Developing a New Decision Support System to Manage Human Reliability based on HEART Method

Human performance and reliability monitoring have become the main issue for many industries since human error ratios cannot be mitigated to the zero level and many accidents, malfunctions, and quality defects are happening due to the human in production systems. Since the human resources implement a different range of tasks, the calculation of human error probability (HEP) is complicated, and several methods have been proposed to identify and quantify the HEP. This fact expresses the necessity of a Decision Support System (DSS) to calculate the HEP and propose optimal scenarios to increase human reliability and decrease its related cost such as quality defect and rework cost. This study develops a DSS that calculates the HEP based work specifications and proposes optimal scenarios to deal with error occurrence probability. The scenarios are provided using an AHP according to experts opinions about the cost and time of corrective actions. The proposed DSS has been applied to a real case, and the provided results show that the proposed DSS can provide effective scenarios to deal with human error in production systems.

Keywords:

HEP , HEART , Decision support system , Artificial Neural Network , reliability