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Electro discharge machining (EDM) process need to be optimized when a new material invented or even if some process variables changed. This process has many variables and it is always difficult to get the optimum set of variables by chance. Therefore, an optimization process need to be conducted considering different combinations of machining parameters as well as other variables even if the process were optimized for a certain set of variables. Optimization of the EDM process for machining stainless steel 304 (SS304) (ASTM A240) was studied in this paper. Signal-to-noise ratio (S/N) was calculated for each performance measures, and multi response performance index (MRPI) was generated using fuzzy logic inference system. Optimal machining parameters for machining SS304 materials were identified, namely current 10, pulse on time 60 μs, and pulse off time 35 μs. Analyses of variances (ANOVA) method was used as well to see which machining parameter has significant effect on the performance measures. The result of ANOVA indicates that pulse off time and current are the most significant machining parameters in affecting the performance measures, with the pulse off time being the most significant parameter.

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PurposeThe current research aims to analyze the literature to determine its strengths and weaknesses and extract the required information, which will be used to identify the characteristics of the highly competitive organization (HCO), define it and identify the HCO's critical success factors (CSFs). Finally, the future research agenda will be proposed.Design/methodology/approachA multiple stages research methodology was used to fulfill the research objectives. The research started with the systematic literature review (SLR). Then, focus group discussions and Pareto analysis were used to fulfill research objectives.FindingsEleven points were identified in the research to represent the characteristics of the HCO. Then, the HCO was defined based on the elements of these points. Moreover, the vital few CSFs to successfully implement many research scopes were identified. Then, the CSFs of the HCO was generated based on these vital few CSFs.Research limitations/implicationsThe main limitation of the current research is the literature sample size. A larger sample selection could enrich the generated lists with many other CSFs.Practical implicationsMany implications points were highlighted in this research which showed the importance of the current research for academic and practical audiences.Originality/valueThe SLR process showed that the reviewed literature lacked a consolidated list of the HCO characteristics and a clear definition of the HCO. Moreover, the reviewed literature lacked a unified list of the HCO CSFs. Therefore, the current research approach is novel and original.