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.     

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.     

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.     

Effect of Preconstruction Planning Effort on Sheet Metal Project Performance

The complexity of construction industry requires the identification of work tasks and the coordination of interactions among them. As a result, construction planning is considered to be one of the most critical steps toward success and is the main focus of past research. Consequently, little research has been performed regarding the preconstruction planning, which is the planning completed by the contractor in the period between project award and project execution. This paper focuses on sheet metal preconstruction planning, primarily that of mechanical and heating ventilations and air conditioning contractors. The research was completed in three phases: phase one gathered data on the current state of preconstruction planning, phase two developed a model sheet metal preconstruction planning process to be used by sheet metal contractors, and phase three validated the model preconstruction planning process. Based on project data collected for this research, projects that used a planning process similar to the model process performed more successfully—they achieved an average profit margin of 23% while projects that were poorly planned experienced an average profit margin of ?3%.     

Labor Productivity Drivers and Opportunities in the Construction Industry

The study of labor productivity in the construction industry is gaining increasing attention as the industry faces multiple problems related to its workforce. This paper presents the results of a survey instrument applied to determine the relative level of relevance of construction labor productivity drivers and opportunities. Owners, general contractors, electrical contractors, mechanical contractors, consultants, and others participated in this survey. Management skills and manpower issues were identified as the two areas with the greatest potential to affect productivity according to survey respondents. Surprisingly, external factors, which are often cited as a major cause for reduced productivity in the construction industry, were considered to be one of the least relevant productivity drivers. These results suggest that respondents consider the improvement of labor productivity within their reach and control rather than determined by external conditions.     

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.     

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.     

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.     

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.     

Modeling and Forecasting Construction Labor Demand: Multivariate Analysis

This paper presents the development of advanced labor demand forecasting models at project level. A total of 11 manpower demand forecasting models were developed for the total project labor and ten essential trades. Data were collected from a sample of 54 construction projects. These data were analyzed through a series of multiple linear regression analyses that help establish the estimation models. The results indicate that project labor demand depends not only on a single factor, but a cluster of variables related to the project characteristics, including construction cost, project complexity attributes, physical site condition, and project type. The derived regression models were tested and validated using four out-of-sample projects and various diagnostic tests. It is concluded that the models are robust and reliable, which merit for contractors and HKSAR government to predict the labor required for a new construction project and facilitate human resources planning and budgeting, and that the methodology used may be applied to develop equally useful models in other subsectors, and in other countries.     

Measuring and Modeling Labor Productivity Using Historical Data

Labor productivity is a fundamental piece of information for estimating and scheduling a construction project. The current practice of labor productivity estimation relies primarily on either published productivity data or an individual’s experience. There is a lack of a systematic approach to measuring and estimating labor productivity. Although historical project data hold important predictive productivity information, the lack of a consistent productivity measurement system and the low quality of historical data may prevent a meaningful analysis of labor productivity. In response to these problems, this paper presents an approach to measuring productivity, collecting historical data, and developing productivity models using historical data. This methodology is applied to model steel drafting and fabrication productivities. First, a consistent labor productivity measurement system was defined for steel drafting and shop fabrication activities. Second, a data acquisition system was developed to collect labor productivity data from past and current projects. Finally, the collected productivity data were used to develop labor productivity models using such techniques as artificial neural network and discrete-event simulation. These productivity models were developed and validated using actual data collected from a steel fabrication company.     

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%.     

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.     

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.     

Impact of Change Orders on Labor Efficiency for Electrical Construction

Change orders are a source of many disputes in today's construction industry. The issue at hand is whether or not the execution of change orders work has a negative impact on overall labor efficiency on a construction project. Previous literature demonstrates evidence that change orders affect labor efficiency. Attempts have been made to quantify these impacts by many researchers, with limited success. Using the electrical construction industry, a research study has been conducted to quantify the impacts of change orders on labor efficiency. In this paper, results of hypothesis testing and regression analysis are presented. A linear regression model that estimates the loss of efficiency, based on a number of independent variables, is also presented. The independent variables used in this model are (1) qualitative and quantitative criteria used to determine whether projects are impacted by changes or not; (2) the estimate of change order hours for the project as a percentage of the original estimate of work hours; (3) the estimate of change order hours for the project; and (4) the total number of years that the project manager had worked in the construction industry. Additional projects were used to validate the model, with an average error rate of 5%. The results of this research study are useful for owners, construction managers, general contractors, and electrical specialty contractors, because they provide a means to estimate the impact of a change order under certain project conditions. This research also identifies factors, which, when understood and effectively managed, may be used to mitigate the impact of a change order on project costs and efficiency.     

Mechanical Contracting Safety Issues

The purpose of this paper is twofold: (1) to determine jobs/tasks associated with current injury, illness, and fatality trends in the mechanical contracting branches of the construction industry; and (2) to identify current safety practices associated with the reduction of risk of these injuries, illnesses, and fatalities. To achieve the project objectives, a survey was designed and sent to Michigan mechanical contractors. To design an adequate survey, the research team first collected background information using U.S. Bureau of Labor Statistics online database, published research, and contractor interviews. Fourteen of the 50 mechanical contracting surveys distributed were completed. The pilot study found eye injuries due to grinding and welding and upper extremity cuts due to sheet metal to be the most frequent mechanical contracting task/injury combination.     

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.     

Quantitative Assessment for Piles Productivity Factors

The management process of any construction operation usually can be defined as a chain of decisions. A decision-maker can bid, plan, and organize the bored pile projects based on an accurate estimate of productivity. To estimate productivity efficiently, piling process qualitative and quantitative factors have to be considered. This paper focuses on assessing the effect of subjective factors on bored pile construction productivity. A productivity index (PI) model is developed to represent this subjective effect in refining productivity assessment using simulation and deterministic techniques. The analytic hierarchy process and fuzzy logic are used to develop the proposed PI model that relies on the actual performance of 10 main piling process subjective factors. Subjective data were collected from bored pile (drilled shaft) contractors considering these subjective factors. The developed PI model implementation to piling process resulted in PI=0.7. This value has been validated using simulation model outputs.     

Contractor Prebid Planning Principles

Planning is an essential function of project management. Yet, many small- and medium-sized contractors do a relatively poor job of operational planning. Better prebid plans will reduce costs, shorten schedules, and improve labor productivity. Unfortunately, the published literature offers little guidance for smaller contractors on what constitutes effective planning. Most papers describe planning as a macrolevel process for owners. Most emphasize scope definition for industrial projects. This paper describes a microlevel planning process for contractors. It consists of eight steps which are: (1) assess contract risks; (2) develop a preliminary execution plan; (3) develop site layout plans; (4) identify the sequences that are essential-to-success; (5) develop detailed operational plans; (6) develop proactive strategies to assure construction input into design; (7) revise the preliminary plan; and (8) communicate and enforce the plan. The entire process is illustrated with a case study project and is fully illustrated with figures which show how to integrate the work of multiple contractors, keep key resources (crews or equipment) fully engaged with no downtime, provide time buffers so the work of follow on crews can be efficiently done, expedite the schedule using multiple work stations and concurrent work, ways to communicate the work plan to the superintendent and foremen, and how to assess the feasibility of various work methods. The steps are easy to understand and implement. They will yield immediate positive results.     

Delay Time Analysis in Microtunneling Projects

Delay in microtunneling projects is a complex multivariate problem. Delay in microtunneling is defined as the nonworking time of a microtunneling project due to any reason other than scheduled stops. There are many reasons for delay such as mechanical failure of system components, leakage of hydraulic hoses, blockage of slurry pipes, and waiting time for excavated materials hauling equipment. Delay time increases the project duration and consequently the project cost. Delay data were collected from 35 microtunneling projects. Collected delay data were delay duration, delay reason, time, and location from the start to the stopping point. Five categories of delay causes were used in the analysis. Prediction of delay time will enhance the estimation accuracy of microtunneling project duration. A predictive model using a probabilistic approach was selected to represent the delay time. Based on data characteristics, a Weibull distribution was determined to best represent the overall delay duration in microtunneling projects. Using “regression with life data,” expected overall delay in a microtunneling project could be predicted as a function of driven length. The model will help contractors to estimate total project time with reasonable accuracy. Knowing the anticipated delay time will allow contractors to have a point of comparison for actual performance.