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In serial–parallel multistage manufacturing processes (SP-MMPs), identifying root cause machine sets (MSs) that contribute to defective product production is crucial for improving product quality. To facilitate the efficient identification of root cause MSs, suspicious MSs are typically screened using association rule-based approaches. A redundant MS contains a root cause MS but does not provide any additional information beyond what the root cause MS already offers. Conventional suspicious MS screening measures calculate suspiciousness based on machine assignment history. However, these measures often overrate redundant MSs because the association of the root cause MS with defective product production is also considered when calculating the suspiciousness of the redundant MS. This overrating can result in redundant MSs being prioritized during screening, while actual root cause MSs are overlooked. This study argues that a suspiciousness measure should assess the association between an MS and defective product production, excluding associations that can be explained by any proper subset MSs of the MS. To address this, a novel suspiciousness measure based on multi-item association is proposed. Case studies conducted on simulated SP-MMPs demonstrate that the proposed measure effectively reduces the overrating of redundant MSs and improves the prioritization of root cause MSs during the screening process. These findings demonstrate the effectiveness of the proposed measure in suspicious MS screening.