Purpose Nowadays, quality is one of the most important key success factors in the automobile industry. Improving the quality is based on optimizing the most important quality characteristics and usually launched by highly applied techniques such as failure mode and effect analysis (FMEA). According to the literature, however, traditional FMEA suffers from some limitations. Reviewing the literature, on one hand, shows that the fuzzy rule-base system, under the artificial intelligence category, is the most frequently applied method for solving the FMEA problems. On the other hand, the automobile industry, which highly takes advantages of traditional FMEA, has been deprived of benefits of fuzzy rule-based FMEA (fuzzy FMEA). Thus, the purpose of this paper is to apply fuzzy FMEA for quality improvement in the automobile industry. Design/methodology/approach Firstly, traditional FMEA has been implemented. Then by consulting with a six-member quality assurance team, fuzzy membership functions have been obtained for risk factors, i.e., occurrence (O), severity (S), and detection (D). The experts have also been consulted about constructing the fuzzy rule base. These evaluations have been performed to prioritize the most critical failure modes occurring during production of doors of a compact car, manufactured by a part-producing company in Iran. Findings Findings indicate that fuzzy FMEA not only solves problems of traditional FMEA, but also is highly in accordance with it, in terms of some priorities. According to results of fuzzy FMEA, failure modes E, pertaining to the sash of the rear right door, and H, related to the sash of the front the left door, have been ranked as the most and the least critical situations, respectively. The prioritized failures could be considered to facilitate future quality optimization. Practical implications This research provides quality engineers of the studied company with the chance of ranking their failure modes based on a fuzzy expert system. Originality/value This study utilizes the fuzzy logic approach to solve some major limitations of FMEA, an extensively applied method in the automobile industry.
Today, combination of medicine and tourism has turned into a new form of industry called health tourism. Health tourism industry has experienced a dramatic growth over the last decade. This industry is an opportunity for hospitals to extend their services to patients from other countries. The present study aims to identify factors contributing to the attraction of medical tourism along with ranking factors affecting customer satisfaction of Persian Gulf Countries .For this purpose, after studying the existing literature and matching the resulting data with the environmental conditions of the study six factors "the expertise and skill level of the hospital staff", "hospital facilities and equipments", "costs", "the way patients are treated by the staff and their relationship", "shared beliefs and values", "tourist and travel facilities" were determined and based on them questionnaires were designed and distributed to a number of patients of Persian Gulf Countries as well as the managers of the Razavi Hospital in Mashhad, where specialized medical tourism services are provided .Investigations indicated that from the viewpoint of both managers and patients, the most important factor affecting the attraction of the medical tourism of Razavi Hospital in Mashhad is the costs. These costs consist of medical expenses (such as hospital, medicine and tests costs), costs of travel and accommodation (such as hotel and guesthouse), transportation costs within the city and side expenditures (including visiting pilgrimage places and entertainment spots). Therefore, lowering the costs of medical treatments can be a strategy that the hospitals can benefit from in order to enter the medical tourism market.
One of the most important steps in formulating and solving a multiattribute decision‐making (MADM) problem is weighting the attributes. Most existing weighting methods are based on judgments by experts/decision‐makers, which are prone to several cognitive biases, making it necessary to examine these biases in MADM weighting methods and develop debiasing strategies. This study uses experimental analysis to look at equalizing bias—one of the main cognitive biases, where decision‐makers tend to assign the same weight to different attributes—in MADM methods. More specifically, we look at AHP (analytic hierarchy process), BWM (best‐worst method), PA (point allocation), SMART (simple multiattribute rating technique), and Swing methods under two structuring formats, hierarchical and non‐hierarchical. To empirically examine the existence of equalizing bias in these methods, we formulate several hypotheses, which are tested using a public transportation mode selection problem among 146 university students. The results indicate that AHP and BWM have less equalizing bias than SMART, Swing, and PA, and that the hierarchical problem structuring leads to a reduction in the equalizing bias in all five methods and that such a reduction significantly varies among the methods. Our findings prove some debiasing strategies suggested in existing literature, which could be used by real decision‐makers (when selecting a method) as well as researchers (when developing new methods).
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