Abstract
Researches have shown that the human errors have been generally recognized as the major cause of industrial accidents. This problem is worsening by the increasing mental workload of human in modern manufacturing environment and the insufficient company resources spending due to the lack of understanding the loss of human errors. To prevent the accidents from happening, it is necessary for company to access the potential human errors and implement the appropriate control measurements. Traditional human reliability assessment techniques and accounting system cannot directly provide loss information for assessing the impacts of human errors. This obstacles force industrial managers to justify the proper accident and injury prevention process through their experiences. The efficiency and effectiveness of the system safety barriers are in doubt and the smooth operation of manufacturing activities are insecure. In this study, a human error prediction and cost estimation model is introduced to facilitate line managers with a proper tool to collect and calculate the total losses of its impact. The analyzer takes the operation procedure as input data and derives the latent human errors tables through the application of Human Error Identification System Tools (HEIST). The error mode tables are used to develop the human error diagnosis checklists. The checklists are used by the managers to observe workers’ daily operations. The critical costs of latent human errors are derived from the results of literature review with the help of line managers and supervisors through the Analytic Hierarchy Process (AHP). Experts’ judgments and pair wise comparison technique are incorporated to interrogate managers’ knowledge of latent human errors and correspondent costs. This approach can overcome the problem of insufficient cost information caused by current accounting system and compensate the influence of safety and health department due to the low organizational status in quo. The loss estimation model not only provides quantitative data, which can be integrated with the current hazard analysis and risk assessment process, but also identified the root causes of losses and respective correction measurements.