the owner initiated several change orders which led to the approval of 3500 additional work hours. The Project Manager (PM) for Badger Mechanical was aware that the job had exceeded the original labor hours planned, but did not realize the extent, as he was not monitoring productivity during construction. At the end of the project when the PM was recapping the project, he realized that in order to finish on schedule, 3735 additional work hours (beyond the original change order for 3500 work hours) had been expended to over-man and over-time the project. The PM presented the project recap sheet to the owner and explained that the owner initiated project change orders had impacted his labor a great deal, accounting for over 35% of the total
6.1. Was this project impacted by change orders? Determination of whether the project was impacted or not was very important for this case, as the two parties held different opinions. Calculation results suggest that there is a strong chance (96.77%) that the project was impacted by change orders (see Table 1). Based on the results, the owner agreed that a cumulative impact had been caused by the multiple changes. 6.2. How much is the impact loss? Once the owner agreed that change impact was present, both parties were interested in estimating the loss incurred. However, as in this example, they did not agree on the amount. Obviously, contractors tend to have high expectations in recovering their losses, while owners tend to minimize their responsibility. After some argument, the parties involved in this example decided to apply the quantification model to determine the predicted value of productivity loss. To apply the impact quantification model, the users determined the necessary information (values) from the project information summary above and applied it to the multiple regression model. Table 2 shows the calculation process and result. The calculation result predicted that the impact loss was 19% of the actual hours, amounting to 3275 work hours (0.19 ⁎ 17,235 = 3275). Based on this result, both parties were able to negotiate a proper
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Fig. 3. System structure and relationships with models.
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7. Summary and conclusion A number of studies have attempted to quantify the impact of change orders on project costs and schedule, including those done by CII, NECA, and MCAA. Some of them attempted to develop regression models to quantify the impact loss while others tried to develop
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compensation for the cumulative impact loss caused by multiple changes. Fig. 4 (Appendix) displays the screen-shots and user interface of the automated system implementation. More details of model effectiveness verification and sensitive analysis could be found in previous study [2–4].
Fig. 4. Appendix—automated system application example.
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Fig. 4 (continued).
Artificial Intelligence (AI) based models. However, AI based models are difficult for field personnel to understand and apply. When considering the acceptable level of model accuracy and applicability to users, regression models were identified as the most appropriate method to quantify change order impact. This study developed an automated system for detection and quantification of change orders impact based on previous research using regression models.
The automated system developed in this research was designed to be used in cases where there is not sufficient project data to prove that the project has been impacted or to quantify the amount of the impact. Ideally, the contractor and owner are able to work together to limit the impact of change orders and to properly identify potential cumulative effects. The presented system does not claim to calculate the exact productivity losses caused by changes, but is intended to work as a guide.
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Fig. 4 (continued).
When little information is available, it is able to provide an estimation or prediction of the productivity losses based upon a number of project characteristics. The predictions can be used to support a contractor's calculations for the productivity losses, or in cases where there are no data, can be used in place of the loss calculations. For future examination, the scope of this research needs to be expanded to include other construction disciplines, project delivery
types, and contract types. Expansion of the research scope will allow more contractors and owners to understand how changes affect the productivity on their construction projects. Furthermore, development of web-based automated interface applications is recommended for simplified dissemination of the technology, thereby allowing stakeholders easy access for application to project cases. Also, a web-based interface system will make it possible to
M.-J. Lee et al. / Automation in Construction 18 (2009) 525–535
Acknowledgement This paper was developed based on the data collection of the Construction Industry Institute (CII) and the University of WisconsinMadison research group, and special thanks go to the electrical and mechanical contractors who supplied the data for this study. References
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build a more robust model through the data collected from industry cases.
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