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Newly prepared titanium alloy (Ti-13Zr-13Nb (TZN)) using powder metallurgy is considered in this investigation. Titanium alloys (TZN) are used in hip and knee replacement for orthopedic implants. Conventional machining, TZN alloys produce higher tool wear rate and poor surface quality, but this can be reduced by Electrical Discharge Machining (EDM) method. Moreover, EDM produce good biological and corrosion resistant surface. In this research, experiments were conducted by considering the influential process factors such as pulse on time, pulse off time, voltage, and current. The experiments were designed based on Response Surface Methodology (RSM) of face centered central composite design. Analysis of Variance (ANOVA) was conducted to identify the significance process factors and their relation to output responses such as Electrode Wear Rate (EWR), Surface Roughness (SR) and Material Removal Rate (MRR). Further, an empirical model was developed by RSM in order to predict the output responses.

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PurposeThe study aims to present the hybrid approach of multiple MCDM techniques with strategic perspective to assist the vendor ranking process.Design/methodology/approachMultiple MCDM techniques such as fuzzy QFD, mathematical modelling and ANP (analytical network process) are integrated in the model for vendor ranking. Multiple phases in vendor ranking such as pre‐qualification and final selection are dealt with using the above techniques.FindingsCompared to individual approaches, the proposed hybrid model effectively assists the vendor ranking process. The efficacy of the proposed approach is evident from the case study of an automotive components manufacturer involving 20 vendors comprising pre‐qualification by fuzzy QFD and final selection by ANP. This set of potential vendors is evaluated based on three main criteria and eight sub criteria.Originality/valueFuzzy QFD is employed for qualifying supplier to form a supplier pool, as it is helpful in converting qualitative information into quantitative parameters. This data is then combined with other quantitative data to form a mathematical model. The mathematical model is solved by the method of integer programming, using TORA. ANP with BOCR (benefits, opportunities, costs, and risks) is proposed for evaluating and selecting appropriate supplier. ANP model is solved using Super Decision package.