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.     

Contractor’s Opportunistic Bidding Behavior and Equilibrium Price Level in the Construction Market

The competitive bidding system has been to blame for abnormally low bids, which are considered as one of the main causes of poor project quality. Previous studies have regarded the pricing of bidders as an optimum decision based on contractor’s cost and market competition level. However, the sell to produce characteristic of construction projects may induce contractors to offer a low bid and then make up the amount initially sacrificed from beyond-contractual reward (BCR) gained through cutting corners and claims. System dynamics was adopted in this study to develop a contractor’s pricing model with consideration of the dimensions of cost, market competition, and BCR. The model was then examined by statistical analysis of data collected from 44 highway projects in Taiwan. It was found that the equilibrium market price is significantly associated with BCR, which is assumed to be determined by the strictness of the owner’s construction management, including both soundness of contract and tightness in construction supervision. Research results suggest that contractors divide the market into different segments according to the owner’s strictness of construction management and the equilibrium price level of each market segment varies. The price level for projects with a strict owner is remarkably higher than for those with relatively less strict owners. Improvement in the construction management system of projects is crucial to lower the possibility that contractors gain BCR and do opportunistic bidding, and to further enhance project quality.     

SIM-UTILITY: Model for Project Ceiling Price Determination

Before considering bids submitted by competing contractors for a public procurement project, the owner should determine a project ceiling price or cost estimate to use as a reference point for evaluating the bids. A high ceiling price conflicts with the owner’s interests in minimizing costs. Meanwhile, a low ceiling price can jeopardize the project if all bids exceed the ceiling price. This paper proposes a model for determining a reasonable project ceiling price. The model, called SIM-UTILITY, is based on a utility theory and facilitated by a cost simulation approach. The utility theory is applied to reflect the owner’s preferences regarding the determination criteria, while the simulation approach is used to generate more objective project cost data to support execution of the utility theory. The advantages of SIM-UTILITY are proven by its successful application to three construction projects in Taiwan. A computerized SIM-UTILITY is expected to be broadly applicable to public construction projects in Taiwan.     

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.     

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.     

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.     

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.     

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.     

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.     

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.     

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.     

Analysis of Owner’s Design and Construction Quality Management Approaches in Design/Build Projects

This paper explores and classifies current approaches to evaluating quality in design/build (DB) proposals. It does so by a thorough content analysis of 78 requests for proposal (RFPs) for public DB projects with an aggregate contract value of over $3.0 billion advertised between 1997 and 2002. In most DB projects, the owner requires the DB contractor to establish a firm-fixed price on a project that has not yet been designed. Usually, the owner also fixes the project delivery period. In the traditional design/bid/build (DBB) system, quality is fixed through the plans and specifications. Thus, in DBB, with schedule and quality fixed, the cost of construction is the factor in which the owner seeks competition. Conversely, in DB, with cost and schedule fixed, the scope and hence the level of quality is the main element of competition. This paper identifies the six owner approaches to articulating DB quality requirements in their RFPs. The six approaches are quality by qualifications, evaluated program, specified program, performance criteria, specification, and warranty. These are important for DB contractors to understand so that they can craft their proposal in a manner that is both responsive to the owners’ requirements and consistent with the owner’s system to make the best value contract award decision.     

Amplitudes of the two-nucleon interaction at 579 MeV

There has been increasing emphasis in recent years on construction contract claims and disputes. This paper examines the frequency of occurrence of 427 separate construction claims which were experienced on 22 federally funded and administered construction projects. The data examined include various claim types, the frequency of their occurrence and the average cost of these claims. Also, various factors thought to influence the frequency of claims occurrence were investigated. This study indicates that the largest proportion of change orders and modifications originate with the owner of the project or with those responsible to the owner.     

Risk Assessment Model of Tendering for Chinese Building Projects

This paper presents a risk assessment model for tendering of Chinese building projects on the basis of identification and evaluation of the major risk events in the Chinese construction market, investigations and interviews from which the factors inducing the risk events were determined, questionnaires on building projects within China’s borders, and the logistic regression method. The findings show that, to a certain extent, the risk of tendering for projects and the risk of a contracted project can be assessed through analysis of factors such as owner type, source of project financing, existence or lack of past cooperation between contractors and owners, the intensity of competition for tendering, the reasonableness of the bid price, and the degree of support from the contracting company to its projects. The model can serve as a supplementary tool for Chinese contractors in making decisions for project tendering within Chinese borders. At the same time, it is of reference significance for international contractors, enabling them to further understand the risks in the contract market for Chinese building projects.     

Owner’s Role in Construction Safety: Guidance Model

The practices implemented by owners that were associated with better project safety performances were described in a companion paper. This paper will expand on those research findings and present a model that evaluates the impact of different owner practices on project safety performance. Four indices were established which quantitatively describe the project characteristics, the selection of safe contractors, the contractual safety requirements, and the owner’s participation in safety management. Analysis of variance and linear regression were conducted to evaluate the combined effects of different owner practices on project safety performance as measured by the total recordable injury rate (TRIR). Results show that project safety performances were closely associated with all the indices except the index to describe the selection of safe contractors. The linear regression model explained 42.6% of the total variance of the TRIR. A scorecard to evaluate the owner’s involvement in construction safety was developed to serve as a practical means of conducting an assessment and predicting project safety performance. The owner’s involvement can favorably influence project safety performance by setting safety objectives, selecting safe contractors, and participating in safety management during construction.     

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.     

Reconciliation of Owner and Contractor Views in Heavy Construction Projects

Delay and loss of productivity are the two main types of damage experienced by the contractor when the owner issues a change order. Courts have recognized critical path method schedule analysis as the preferred method of identifying and quantifying critical delays. As for the inefficiency damages, there is no direct way of measuring inefficiency due to its qualitative nature and the difficulty of linking the cause of the productivity loss to the damage. Most of the scholarly work published in this area was based on data supplied by the contractors; and that explains why there are discrepancies between what the contractor asks for and what the owner believes the contractor is entitled to. This study addresses the need for a statistical model to quantify the productivity loss from verifiable site data such as owner’s daily reports, change orders, drawings, and specifications, rather than rely solely on contractor surveys. A model is developed and validated to quantify the productivity loss in pipe work in roadway projects due to the change orders. The productivity loss study analyzed two sets of data that include: (1) variables that predict which of the two parties, the owner and the contractor, contributed to the productivity loss; and (2) variables that predict, from the legal viewpoint, productivity losses which only the owner is responsible for. The study showed the difference between what the contractor asked for and what he/she is actually entitled to. This model can be used by both the owner and the contractor to quantify the productivity loss due to change orders, and to offer an objective approach to reconcile their differences. This study concludes with an example to demonstrate the use of the model.     

Time Series Analysis of ENR Construction Cost Index

Every month, Engineering News-Record (ENR) publishes the construction cost index (CCI), which is a weighted aggregate index of the 20-city average prices of construction activities. Although CCI increases over the long term, it is subject to considerable short-term variations, which make it problematic for cost estimators to prepare accurate bids for contractors or engineering estimates for owner organizations. The ability to predict construction cost trends can result in more-accurate bids and avoid under- or overestimation. This paper summarizes and compares the applicability and predictability of various univariate time series approach for in-sample and out-of-sample forecastings of CCI. It is shown that the seasonal autoregressive integrated moving-average model is the most-accurate time series approach for in-sample forecasting of CCI, while the Holt-Winters exponential smoothing model is the most-accurate time series approach for out-of-sample forecasting of CCI. It is also shown that several time series models provide more-accurate out-of-sample forecasts than the ENR’s subject matter experts’ CCI forecast. Cost estimators can benefit from CCI forecasting by incorporating predicted price variations in their estimates and preparing more-accurate bids for contractors and budgets for owners. Owners and contractors can use CCI forecasting in reducing construction costs by better-timed project execution.     

Profit Measures for Construction Projects

The basic issues related to profit measures of a construction project for the contractor are presented. Specifically, the problem of measuring gross operating profit and financing costs under fluctuating economic environments which has been heretofore inadequately treated in the literature of construction management is addressed. A framework for analyzing financing costs under different financial mechanisms, including the effects of overdraft and other borrowing arrangements, is presented. The general procedure is also applicable to the analysis of the effects of inflation and of work stoppages on profit. Finally, the cost of a project to the owner and the relationship between uncertainty and contract price from the views of both the owner and the contractor are considered.