Evaluation of best system performance: Human, automated, and hybrid inspection systems

Abstract Recently, 100% inspection with automated systems has seen more frequent application than traditional sampling inspection with human inspectors. Nevertheless, humans still outperform machines in most attribute inspection tasks. Because neither humans nor automation can achieve superior inspection system performance, hybrid inspection systems where humans work cooperatively with machines merit study. In response to this situation, this research was conducted to evaluate three of the following different inspection systems: (1) a human inspection system, (2) a computer search/human decision‐making inspection system, and (3) a human/computer share search/decision‐making inspection system. Results from this study showed that the human/computer share search/decision‐making system achieve the best system performance, suggesting that both should be used in the inspection tasks rather than either alone. Furthermore, this study looked at the interaction between human inspectors and computers, specifically the effect of system response bias on inspection quality performance. These results revealed that the risky system was the best in terms of accuracy measures. Although this study demonstrated how recent advances in computer technology have modified previously prescribed notions about function allocation alternatives in a hybrid inspection environment, the adaptability of humans was again demonstrated, indicating that they will continue to play a vital role in future hybrid systems. © 2003 Wiley Periodicals, Inc. Hum Factors Man 13: 137–152, 2003.