Providing an early warning framework to identify, assess and control the human performance influencing factor

Accident root causes analysis shows optimization of factors which affecting performance has an essential role in reducing of accidents. These factors are dynamic and complex and they may also be dependent. Therefore, a comprehensive analysis of the working environment is essential. The main objective of this study is to propose a framework to control of human performance influencing factors in a automotive industry in IRAN. The present study provided an early warning model that predicts the risk factors affecting human performance. Since behavioral factors that are causing errors are complex in structure, FANP method was used for modeling. Using the proposed model, the potential risk of workplace determined before it leads to accidents and based on the type and level of risk and risk control measures was determined. The model was tested on two major projects in the car manufacturing industry. The results show that the risk indexes in the first and second project are 0.391 and 0.197 respectively. Since the value of the index in the first project is greater than the amount authorized by the model so corrective action suggested in accordance with identified risk factors without stopping the system. The system can by predict and assess the performance influencing factors, act as an early warning system. As a result, this system will lead to improved performance and enhance safety.