Impact of Change Orders on Labor Efficiency for Mechanical Construction

Change orders have become an everyday occurrence in construction. It is widely accepted by both owners and contractors that change orders have an effect on the labor efficiency, but these effects are difficult to quantify and frequently lead to disputes. Data from 61 mechanical construction projects were collected to develop a statistical model that estimates the actual amount of labor efficiency lost due to the change orders. The input variables needed in the model are as follows: (1) The original estimated labor hours; (2) impact classification; (3) total estimated change hours; (4) number of change orders; and (5) the timing of changes. The results of this study show that impacted projects have a larger decrease in labor efficiency than unimpacted projects. Additionally, the later a change order occurs in the life of a project the more impact it will have on the labor efficiency. The results appear to be consistent with the intuition of experienced professionals. Although each project has unique characteristics, the resultant model provides owners and contractors with a baseline measure of lost labor efficiency.     

Cumulative Effect of Project Changes for Electrical and Mechanical Construction

Change is inevitable on construction projects, primarily because of the uniqueness of each project and the limited resources of time and money that can be spent on planning, executing, and delivering the project. Change clauses, which authorize the owner to alter work performed by the contractor, are included in most construction contracts and provide a mechanism for equitable adjustment to the contract price and duration. Even so, owners and contractors do not always agree on the adjusted contract price or the time it will take to incorporate the change. What is needed is a method to quantify the impact that the adjustments required by the change will have on the changed and unchanged work. Owners and our legal system recognize that contractors have a right to an adjustment in contract price for owner changes, including the cost associated with materials, labor, lost profit, and increased overhead due to changes. However, the actions of a contractor can impact a project just as easily as those of an owner. A more complex issue is that of determining the cumulative impact that single or multiple change orders may have over the life of a project. This paper presents a method to quantify the cumulative impact on labor productivity for mechanical and electrical construction resulting from changes in the project. Statistical hypothesis testing and correlation analysis were made to identify factors that affect productivity loss resulting from change orders. A multiple regression model was developed to estimate the cumulative impact of change orders. The model includes six significant factors, namely: Percent change, change order processing time, overmanning, percentage of time the project manager spent on the project, percentage of the changes initiated by the owner, and whether the contractor tracks productivity or not. Sensitivity analysis was performed on the model to study the impact of one factor on the productivity loss (%delta). The model can be used proactively to determine the impacts that management decisions will have on the overall project productivity. They may also be used at the conclusion of the project as a dispute resolution tool. It should be noted that every project is unique, so these tools need to be applied with caution.     

Impact of Change Orders on Small Labor-Intensive Projects

In today’s construction, small projects can be just as important if not more important than the larger projects. However, small projects are usually fast track projects, which often involve overlapping design and construction time. Subsequent modifications may be required for the sections that are already under construction. These disruptions to the ongoing project are labeled as change orders. The impact due to changes has been described as the adverse effect upon the unchanged work due to changes in the contract. For this study, 34 projects were selected to develop a statistical model that estimates the amount of labor efficiency lost due to change orders for small projects. The variables in the final model are percent design related changes, percent owner initiated changes, the ratio of actual peak labor to estimated peak labor, the ratio of actual project duration to estimated project duration, and project manager’s percent time on the project. The results of this paper are of value to owners, electrical and mechanical contractors, and construction managers. The model quantifies the impact of change orders by introducing the most important variables that bring the largest disruptions.     

Model for Quantifying the Impact of Change Orders on Project Cost for U.S. Roadwork Construction

Change orders are very common in almost every construction project nowadays, often resulting in increases of 5–10% in the contract price. Understanding the consequences of such trends, several studies have attempted to quantify the impact of change orders on the project cost. Most of the studies aimed at the quantification of the change orders were sponsored by contractors’ organizations, where statistical models used to quantify the impact of the change orders on the project cost were based on data supplied by the contractors; a situation that can lead to owner-contractor disagreements related to the quantification method used. In addition, most of the studies tackled commercial and electromechanical work, and very rare studies tackled the field of heavy construction; a field that suffers from change orders because of errors and omissions, scope of work changes, or changes because of unforeseen conditions. This study addresses the need for a statistical model to quantify the increase of the contract price due to change orders in heavy construction projects in Florida. The model is based on data collected from 16 Florida DOT projects with contract values that ranged between $10–$25 million, and that encountered an increase in the contract price from 0.01 to 15%. Eleven variables were analyzed to test their impact on the cost of the change orders. The study concluded that most significant variables that impact the value of the change order, which are (1) the timing of the change order and (2) when the reason for issuing the change order is unforeseen conditions. Two regression models are developed and validated as follows: (1) a model to quantify the percentage increase in the contract price due to the change orders that increase the contract price from 0.01 to 5% and (2) a model to quantify the percentage increase in the contract price due to the change orders that increase the contract price from 5 to 15%. Those models will provide the owner with a retrospective or forward pricing of the change orders, and hence, allow the owner to estimate and utilize contingency amounts.     

Quantitative Definition of Projects Impacted by Change Orders

Change, defined as any event that results in a modification of the original scope, execution time, or cost of work, is inevitable on most construction projects due to the uniqueness of each project and the limited resources of time and money available for planning. Change may occur on a project for a number of reasons, such as design errors, design changes, additions to the scope, or unknown conditions. For each change, contractors are entitled to an equitable adjustment to the base contract price and schedule for all productivity impacts associated with the change. Changes may or may not have an impact on labor productivity. Existing literature uses subjective evaluation to determine whether the project is impacted. Projects impacted by change cause the contractor to achieve a lower productivity level than planned. The focus of this paper is to quantify whether an electrical or mechanical project is impacted by a change order. Through statistical hypothesis testing, groups of factors that correlate with whether a project is impacted by change orders were identified and used to develop a quantitative definition of impact. Logistic regression techniques were used to develop models that predict the probability of a project being impacted. The results of this research show that percent change, type of trade, estimated and actual peak manpower, processing time of change, overtime, overmanning, and percent change related to design issues are the main factors contributing to the project impact.     

Decision Tree Approach to Classify and Quantify Cumulative Impact of Change Orders on Productivity

Multiple or unusual change orders often cause productivity losses through a “ripple effect” or “cumulative impact” of changes. Many courts and administrative boards recognize that there is cumulative impact above and beyond the change itself. However, determination of the impact and its cost is difficult due to the interconnected nature of construction work and the difficulty in isolating causal factors and their effects. As a result, it is very difficult for owners and contractors to agree on equitable adjustments for the cumulative impact. What is needed is a reliable method (model) to identify and quantify the loss of productivity caused by the cumulative impact of change orders. A number of studies have attempted to quantify the impact of change orders on the project costs and schedule. Many of these attempted to develop regression models to quantify the loss. However, traditional regression analysis has shortcomings in dealing with highly correlated multivariable data. Moreover, regression analysis has shown limited success when dealing with many qualitative or noisy input factors. Classification and regression tree methods have the ability to deal with these complex multifactor modeling problems. This study develops decision tree models to classify and quantify the labor productivity losses that are caused by the cumulative impact of change orders for electrical and mechanical projects. The results show that decision tree models give significantly improved results for classification and quantification compared to traditional statistical methods in the field of construction productivity data analysis, which is characterized by noisiness and uncertainty.     

Benchmarking Productivity Indicators for Electrical/Mechanical Projects

Labor-intensive industries such as the electrical and mechanical trades are considered high risk due to the high percentage of labor costs. Because of this high risk, it is important for contractors in these industries to closely track labor costs on projects and compare these costs to industry benchmarks. In this paper, benchmark indicators for these industries are established on the basis of actual project data. These benchmarks include the relationship between the percent complete or percent time and cumulative work hours or cost, project size and duration, project size and average man power, project size and peak man power, and average versus peak man power. These relationships were developed using regression analysis. Man power loading charts and the related S-curves were developed from actual project data. The man power loading charts and the related S-curves are useful for resource planning and for tracking progress on a construction project. They can be used to show the cause-and-effect relationship between projects impacted by outside factors and normal labor productivity.     

Quantifying the Impact of Schedule Compression on Labor Productivity for Mechanical and Sheet Metal Contractor

In a typical construction project, a contractor may often find that the time originally allotted to perform the work has been severely reduced. The reduction of time available to complete a project is commonly known throughout the construction industry as schedule compression. Schedule compression negatively impacts labor productivity and consequently becomes a source of dispute between owners and contractors. This paper examines how schedule compression affects construction labor productivity and provides a model quantifying the impact of schedule compression on labor productivity based on data collected from 66 mechanical and 37 sheet metal projects across the United States. The model can be used in a proactive manner to reduce productivity losses by managing the factors affecting productivity under the situation of schedule compression. Another useful application of the model is its use as a litigation avoidance tool after the completion of a project.     

Schedule Acceleration, Work Flow, and Labor Productivity

This paper details how construction labor efficiency is affected by deviations from the normal flow of work. A methodology is presented to estimate the loss of labor efficiency, based on variations in the percentage of labor hours used each week. The procedure can be used without the need for contractor production records. The theoretical basis for the method rests on the assumption that the rate of manpower utilization is consistent with the amount of work available for the contractor to perform. Using productivity data from three electrical projects that were accelerated, the labor efficiency is shown to be correlated to changes in the percentages of weekly work hours. Loss of efficiency curves are developed for various project phases. A case study is presented of an actual electrical construction project. Losses of efficiency are calculated for each phase, and it is shown that the contractor incurred an estimated loss of productivity of 25%. The analysis is validated by comparing a weekly inefficient work-hour profile to the chronology of events that occurred on the project.     

Conceptual Planning Process for Electrical Construction

The competitive nature of the construction industry has motivated many specialty contractors to search for ways to improve efficiency by increasing their quality and decreasing their costs in order to strengthen their market share. As a result, contractors are turning to “better planning” as a method for improving their efficiency and, consequently, increasing their profitability. In fact, a consensus exists in the construction industry that more formalized preconstruction planning is necessary to remain successful in an increasingly competitive industry. This paper presents a model electrical preconstruction planning process that was crafted from outstanding processes used on several successful electrical projects. Furthermore, a method to evaluate the effectiveness of planning, by comparing actual planning to the model process, is briefly introduced. From this assessment, “effective planning” was correlated to project outcome, and evidence is provided that better planning is, indeed, related to successful performance.     

Quantitative Impacts of Project Change: Size Issues

Change has a tremendous effect on the performance of a construction project. Research that focuses on the quantitative impact is limited, incomplete, and in some cases questionable. The goals of this study were to quantify the nature and impacts of project change and develop recommended practices so that owners and contractors can manage change better. The focus was on project change during detailed design and construction, in particular the size of change and its impact on the project. These results show that the amount of change is negatively correlated with productivity and total installed project cost, whether within the design phase or construction phase, or between them. The greater the amount of change the more productivity and costs are degraded. Recommendations are also offered here on how to mitigate the impact of project change.     

Role of Workforce Management in Bridge Superstructure Labor Productivity

There have been many studies on different aspects of the construction process in regard to how they each impact construction productivity. In reviewing the documentation of this research, very few articles were located that dealt with heavy/highway construction in general, and even fewer were found that dealt with bridges in particular. In addition, very little was found in the literature dealing with the effect that the quality of workforce management has on construction productivity. This paper describes the results of four case studies of highway bridge construction performed by established contractors with little bridge building experience, in which workforce management had a significant negative effect on labor productivity. The contractors’ lack of experience in bridge construction seemed to be the cause of several problems that plagued each of the four projects. The baseline productivity of each project was calculated, and the loss of labor efficiency was estimated to be 80, 75, 32, and 70%, respectively. The schedule slippage on the four case study projects was estimated to be between 127 and 329%.     

Effects of Delivery Systems on Change Order Size and Frequency in Mechanical Construction

Change orders represent one of the largest sources of cost growth on building construction projects. Field generated, or “unforeseen” change orders can also be highly disruptive to field productivity. Design-build delivery methods can potentially help minimize change orders on construction projects. This study was performed to closely examine the effects of delivery methods on the frequency and magnitude of change orders in mechanical construction, and how design-build business practices can be used to minimize the frequency of field generated chance orders. In a study of 598 change orders occurring on 120 construction projects performed by the same contractor, the total number of change orders was found to be close to the same on design-build and design-bid-build projects, however an 87% decrease in the average number of unforeseen change orders was observed on design-build projects versus design-bid-build projects. In addition, the average size of unforeseen change orders was 86% smaller on design-build projects. A detailed and qualified presentation of the research methodology and resulting data is provided. Key attributes and business practices leading to the results are discussed and practical applications of this research for owners and contractors are provided.     

Union and Open Shop Labor Cost Differences

One area within the construction industry that has dramatically changed is the growth of open shop construction. This growth suggests that open shop general contractors may have a competitive advantage over union general contractors. This paper investigates productivity and wage rate variances between Colorado open shop and union general contractors on projects completed since January 1981. Though there is little productivity variance, a significant variance of wage rates indicate the Colorado open shop general contractor presently has a clear overall labor advantage. The productivity and wage rate variance of 29 individual project job tasks were investigated. Data was collected from 35 union and 20 open shop contractors' project labor costs reports. Additional information was collected during interviews with representatives of eleven general contractors. The productivity and wage rate variances are described by individual job tasks; by the union craft jurisdictions of laborers, carpenters, ironworkers, and cement finishers; and by an overall comparison between open shop and union general contractors.     

Impact of Change’s Timing on Labor Productivity

There are many types of construction changes and each type can have an effect on labor productivity. To a certain extent though the specific type of change is not as important as the mere presence of the change and, as analyzed in this paper, the timing of that change. The research reported in this paper reaffirms that project change is disruptive and detrimental to labor productivity. Data from 162 construction projects were statistically analyzed and a series of three curves are presented in this paper, representing the impact that change has on the labor productivity for early, normal, and late timing situations. The projects are a representative sample of the industry, involving a wide range of sizes, different delivery systems, and industry sectors. Late change is more disruptive of project productivity than early change, all other things being equal. The implications and benefits of this research are clear: if changes are necessary, they should be recognized and incorporated as early as possible. Practitioners can use these data and curves for either forward pricing or retrospective pricing of changes. Other researchers can use these findings to test their own findings and to explore timing issues in further detail.     

Impact of Overmanning on Mechanical and Sheet Metal Labor Productivity

This paper details the impacts of overmanning on labor productivity for labor-intensive trades, namely mechanical and sheet metal contractors. Overmanning, as used in the following research, is defined as an increase of the peak number of workers of the same trade over the actual average manpower used throughout the project. The paper begins by reviewing literature on the effects of overmanning on labor productivity. Via a survey to various contractors, data were collected from 54 mechanical and sheet metal projects located across the United States. Various statistical analysis techniques are then performed to determine quantitative relationship between overmanning and labor productivity. These techniques include the stepwise method, T-test, P-value tests, analysis of variance, and multiple regression analysis. The results indicate a 0–41% loss of productivity depending on the level of overmanning and the peak project manpower. Cross-validation is performed to validate the final model. Finally, a case study is provided to demonstrate the application of the model.     

Impact of Extended Overtime on Construction Labor Productivity

This paper presents an analysis of the impacts of extended duration overtime on construction labor productivity. The results show a decrease in productivity as the number of hours worked per week increase and/or as project duration increases. The research focuses on labor intensive trades such as the electrical and mechanical trades. Overtime in this research is defined as the hours worked beyond the typical 40 h scheduled per week. The paper begins by presenting the effects of overtime and the need for an updated overtime productivity model. Data for the quantitative analysis was collected from 88 projects located across the United States by means of a questionnaire. Various statistical analysis techniques were performed to develop quantitative relationship curves, including multiple regression, P-value tests, and analysis of variance.     

Owner’s Role in Construction Safety

Despite dramatic improvements in recent decades, the construction industry continues to be one of the industries with the poorest safety records. Recent improvements are due, in part, to the concerted efforts of owners, contractors, subcontractors, and designers. While past safety studies have investigated the roles of contractors, subcontractors, and designers, the owner’s impact on construction safety has not been previously investigated. This paper will present the results of a study on the owner’s role in construction safety. Data were obtained by conducting interviews on large construction projects. The relationship between project safety performance and the owner’s influence was examined, with particular focus on project characteristics, the selection of safe contractors, contractual safety requirements, and the owner’s participation in safety management during project execution. By identifying practices of owners that are associated with good project safety performances, guidance is provided on how owners directly impact safety performance.     

Change Orders Impact on Labor Productivity

This paper describes a study conducted to investigate the impact of change orders on construction productivity and introduces a new neural network model for quantifying this impact. The study is based on a comprehensive literature review and a field investigation of projects constructed in Canada and the USA. The field investigation was carried out over a 6-month period and encompassed 33 actual cases of work packages and contracts. Factors contributing to the adverse effects of change orders on labor productivity are identified and a model presented earlier is expanded to account primarily for the timing of change orders, among other factors. The developed model, as well as four models developed by others, have been incorporated in a prototype software system to estimate the loss of labor productivity due to change orders. A numerical example is presented to demonstrate the use of the developed model, and illustrate its capabilities.