Developing a new BWM-based GMAFMA approach for evaluation of potential risks and failure modes in production processes

Purpose Nowadays, the risk assessment and reliability engineering of various production processes have become an inevitable necessity. Because if these risks are not going to be evaluated and no solution is going to be taken for their prevention, managing them would be really hard and costly in case of their occurrence. The importance of this issue is much higher in producing healthcare products due to their quality's direct impact on the health of individuals and society. Design/methodology/approach One of the most common approaches of risk assessment is the failure mode and effects analysis (FMEA), which is facing some limitations in practice. In this research, a new generalized multi-attribute failure mode analysis approach has been proposed by utilizing the best-worst method and linguistic 2-tuple representation in order to evaluate the production process of hemodialysis solution in a case of Tehran, Iran. Findings According to the results, entry of waste to the mixing tanker, impurity of raw materials and ingredients and fracture of the mixer screw have been identified as the most important potential failures. At last, the results of this research have been compared with the previous studies. Originality/value Some reinforcement attributes have been added to the traditional FMEA attributes in order to improve the results. Also, the problems of identical weights for attributes, inaccuracy in experts' opinions and the uncertainties in prioritizing the potential failures were improved. Furthermore, in addition to the need for less comparative data, the proposed approach is more accurate and comprehensive in its results.

Automated summary

The research proposes a new generalized multi-attribute failure mode analysis approach to evaluate the production process of hemodialysis solution in Tehran, Iran. The most important potential failures identified are entry of waste to the mixing tanker, impurity of raw materials and ingredients, and fracture of the mixer screw. The proposed approach improves upon traditional FMEA attributes and provides more accurate and comprehensive results.