Fuzzy inference system application for failure analyzing in automobile industry

Method for the estimation of institutional quality indexes using fuzzy logic

This paper presents a method to estimate institutional environment indexes using fuzzy modeling. Because of the complexity of the subject, institution, elements associated with this thinking are difficult to measure and compare. In order to address this problem, this research presents how a fuzzy inference system works and how to create institutional indexes from it. While methods that analyze institutional environments generally use secondary data from countries or regions provided by international organizations, the illustrative case applied to aquaculture in Brazil demonstrates the effectiveness of using this method to generate indexes related to the subject from primary data collected at the firm level. Furthermore, the combined use of this method with others already used in the institutional literature can be valuable both for researchers and public policy makers who seek to increasingly understand the role of institutions in economic performance.•

Uses a Mamdani expert system of MIMO type to estimate institutional indexes.

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Institutional ambient scores related to tilapia production in Brazil are presented.

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The combined use of the method with others can be valuable for the research field.

Interval-valued neutrosophic failure mode and effect analysis

Failure mode and effects analysis (FMEA) is a structured approach for discovering possible failures that may occur in the design of a product or process. Since classical FMEA is not sufficient to represent the vagueness and impreciseness in human decisions and evaluations, many extensions of ordinary fuzzy sets such as hesitant fuzzy sets, intuitionistic fuzzy sets, Pythagorean fuzzy sets, spherical fuzzy sets, and picture fuzzy sets. Classical FMEA has been handled to capture the uncertainty through these extensions. Neutrosophic sets is a different extension from the others handling the uncertainty parameters independently. A novel interval-valued neutrosophic FMEA method is developed in this study. The proposed method is presented in several steps with its application to an automotive company in order to prioritize the potential causes of failures during the design process by considering multi-experts’ evaluations.

Evaluation of risks in foodgrains supply chain using failure mode effect analysis and fuzzy VIKOR

Purpose

This paper investigates the risks involved in the Indian foodgrain supply chain (FSC) and proposes risk mitigation taxonomy to enable decision making.

Design/methodology/approach

This paper used failure mode and effect analysis (FMEA) for risk estimation. In the traditional FMEA, risk priority number (RPN) is evaluated by multiplying the probability of occurrence, severity and detection. Because of some drawbacks of the traditional FMEA, instead of calculating RPN, this paper prioritizes the FSC risk factors using fuzzy VIKOR. VIKOR is a multiple attribute decision-making technique which aims to rank FSC risk factors with respect to criteria.

Findings

The findings indicate that “technological risk” has a higher impact on the FSC, followed by natural disaster, communication failure, non-availability of procurement centers, malfunctioning in PDS and inadequate storage facility. Sensitivity analysis is performed to check the robustness of the results.

Practical implications

The outcomes of the study can help in deriving detailed risk mitigation strategy and risk mitigation taxonomy for the improved resilience of FSC.

Originality/value

Specifically, this research investigates the risks for foodgrains supply chain system for a developing country such as India, an area which has received limited attention in the present literature.

An integrated HFMEA-DES model for performance improvement of general hospitals

Purpose

Healthcare failure mode and effect analysis (HFMEA) identifies potential risks and defines preventive actions to reduce the effects of risks. In addition, a discrete event simulation (DES) could evaluate the effects of every improvement scenario. Consequently, a proposed integrated HFMEA-DES model is presented for quality improvement in a general hospital.

Design/methodology/approach

In the proposed model, HFMEA is implemented first. As any risk in the hospital is important and that there are many departments and different related risks, all defined risk factors are evaluated using the risk priority number (RPN) for which related corrective actions are defined based on experts' knowledge. Then, a DES model is designed to determine the effects of selected actions before implementation.

Findings

Results show that the proposed model not only supports different steps of HFMEA but also is highly in accordance with the determination of real priorities of the risk factors. It predicts the effects of corrective actions before implementation and helps hospital managers to improve performances.

Practical implications

This research is based on a case study in a well-known general hospital in Iran.

Originality/value

This study takes the advantages of an integrated HFMEA-DES model in supporting the limitation of HFMEA in a general hospital with a large number of beds and patients. The case study proves the effectiveness of the proposed approach for improving the performances of the hospital resources.

A practical review and taxonomy of fuzzy expert systems: methods and applications

Purpose

Expert systems are computer-based systems that mimic the logical processes of human experts or organizations to give advice in a specific domain of knowledge. Fuzzy expert systems use fuzzy logic to handle uncertainties generated by imprecise, incomplete and/or vague information. The purpose of this paper is to present a comprehensive review of the methods and applications in fuzzy expert systems.

Design/methodology/approach

The authors have carefully reviewed 281 journal publications and 149 conference proceedings published over the past 37 years since 1982. The authors grouped the journal publications and conference proceedings separately accordingly to the methods, application domains, tools and inference systems.

Findings

The authors have synthesized the findings and proposed useful suggestions for future research directions. The authors show that the most common use of fuzzy expert systems is in the medical field.

Originality/value

Fuzzy logic can be used to manage uncertainty in expert systems and solve problems that cannot be solved effectively with conventional methods. In this study, the authors present a comprehensive review of the methods and applications in fuzzy expert systems which could be useful for practicing managers developing expert systems under uncertainty.