• International conference on construction engineering and project management
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• 2009.05a
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• pp.1283-1287
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• 2009

The Korean government implemented 259 road projects from 2004 to 2007, valued at $18.4 billion. Change orders of these road projects occurred 8,973 times and, subsequently, caused significant increases in the cost of the projects, approximately up to $4.2 billion (22.8% of the initial budget). These significant problems of huge change orders require a more workable control system for budget management whereas the effectiveness of the government's control is still not satisfied. However, previous approaches and studies mostly limited their analyses to simply classifying the causes of the change orders. This paper investigates the real frequency and cost impacts incurred by each cause of a change order, primarily based on 218 road projects in Korea. The paper then identifies the attributes of change orders through a survey of 204 project participants in that those sources were inevitable or avoided if properly managed. The causes of the change orders are further analyzed with analysis of variance (ANOVA) in connection with contract volume, bid award rate, the contractor's capacity to perform, and the design company's capacity. This study found that if the contract volume is smaller, then the possibility of change orders is higher. Interestingly, if the bid award rate is less than 67.5%, it signifies the highest rate of change orders. In addition, the contractors whose construction ability is assessed as the top-ranked group showed the lowest change order rates. With these results, this paper provides the preventive guidelines for reducing the likelihood of change orders.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.188-195
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• 2022

Although issuing change orders is a common practice in the construction phase of any project, non-structural utility subcontractors are struggling and seek to find a way to reduce change orders. Therefore, this paper presents the analysis results on change orders to cultivate possible suggestions and solutions on how to reduce or minimize change orders in mechanical, electrical, and plumbing (MEP) works. Change orders in non-structural utility works are analyzed based on six categories such as rerouting and change of location, changes in weight, rejected design by Office of Statewide Health Planning and Development, District Structural Engineer, or the Structural Engineer of Record, unforeseen conditions, changed equipment, and owner-initiated change. The analysis findings showed that rerouting and changing location is the most significant cause, followed by unforeseen conditions. The results not only contribute to the existing body of knowledge on change order research area, but also help MEP contractors reduce the time and cost of change orders.

• Journal of the Korea Institute of Building Construction
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• v.13 no.3
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• pp.263-271
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• 2013

Change orders have been widespread in both the private and public construction sectors. In particular, in the case of G2B (Government to Business) contracts, adjustment of contract price and/or schedule extension is a frequent occurrence due to change orders. To uncover the causes of change orders and suggest an appropriate strategy, this study analyzed 296 cases of change orders in military facility construction projects from 2008 to 2010. The analysis revealed that the major causes of change orders are users' additional requirements (28.38%), a change of finishing materials (23.99%), and change of footing type (17.57%), in that order. Building on the results of this analysis, the authors suggest plans for practical improvements. Specific recommendations include 1) reflect user requirements at the early stage, 2) minimize the use of additional budget due to change orders, and 3) reduce the process and time for contract amendment, among others. The results of this study may provide significant implications to those involved in military construction projects, particularly project owners (i.e., the Ministry of Defense) and contractors.

• Korean Journal of Construction Engineering and Management
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• v.6 no.5 s.27
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• pp.69-77
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• 2005

Change is inevitable and is a reality of construction projects. Most construction contracts include change clauses and allowing contractors an equitable adjustment to the contract price and duration caused by change. However, the actions of a contractor can cause a loss of productivity and furthermore can result in disruption of the whole project because of a cumulative or ripple effect. Because of its complicated nature, it becomes a complex issue to determine the cumulative impact (ripple effect) caused by single or multiple change orders. Furthermore, owners and contractors do not always agree on the adjusted contract price for the cumulative Impact of the changes. A number of studies have attempted to quantify the impact of change orders on project costs and schedule. Many of these attempted to develop regression models to quantify the loss. However, regression analysis has shortcomings in dealing with many qualitative or noisy input data. This study develops ANN models to classify and quantify the labor productivity losses that are caused by the cumulative impact of change orders. The results skew that ANN models give significantly improved performance compared to traditional statistical models.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.179-187
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• 2022

The project performances can be measured in terms of meeting the project schedule, budget, and conformance to functional and technical specifications. Numerous studies have been conducted to examine the causes and effects of change orders for both vertical and horizontal construction, respectively. However, these studies mainly focus on a single project type, so this paper examines the impact of change order for cost growth and schedule overruns using four different building types to close the gap in the change order research area. A total of 211 building projects are collected from four building types: healthcare, residential, office, and education. Statistical analyses using ANOVA tests and linear regression models are used to examine the created metric $CO/day on the cost and schedule impacts. The results found that mean $CO/day values were not statistically different among building types, and that the sum of change orders is a statistically significant predictor of $CO/day. The results will help project stakeholders mitigate the negative change orders effects can be a challenge for project managers and researchers alike.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.50-56
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• 2022

Change Orders generally impact cost and schedule performance of highway projects. However, highway projects that do not have any change orders also face cost growth and schedule delays. This study seeks to determine the cost and schedule performance of Texas DOT projects by collecting project data for 120 highway projects completed between 2016 to 2020. For the study, we selected project data that has zero or negative change orders which were then grouped and analyzed based on their Project Types i.e., maintenance works; structural works; restoration and rehabilitation works; and safety works. The study found that performance of Maintenance and Safety type projects had less cost and schedule growth among the data analyzed. Statistical tests also found that even though the projects have no change orders, Rehabilitation and Restoration type projects experienced significant schedule growth compared to others. However, the data did not show any significant cost and schedule growth for the projects when statistical tests were performed on overall data. The study concluded that highway projects are experiencing schedule growth even though the projects had no change orders. Results from the study can help planners, engineers, and administrators to gain better insight on how different types of highway projects are performing in terms of cost and schedule and eventually derive appropriate solutions to minimize cost and schedule growth in such projects.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.622-624
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• 2015

As uncertain factors are latent in a construction project by nature, a change order occurs frequently. The occurrence of change orders in construction projects conducted during construction phase is known to cause unexpected negative impacts such as cost overrun, schedule delay, quality problem, and claims in the post-process. Thus, an efficient management method is necessary to prevent and minimize change orders during construction phase when they occur frequently. This paper analyzed the causes of change orders and the impact factors that occur during the construction phase of a construction project and suggested a direction for change order database building.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.616-621
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• 2009

Change orders represent one of the largest sources of cost growth on construction projects, but an efficient change management control system can help the projects steer clear of the constant construction changes. This study was performed to achieve a better understanding of all changes and to develop a new set of Best Practices using process mapping techniques. The project data for this research were collected from case studies of aviation projects implemented in Houston, Texas at Bush Intercontinental Airport. The findings and contributions of this research should help owners and project managers determine and identify major causes that impact project budget and schedule and implement solutions prior to them surfacing.

• Journal of the Korea Institute of Building Construction
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• v.21 no.3
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• pp.213-219
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• 2021

Change orders that occur frequently during the construction phase, af ect the construction performance in terms of cost, time, quality, safety and environment, and place a huge burden for stakeholders of given projects. This study analyzed the causes of change orders and their impact on the basis of 721 cases and a questionnaire of 164 domestic U.S. military construction participants in a total of 24 U.S. military projects. Important factors for change orders in the US military construction projects were engineering change due to design errors (348 cases, 48.3%), user requests change(86 cases, 11.9%), and different site conditions (69 cases, 9.6%). In addition, due to the change orders, construction cost increased by 6.56% on average and construction period was extended by 21.1% compared to the original schedule. As a result, it is anticipated that domestic construction companies can obtain a better understanding of change orders and construction performance, which may be difficult due to accessibility and limitations to military facilities. Also, it is proposed a successor study that guides in the right direction for the U.S. Military Construction.

• Korean Journal of Construction Engineering and Management
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• v.7 no.2 s.30
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• pp.171-180
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• 2006

Change is inevitable and is a reality of construction projects. Most construction contracts include change clauses and allowing contractors an equitable adjustment to the contract price and duration caused by change. However, the actions of a contractor can cause a loss of productivity and furthermore can result in disruption of the whole project because of a cumulative or ripple effect. Because of its complicated nature, it becomes a complex issue to determine the cumulative impact (ripple effect) caused by single or multiple change orders. Furthermore, owners and contractors do not always agree on the adjusted contract price for the cumulative impact of the changes. A number of studies have attempted to quantify the impact of change orders on project costs and schedule. Many of these attempted to develop regression models to quantify the loss. However, regression analysis has shortcomings in dealing with many qualitative or noisy input data. This study develops ANN models to classify and quantify the labor productivity losses that are caused by the cumulative impact of change orders. The results show that ANN models give significantly improved performance compared to traditional statistical models.