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.     

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.     

Causes, Effects, Benefits, and Remedies of Change Orders on Public Construction Projects in Oman

Change orders are usually issued to cover variations in scope of work, material quantities, design errors, and unit rate changes. This paper discusses variations in public construction projects in Oman by investigating causes of variations, studying their effects on the project, identifying the beneficial parties, and suggesting remedies to alleviate related problems. Tasks included an analysis of four actual case studies and conducting a field survey via a questionnaire. It was determined that the client’s additional works and modifications to design were the most important factors causing change orders, followed by the nonavailability of construction manuals and procedures. The most important effects of change orders on the project were found to be the schedule delays, disputes, and cost overruns. The contractor was found to be the party most benefiting from the change orders followed by the consultant and then the client. A set of remedial actions were suggested and respondents viewed that the revision of registration of consulting offices would be the most important action followed by establishing standard documents for design procedures and building a national database about soil conditions and services.     

Avoiding Change Orders in Public School Construction

Increased student enrollment and the current poor state of the educational infrastructure require the construction of more school buildings and the renovation of many of the existing ones. The large number and magnitude of change orders in these projects constitute an impediment to the rapid and economic delivery of these projects. A total of 6,585 change orders filed in a school district’s projects in the 5 1/2 year period from 1999 to 2004 were analyzed in five categories including owner-directed changes, code compliance issues, errors/omissions in contract documents, discovered or changed conditions, and others. The results of the study indicate that the dollar value of change orders relative to the original contract can be reduced if preventive measures are taken. These measures include choosing the right construction management firm, emphasizing the definition of project scope early in the project, and effectively managing the precontract activities by conducting value engineering and constructability reviews. The results indicate that school projects can be completed with change orders not exceeding 5% of the contract value if these measures are taken. This study is of relevance to practitioners involved in school design and construction projects.     

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.     

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.     

Frequency of Change Orders in Highway Construction Using Alternate Count-Data Modeling Methods

A common problem at state transportation agencies is the inability to complete projects within the original scope of work. Change orders, which are contractual documents issued to accommodate the additional work in a contract, are generally due to root causes such as design errors, unexpected site conditions, and weather conditions, and intermediate causes such as bidding characteristics. At the preaward phase of project management, an improved understanding of the factors that are associated with change orders will be of value and also can serve as a basis for taking steps to reduce concomitant contractual aberrations such as time delay and cost overruns. Recognizing that the occurrence of change orders is a count variable, this paper analyzes the frequency of change orders using a variety of count-modeling methods including the negative binomial, Poisson, zero-inflated negative binomial, and zero-inflated Poisson. Using 5 years of contract data from Indiana highway projects, appropriate models are estimated to assess the influence of project type, contract type, project duration, and project cost on the frequency of change orders.     

Web-Enabled System Dynamics Model for Error and Change Management on Concurrent Design and Construction Projects

Construction projects are uncertain and complex in nature often because of iterative cycles caused by errors and changes. These errors and changes impair project performance and, consequently, cause schedule and cost overruns to be prevalent. Iterative cycles are more detrimental when design and construction are concurrent and often force activities to proceed without complete information. In an effort to address this issue, this paper presents the information technology aspect of the dynamic planning and control methodology (DPM), which provides a mechanism that will analyze the impact of negative iterative cycles on construction performance. In order to guarantee a smooth application of this method to real-world projects, DPM has been developed by integrating several existing methods around a core system dynamic model for quality and change management and then implementing these methods into a web-based collaborative environment. A case project, applying the developed web-based DPM, shows great potential in facilitating on-site decision making by virtue of its support of data analysis as well as real-time information sharing.     

Quality and Change Management Model for Large Scale Concurrent Design and Construction Projects

Concurrent design and construction has been lauded for streamlining projects in terms of time. However, such an approach may actually make projects more uncertain and complex than the traditional sequential design and construction process. The main sources of risk that have been identified with concurrent design and construction are iterative cycles that result from unanticipated errors and changes and their subsequent impacts on project performance. As an effort to address these detrimental impacts, a framework for quality and change management that identifies those negative iterative cycles is proposed. The proposed framework is incorporated into the system dynamics model of dynamic planning and control methodology (DPM), which has been developed to evaluate negative impacts of errors and changes on construction performance. Relevant to practitioners and researchers, the potential of DPM as a robust design and construction planning methodology that could effectively deal with errors and changes inherent in the design and construction process is demonstrated through a case study involving the Treble Cove road bridge in Massachusetts.     

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.     

Partnering on Small Construction Projects

Application of partnering concepts on small construction projects by owners or government functionaries could result in substantial savings for private owners and public exchequers annually. This paper develops a concept for partnering for small construction projects based on recent management research and theories being developed in universities with the lessons learned from the U.S. Army Corps of Engineers experiences on partnering for large projects. The discussion addresses current attitudes toward partnering, particularly, in small (i.e., up to $3,000,000) private or government sponsored projects. An objective of this paper is to share the benefits of partnering on small construction projects, based on practices and experiences of the U.S. Army Corps of Engineers. A final objective is to develop a primer for construction practitioners.     

Statistical-Fuzzy Approach to Quantify Cumulative Impact of Change Orders

This paper presents a hybrid approach to quantify the impact of change orders on construction projects using statistical regression and fuzzy logic. There are many qualitative variables affecting the impact of change orders on labor productivity; statistical analysis falls short of addressing the fuzziness of those variables. Because of their complementary nature, fuzzy logic and regression analysis can be integrated; regression analysis is used to determine the membership functions of the input linguistic values. In this paper, each input variable is statistically treated before entering a general rule relating its space to the space of loss in labor productivity. The relative weight of each input variable is determined by its coefficient of determination (R2) value. The expected loss of labor productivity and its standard deviation are then determined from the output fuzzy membership function. The proposed methodology is general and can be applied in areas of system analysis and decision making when a complex input-output function is to be predicted in the presence of some fuzzy knowledge and a large number of real input-output data.     

Safety Hazard Identification on Construction Projects

Hazard identification is fundamental to construction safety management; unidentified hazards present the most unmanageable risks. This paper presents an investigation indicating the current levels of hazard identification on three U.K. construction projects. A maximum of only 6.7% of the method statements analyzed on these projects managed to identify all of the hazards that should have been identified, based upon current knowledge. Maximum hazard identification levels were found to be 0.899 (89.9%) for a construction project within the nuclear industry, 0.728 (72.8%) for a project within the railway industry, and 0.665 (66.5%) for a project within both the railway and general construction industry sector. The results indicate that hazard identification levels are far from ideal. A discussion on the reasons for low hazard identification levels indicates key barriers. This leads to the presentation of an Information Technology (IT) tool for construction project safety management (Total-Safety) and, in particular, a module within Total-Safety designed to help construction personnel develop method statements with improved levels of hazard identification.     

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.     

Risk Allocation by Law—Cumulative Impact of 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. There are many factors that may cause a change such as design errors, design changes, additions to the scope, or unknown conditions in the field. 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. The focus of this paper is to outline the types of changes that can occur on a construction project and also to spell out the financial recovery possibilities that exist for the contractor for each type of change. There are many historical and current court decisions that shape the outcomes of such claims and determine who holds the risks associated with various project changes. Also, an effective cumulative impact claim contains certain vital elements upon which the final outcome will be determined by the legal system. Last, there are certain actions that a contractor and owner can do to either enhance or mitigate the effectiveness of a potential cumulative impact claim.     

CAD and Management of Construction Projects

The increasing interest in computer‐aided design (CAD) has prompted research that is aimed at identifying the opportunities for construction managers and building contractors. It has been found that the use of CAD systems in the U.K. is mainly confined to the production of detailed drawings. Indeed, most of the systems used are 2‐D drafting tools and incapable of supporting the integration of even modest amounts of nongraphical (construction) data. On the other hand, many 3‐D modeling systems have the potential to integrate construction data, although they appear to be almost ignored. The use of 3‐D modeling systems is considered to be the most suitable vehicle for successfully integrating these data. However, this is likely to necessitate the introduction of separate databases, preferably of the relational type. The use of 3‐D modeling systems in assessing the construction implications of outline designs also presents interesting possibilities and is discussed.