بهره گیری از تحلیل پوششی داده ها برای الگوی عملکرد ایمنی قرارداد های ساخت و ساز
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|1317||2010||7 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : International Journal of Project Management, Volume 28, Issue 1, January 2010, Pages 61–67
The purpose of this paper is to utilize data envelopment analysis (DEA) to benchmark safety performance of construction contractors. DEA has been recognized as a robust tool that is used for evaluating the performance of business organizations. The proposed approach is deployed based on empirical data collected from 45 construction contractors. On a scale of 0–1.0, DEA analysis assesses the relative efficiency of every contractor relative to the rest of the contractors in terms of safety performance. For inefficient contractors, DEA analysis provides quantitative guidance on how to become efficient.
The purpose of this paper is to utilize data envelopment analysis (DEA) to benchmark safety performance of construction contractors. DEA has been recognized as a robust tool that is used for evaluating the performance of organizations such as business firms, hospitals, government agencies, educational institutions, etc. DEA is well-deployed in other industries. DEA is a nonparametric linear programming approach that produces a single measure of efficiency for each unit relative to its peers. It enables firms to assess their relative efficiency compared to other firms in the industry. Construction literature includes several methods for assessing safety performance of construction contractors. Two of the most commonly used ones are OSHA recordable incidence rates and experience modification rating (EMR)  and . OSHA recordable incidence rates are based on the US Occupational Safety and Health Act (1970), which requires employers to record and report accident information. Incidents are recorded and a formula is used to compute the incidence rates. EMR, on the other hand, is established by independent rating bureaus. It dictates the contractor’s premium of the workers’ compensation insurance. EMR formula is criticized for its complexity and because of the existence of different versions in practice . It is also argued that EMR is sensitive to company size ,  and . Ng et al.  develop a safety performance evaluation (SPE) framework for evaluating contractor’s safety performance. The model includes a range of organization-related and project-related SPE factors. Based on a survey, the authors assign weights to the different SPE factors to calculate a weighted average safety performance score for each contractor. Generally, it is well-accepted that weighted average scores have an inherent weakness due to the biases introduced in the development of the weights and the additive assumptions utilized in the computations of the weighted score average. Teo and Ling  develop a model to measure the effectiveness of safety management systems (SMS) of construction sites. The authors utilize surveys and experts interviews and workshops to collect the important factors affecting safety. The analytic hierarchy process and factor analysis are used to identify the most crucial factors and attributes affecting safety. Using the model, a construction safety index can be calculated. The authors indicate that the limitations of their model include the small number of experts and respondents involved in the study. The importance weights and attributes are developed within the context of Singapore. Another limitation is that their model includes 590 attributes that must be evaluated on the site. Despite the limitations associated with some of the existing methods, they are useful measures of construction safety performance. However, new methods are still needed as they offer new insights to both researchers and practitioners. A point of departure for the DEA approach compared to existing methods is that DEA relates resources expended on a certain performance to the level of success for that particular performance. Under existing methods, two contractors that suffer the same numbers and types of accidents are considered of identical performance. This is clearly not the case if one contractor is expending more resources (i.e., money, etc.) on safety than the other contractor. It makes more sense to consider the contractor that commits fewer resources to arrive at a certain safety performance as a better performer. The rest of the paper unfolds as follows: data envelopment analysis, data collection, results and analysis, future extension of the research, and conclusions.
نتیجه گیری انگلیسی
This paper contributes a DEA approach for benchmarking safety performance of construction contractors. A point of departure for the DEA approach compared to existing methods is the input–output framework. Compared to each other, DEA measures the efficiency of construction contractors in utilizing their expenses on safety to minimize the number of suffered accidents. Therefore, the DEA approach relates resources expended on safety to safety performance. DEA analysis scores safety performance of construction contractors on a scale of 0–1.0. The analysis identifies contractors E, CC, RR, SS, TT, ZZ, AAA, and CCC as efficient frontier contractors. Compared to the rest of the contractors, these eight contractors are the industry leaders in safety performance. They serve as the “benchmark” for the industry and can be utilized as role models to which inefficient contractors may adjust their practices in order to become efficient. An excellent utilization for the results of this study is programs like the US Malcolm Baldrige National Quality Award  and the UK Department of Trade and Industry Business-to-Business Exchange Program . These programs aim at improving the performance of particular industries. For example, the UK Department of Trade and Industry Business-to-Business Exchange Program offers visits to UK best practicing organizations in manufacturing and service industries. The goal of these visits is to transfer best practices across interested organizations for the purpose of improving their performance. Therefore, the deployment of the DEA methodology makes it possible for programs like the ones described above to utilize the results and target industry leaders in order to publicize their strategies and procedures for the benefit of the whole industry. The Jordanian construction industry currently lacks any readily available safety performance measure to assess safety performance of construction contractors. To judge safety performance, the industry currently relies on the segregated reported numbers of the different types of accidents. As such, the DEA approach is well suited to fill this gap and assesses contractors’ safety performance. The DEA approach presented in this paper can be utilized by a particular contractor to gauge its own safety performance over time. With data available for several numbers of years, every year might be considered as a single DMU. By conducting such analysis a contractor would be able to quantitatively determine whether or not the safety performance of the firm is getting better over time. Additionally, the proposed methodology is deployable at the project level. Every project is regarded as a single DMU and projects as a result are “benchmarked” against each other. Consequently, contractors will be able to identify their best performing projects and to isolate internal factors that contributed to better performance. Even though the development in this paper is based on data collected from the Jordanian construction industry, the methodology would suggest a much broader geographical applicability on evaluating safety performance for construction projects internationally as well as other projects in other disciplines like manufacturing projects. The next step for the research team is to attempt to associate variability in safety performance with certain explanatory variables (i.e., organizational safety policy, safety training, safety equipment, etc.). Clearly, to test such hypotheses, both a larger sample size and a larger scope of data collection are required.