A fuzzy extended analytic network process-based approach for global supplier selection

Improved Failure Mode and Effect Analysis: Implementing Risk Assessment and Conflict Risk Mitigation with Probabilistic Linguistic Information

Failure mode and effect analysis (FMEA) is a system reliability analysis technique that has been widely used in various industries to reduce the failure risk of products, systems, and services. However, traditional FMEA methods have limitations in managing the complex uncertain environment as well as the aggregation and weight allocation of FMEA attributes. Complex real-world problems usually involve multiple decision-makers. Individual perceptions and subjective factors are likely to lead to the differences in opinion, and even the conflict risk, which can ultimately become a challenge to achieve a highly recognized solution. This paper proposes an improved FMEA method to implement the risk assessment, which integrates probabilistic linguistic information and conflict risk mitigation. Probabilistic linguistic term sets (PLTSs) are used to describe the risk assessments, and a comprehensive method is applied to determine the weights of the FMEA attributes. Several new operations and distance measures related to PLTSs are defined. Then, a conflict risk mitigation model is developed to reduce the differences among decision-makers’ FMEA risk assessments. Finally, a case study on global production base selection is presented to illustrate the feasibility and effectiveness of the proposed method. Comparative analysis and discussion verify features and advantages of the method.

Measuring interdependencies of preferred supplier enablers

Purpose

The purpose of this paper is to analyze preferred supplier enablers (PSEs) and measure the interdependencies among themselves for enhancing preferred supplier relationship.

Design/methodology/approach

In the current study, an approach has been developed in which the significance of various PSEs was determined by applying Fuzzy Analytic Hierarchy Process (FAHP) and the prominent PSEs were screened out through Pareto analysis. Also, the interdependence among the prominent PSEs was measured by applying the Fuzzy Decision Making Trial and Evaluation Laboratory method in order to select the right PSEs in ascribing the Preferred Supplier Status. Finally, the weakest relationships among the PSEs were confirmed by applying the student’s t-test and then an impact relationship map of PSEs was developed.

Findings

The strength of relationships among the PSEs, grouping of PSEs into causes and effects on a causal diagram and a concise impact relationship map of PSEs were determined. Further, the PSEs that a manufacturer must primarily focus and monitor were also obtained.

Research limitations/implications

The study was conducted in an Indian electronic manufacturing environment. Therefore, the results obtained would be more relevant to the high end technology product manufacturers operating in the developing countries.

Practical implications

From the current study, a manufacturer can alleviate, favorably associate and integrate with the good suppliers and then eventually establish a strong supply base.

Originality/value

Manufacturers are looking for the closer and favored relationships by bringing in the concept of preferred supplier while dealing with their key suppliers. Thus, the results obtained from the current study would be of great assistance to a manufacturer in gaining an extra cutting-edge and, in turn, tackling the increased competitive pressures and reduced availability of resources.

Rifai A, Aoyama H. An Integrated MCDM Model for Conveyor Equipment Evaluation and Selection in an FMC Based on a Fuzzy AHP and Fuzzy ARAS in the Presence of Vagueness

The conveyor system plays a vital role in improving the performance of flexible manufacturing cells (FMCs). The conveyor selection problem involves the evaluation of a set of potential alternatives based on qualitative and quantitative criteria. This paper presents an integrated multi-criteria decision making (MCDM) model of a fuzzy AHP (analytic hierarchy process) and fuzzy ARAS (additive ratio assessment) for conveyor evaluation and selection. In this model, linguistic terms represented as triangular fuzzy numbers are used to quantify experts’ uncertain assessments of alternatives with respect to the criteria. The fuzzy set is then integrated into the AHP to determine the weights of the criteria. Finally, a fuzzy ARAS is used to calculate the weights of the alternatives. To demonstrate the effectiveness of the proposed model, a case study is performed of a practical example, and the results obtained demonstrate practical potential for the implementation of FMCs.