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Author: Paul Joseph Vandenberg Publisher: ISBN: Category : Construction industry Languages : en Pages : 236
Book Description
Change orders impact many areas of construction projects. However, the impacts that change orders have on labor efficiency are much harder to quantify and are, therefore, a significant risk to contractors. Little research has been completed in the past quantifying these impacts so that disputes are common between owners and contractors regarding the actual cost of change. This study uses data from 43 projects, 27 impacted by changes and 16 not impacted by changes, to develop a linear regression model that predicts the impact on labor efficiency. The input factors needed for the model are: (1) Total Actual Project Hours, (2) Total Estimated Change Hours, (3) Impact Classification, and (4) Timing of Change. Timing of Change is calculated by breaking the project schedule down into six periods (i.e., changes before construction start, 0 - 20%, 20 - 40%, 40 - 60%A, 60 - 80%, and 80 - 100%), listing the percentage of change that occurred in each period, and calculating a weighted timing factor. The model calculates the labor loss or gain in efficiency for a particular project so that owners and contractors will better understand the true change impact on labor efficiency. Significant results have been found in hypothesis testing. The results show that impacted projects have larger amounts of change, have a larger decrease in labor efficiency, and are more impacted by change that occurs later in the project schedule. These results appear to be consistent with the intuitive judgement of industry professionals. The research is limited to the mechanical trade, but does include specific work in plumbing, HVAC, process piping, and fire protection.
Author: Paul Joseph Vandenberg Publisher: ISBN: Category : Construction industry Languages : en Pages : 236
Book Description
Change orders impact many areas of construction projects. However, the impacts that change orders have on labor efficiency are much harder to quantify and are, therefore, a significant risk to contractors. Little research has been completed in the past quantifying these impacts so that disputes are common between owners and contractors regarding the actual cost of change. This study uses data from 43 projects, 27 impacted by changes and 16 not impacted by changes, to develop a linear regression model that predicts the impact on labor efficiency. The input factors needed for the model are: (1) Total Actual Project Hours, (2) Total Estimated Change Hours, (3) Impact Classification, and (4) Timing of Change. Timing of Change is calculated by breaking the project schedule down into six periods (i.e., changes before construction start, 0 - 20%, 20 - 40%, 40 - 60%A, 60 - 80%, and 80 - 100%), listing the percentage of change that occurred in each period, and calculating a weighted timing factor. The model calculates the labor loss or gain in efficiency for a particular project so that owners and contractors will better understand the true change impact on labor efficiency. Significant results have been found in hypothesis testing. The results show that impacted projects have larger amounts of change, have a larger decrease in labor efficiency, and are more impacted by change that occurs later in the project schedule. These results appear to be consistent with the intuitive judgement of industry professionals. The research is limited to the mechanical trade, but does include specific work in plumbing, HVAC, process piping, and fire protection.
Author: Shrouk Gharib Publisher: ISBN: Category : Labor productivity Languages : en Pages : 0
Book Description
Abstract: Change orders in construction projects lead to numerous negative impacts, including loss of labor productivity, delays, and cost overruns. Owners and contractors are usually in disagreement when it comes to allocating the extent of responsibilities with respect to the resulting overruns. Each party tries to hold the other party fully responsible for such overruns through a series of claims and disputes. Several delay analysis techniques have been developed to aid in settling such disputes, however, they do not fully grasp the rippled impacts of change orders and do not assist parties in reaching consensus when it comes to finding the isolated rippled impacts of each change order. This research aims to develop a framework that supports delay analysis based on dynamic modeling with a focus on the impacts of change orders. System dynamics is utilized as the base modeling methodology due to its capability of capturing rippled impacts and complex interrelations. A novel calibration methodology is also developed to enable using this framework in any construction project. After development and verification, the framework was tested on a sample construction project that faced delays due to change orders. The developed model was able to quantitatively link the productivity losses and delays to each change order, which helped in clearly allocating the responsible parties for the delays. In addition, several what-if-scenarios were conducted to enhance the understanding of how such impacts could have been avoided. This research is envisaged to support owners and contractors in quickly reaching consensus regarding the impacts of change orders; thus, minimizing the corresponding disputes and fostering a healthier contracting environment.
Author: Karim Ashraf Sabry Iskander Publisher: ISBN: Category : Languages : en Pages : 149
Book Description
Change orders occur in almost every construction project and regularly cause variations to the contractors' anticipated working conditions, resources, and manner of work completion. Change orders are major source of additional congestion, change in sequence, and loss of momentum in the construction jobsite. They frequently cause unforeseen labor productivity loss, which forces contractors to extend their stays on projects. Contractors encounter a lot of resistance from owners when proving productivity loss attributable to change orders, which may lead to unresolved disputes and lengthy litigations. Previous researchers attempted to set standards and methods in order to quantify the cumulative impact of changes on labor productivity. Some of the previous studies were based on case studies of two or three projects, others included a larger number of projects and more reliable analysis. Generally, it is very difficult to conclusively determine the exact amount of productivity loss attributable to change orders. As a result, there is a continuous need to enhance and enrich the cumulative impact research field. This current research is based on a database of one hundred and forty-five mechanical and electrical projects, encompassing two project groups: projects impacted by changes, and projects unimpacted by changes. Using two-sample t-tests and Chi-squared tests, a series of numerical and categorical variables were found to be significant in distinguishing between impacted and unimpacted projects, thus revealing the underlying causes of productivity loss associated with change orders. Furthermore, sixty-eight impacted projects were used in order to quantify the cumulative impact of changes using linear regression analysis. A series of statistical model selection criteria were applied in order to carefully identify the best predictive models. Candidate models were statistically diagnosed and thoroughly tested to check their validity. Statistical tests and measures were used in order to check whether there are outlying or influential observations in the models. In addition to that, new projects were collected to verify the future predictive ability of the candidate models. The analysis identified the following six factors as best cumulative impact predictors: percent owner initiated change orders, overmanning, turnover, absenteeism, percent time spent by project manager on project, and productivity tracking. The models developed in this research provide the construction industry with means that could be used during dispute resolutions to support the contractors' calculations and assertions for cumulative impact claims. Finally, this study incorporates a significant statistical component that highlights the most common challenges that analysts face when building linear regression models, such as multicollinearity and the presence of hidden extrapolations. The models developed in this research were extensively analyzed in full details through various statistical tests and measures in order to avoid misleading and deceptive results.
Author: Paul Joseph Vandenberg
Author: Paul Joseph Vandenberg
Author: Timothy W. Gotzion
Author: David John Thomack
Author: Joel Phillip Dettwiler
Author: Pehr Anthony Peterson
Author: Matthew J. Bruggink
Author: Shrouk Gharib
Author: Kenneth Timothy Sullivan
Author: Kenneth Timothy Sullivan
Author: Karim Ashraf Sabry Iskander