An analysis of human errors in the receiving process of raw material warehouses in the automotive industry using HEART and SHERPA methods

Abstract

The development of industrial manufacturing automation drives the need for efficiency, but the majority of part receiving processes in the automotive warehouse department are still traditional, making them prone to human error. This research aims to identify the causes of human error in receiving, analyze its impact on warehouse accuracy and efficiency, and provide recommendations for improvement. Periodic data from July-December 2024 shows that 43.27% of warehouse errors occurred during receiving. The quantitative method Human Error Assessment and Reduction Technique (HEART) was applied to 6 main activities and 12 subtasks, resulting in the highest error probability (HEP) for the subtask of checking the physical condition of the part (HEP = 12.085). The qualitative approach Systematic Human Error Reduction and Prediction Approach (SHERPA) identified the dominance of action errors and checking errors, particularly in part checking, data input, material handling, and document verification. The combination of findings from HEART-SHERPA reveals that human error slows down the receiving process, disrupts actual stock, and delays production flow. Recommendations for improvement include (1) technical training and routine briefings to enhance operator competency, (2) implementing a buddy system and double verification using checklists, and (3) re-layouting the transit area with tag labels and structured archiving.