Hospital hazards and human errors pose a significant and complex problem, with rising incidents and irreversible consequences. Managing laboratory errors and risks is vital due to the presence of chemicals, electrical equipment, and the involvement of students, professors, and staff. The high value of laboratory equipment further underscores the need for robust risk management strategies. To address these challenges, researchers have explored the Failure Mode and Effects Analysis (FMEA) method for risk identification and assessment in healthcare settings. However, recognizing its limitations, this study aims to prioritize and evaluate laboratory errors using an integrated approach that combines the Best-Worst Method (BWM) and Complex Proportional Assessment with a Fuzzy Spherical Environment (CoCoSo-FSE). By applying the BWM, criteria such as severity, detectability, and occurrence probability are weighted to account for the nature of laboratory errors. The CoCoSo-FSE is then employed to evaluate and prioritize 18 identified laboratory errors, reducing uncertainty and enhancing decision-making. The fuzzy spherical set is used to address uncertainties by providing a flexible framework for decision-makers to define membership functions in specific spherical regions, enhancing the representation of knowledge and decision-making information. The proposed approach is compared with other decision-making methods, namely MOORA and COPRAS, demonstrating reliable ranking results. Sensitivity analysis confirms the stability of the approach's ranking when adjusting the flexibility parameter. This integrated approach offers a reliable and robust decision-making technique for managing laboratory errors, providing valuable insights to enhance laboratory safety and risk management for stakeholders, managers, and policymakers.

Sending men to space has never been an ordinary activity, it requires years of planning and preparation in order to have a chance of success. The payoffs of reliable and repeatable space flight are many, including both Commercial and Military opportunities. In order for reliable and repeatable space flight to become a reality, catastrophic failures need to be detected and mitigated before they occur. It can be shown that small pieces of a design which seem ordinary can create devastating impacts if not designed and tested properly. This paper will address the use of a Failure Mode, Effects, and Criticality Analysis (FMECA) with modified Risk Priority Number (RPN) and its application to safety critical design components of shuttle liftoff. An example will be presented here which specifically focuses on the Solid Rocket Boosters (SRBs) to illustrate the FMECA approach to reliable space travel.

This paper presents a method to determine factors influencing alternator failure causes. Failure Mode and Effects Analysis (FMEA) is one of the first systematic techniques for failure analysis based on three factors including Probability (P), Severity (S) and Detection (D). Traditional FMEA method considers equal weights for all three factors, however, in read-world cases; one may wish to consider various weights. The proposed study develops a mathematical model to determine optimal weights based on analytical hierarchy process technique. The implementation of the proposed study has been demonstrated for a read-world case study of alternator failure causes.

Root cause failure analysis (RCFA) is a structured process to acknowledge cause and effect relationship of failure or unfavorable events in the organization in order to prevent repetition or reduction of failures. There are various tools for RCFA like interviewing, fault tree analysis (FTA), 5 whys, failure mode and effects analysis (FMEA), Pareto analysis and storytelling method with different strengths and weaknesses. In this paper, an integrated process model is developed using Reality Charting, FMEA and DEMATEL to understand and implement RCFA effectively. The proposed process model has eight main steps. The presented model in a case study for the UTD-20 engine is implemented and thus, in addition to the use of supporting research in model development, real data as well as the approval of the UTD-20 analysis team members are assisted to validate the proposed model.

This paper presents a hybrid method for detecting the most important failure items as well as the most effective alternative strategy to cope with possible events. The proposed model of this paper uses grey technique to rank various alternatives and FMEA technique to find important faults. The implementation of the proposed method has been illustrated for an existing example on the literature. The results of this method show that the proposed model has been capable of detecting the most trouble making problems with fuzzy logic and finds the most important solution strategy using FMEA technique.