RESUMEN
BACKGROUND: Occupational injuries are currently a major contributor to job loss around the world and are also costly for businesses. The absence of rational analysis is felt in this area, so mathematical analysis is needed to obtain the logical results of these injuries in order to find gaps or loss points of the industry. OBJECTIVE: This paper assesses the effects of five demographic factors on ergonomic risk and occupational injuries using an integrated mathematical programming approach. The obtained results will help managers to carry out any required corrective actions or establish benchmarks. METHODS: Two typical ergonomic risk assessment methods, the Quick Exposure Check (QEC) and Rapid Entire Body Assessment (REBA), are applied to assess musculoskeletal disorders in workers. Then, considering the five demographic factors as input variables and risks computed by the QEC and REBA methods as outputs, final risk rates for each decision-making unit (DMU) are calculated using data envelopment analysis (DEA). The importance and weight of each risk factor is analyzed using statistical techniques and analysis of variance (ANOVA). To demonstrate the applicability of the methodology, it is applied to a large auto parts manufacturer. RESULTS: The results revealed that the information obtained by DEA is consistent with those for REBA and QEC, which shows that education, experience and weight are factors that could help reduce ergonomic risks. CONCLUSION: It is shown that demographic variables such as age, height, weight, education and work experience play an important and effective role in the explanation of ergonomic risk factors.
Asunto(s)
Ergonomía , Industria Manufacturera , Enfermedades Musculoesqueléticas , Enfermedades Profesionales , Automóviles , Humanos , Enfermedades Musculoesqueléticas/epidemiología , Enfermedades Musculoesqueléticas/etiología , Medición de Riesgo , Factores de RiesgoRESUMEN
BACKGROUND: Resilience engineering (RE) is a new paradigm that can control incidents and reduce their consequences. Integrated RE includes four new factors—self-organization, teamwork, redundancy, and fault-tolerance—in addition to conventional RE factors. This study aimed to evaluate the impacts of these four factors on RE and determine the most efficient factor in an uncertain environment. METHODS: The required data were collected through a questionnaire in a petrochemical plant in June 2013. The questionnaire was completed by 115 respondents including 37 managers and 78 operators. Fuzzy data envelopment analysis was used in different α-cuts in order to calculate the impact of each factor. Analysis of variance was employed to compare the efficiency score means of the four above-mentioned factors. RESULTS: The results showed that as α approached 0 and the system became fuzzier (α = 0.3 and α = 0.1), teamwork played a significant role and had the highest impact on the resilient system. In contrast, as α approached 1 and the fuzzy system went toward a certain mode (α = 0.9 and α = 1), redundancy had a vital role in the selected resilient system. Therefore, redundancy and teamwork were the most efficient factors. CONCLUSION: The approach developed in this study could be used for identifying the most important factors in such environments. The results of this study may help managers to have better understanding of weak and strong points in such industries.
Asunto(s)
Análisis de Varianza , Plantas , Encuestas y CuestionariosRESUMEN
BACKGROUND: A unique framework for performance optimization of generation companies (GENCOs) based on health, safety, environment, and ergonomics (HSEE) indicators is presented. METHODS: To rank this sector of industry, the combination of data envelopment analysis (DEA), principal component analysis (PCA), and Taguchi are used for all branches of GENCOs. These methods are applied in an integrated manner to measure the performance of GENCO. The preferred model between DEA, PCA, and Taguchi is selected based on sensitivity analysis and maximum correlation between rankings. To achieve the stated objectives, noise is introduced into input data. RESULTS: The results show that Taguchi outperforms other methods. Moreover, a comprehensive experiment is carried out to identify the most influential factor for ranking GENCOs. CONCLUSION: The approach developed in this study could be used for continuous assessment and improvement of GENCO's performance in supplying energy with respect to HSEE factors. The results of such studies would help managers to have better understanding of weak and strong points in terms of HSEE factors.