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Purpose – Supply chains (SCs) have become increasingly vulnerable to catastrophic events/disruptions that may be natural or man-made. Hurricanes, tsunamis and floods are natural disasters, whereas man-made disasters may be strikes, terrorist attacks, etc. Failure at any point in the SC network has the potential to cause the entire network to fail. SCs must therefore be properly designed to survive well in the disruption scenario. The capability of successful survival (of the firm’s SC) against those adverse events/happenings is termed as resilience; and, the SC designed under resilience consideration is called a resilient SC. Effective supplier selection is considered as a key strategic consideration in SC management. It is felt that apart from considering traditional suppliers selection criterions, suppliers’ resiliency strategy must be incorporated while selecting a potential supplier which can provide best support to the firm even in the disaster/disruption scenario. The purpose of this paper is to focus aspects of evaluation and selection of resilience supplier by considering general as well as resiliency strategy, simultaneously. Design/methodology/approach – In this work, subjectivity associated with ill-defined (vague) evaluation information has been tackled through logical exploration of fuzzy numbers set theory. Application of VIKOR embedded with fuzzy mathematics has been utilized here. Sensitivity analysis has been performed to reflect the effect of decision-makers’ (DM) risk bearing attitude in selecting the best potential supplier in a resilient SC. A case empirical example has also been presented. Findings – The work attempts to focus on a decision-making procedural hierarchy towards effective supplier selection in a resilient SC. The work exhibits application potential of VIKOR method integrated with fuzzy set theory to select potential supplier based on general strategy as well as resiliency strategy. The final supplier selection score (obtained by considering general strategy) and that of obtained by analyzing resiliency strategy have been combined to get a final compromise solution. The decision-support framework thus reported here also considers DMs’ risk bearing attitude. Practical implications – The study bears significant impact to the industry managers who are trying to adapt resiliency strategy in their SC followed by potential supplier selection in the context of resilient SC. Originality/value – Exploration of VIKOR embedded with fuzzy set theory towards suppliers’ evaluation and selection by considering general and resiliency criteria both. The decision-support module(s) adapted in this paper considers DMs’ risk bearing attitude to arrive the best compromise solution.

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Fused deposition modeling (FDM) is a process by which functional parts can be produced rapidly through deposition of fused layers of material according to a numerically defined cross-sectional geometry. Literature suggests that process parameters largely influence on quality characteristics of rapid prototyping (RP) parts. A functional part is subjected to different loading conditions in actual practice. Therefore, process parameters need to be determined in such a way that they collectively optimize more than one response simultaneously. To address this issue, effect of important process parameters viz., layer thickness, orientation, raster angle, raster width, and air gap have been studied. The responses considered in this study are mechanical property of FDM produced parts such as tensile, bending and impact strength. The multiple responses are converted into a single response using principal component analysis (PCA) so that influence of correlation among the responses can be eliminated. Resulting single response is nothing but the weighted sum of three principal components that explain almost hundred percent of variation. The experiments have been conducted in accordance with Taguchi's orthogonal array to reduce the experimental runs. The results indicate that all the factors such as layer thickness, orientation, raster angle, raster width and air gap and interaction between layer thickness and orientation significantly influence the response. Optimum parameter settings have been identified to simultaneously optimize three responses. The mechanism of failure is explained with the help of SEM micrographs.