5S practice follows structured 5S activities from structurize, systematize, sanitize, standardize, and self-discipline to deal with scene management in shop floor control, and it is regarded as the most troublesome aspect with respect to environmental safety and health for a semiconductor manufacturing fabrication. The improved action items for 5S activities can amount to thousands from messy paper filing to untightened chemical piping. However, there is no clear key performance indicator to evaluate how good (safe) the fab is and how to be good (safe) for 5S practice. Failure modes and effects analysis is an effective and efficient way to deal with risk assessment for 5S activities and to prioritize the action requests from the improved result of continuous improvement. However, when failure modes and effects analysis is applied to the risk assessment of 5S audit, the conventional risk priority number lacks of all comprehensive information and misleads to a bias for not considering weights of severity (S), occurrence (O), and detectability (D). In order to improve the method of risk priority number evaluation, this article combining 2-tuple fuzzy linguistic representation model and weighted geometric averaging operators to quantify 5S audit findings is proposed to eliminate the bias from different 5S auditors. This is the first approach for the numerous 5S action items to be quantified and prioritized with resource constraints to sustain 5S practice robust. A case study in a fab was demonstrated to show how the model was implemented to approve its validity.