PurposeBlock chain technology (BCT) has apparent capability of handling information in digital format, which has dragged attention of the practitioners for its utility in industrial and manufacturing practices. Conversely, the managerial adoption of BCT is relatively limited, which motivated the authors to identify crucial dimensions that can persuade the acceptance of BCT from an executive perspective. Thus, the present study is aimed to conduct to understand crucial barriers under BCT for managerial implementation in supply chain management (SCM) of small and medium enterprises (SMEs).Design/methodology/approachThe present study investigated evident barriers to understand implementation of BCT. A questionnaire based survey is performed to collect primary data from service and manufacturing based companies in India. Survey responses are received online and the data is analyzed in a scorecard. The scorecard embedded the scribed entries of Likert scale to determine the relative score.FindingsIn present study, sixteen barriers from three categories named as technological, organizational and environmental are evaluated, where, five sub-barriers from technological domain, seven sub-barriers from organizational domain and four sub-barriers from environmental domain are evaluated. The findings of the study determined that the three factors, i.e. “complexity in setup/use”, “Security and privacy concern” and “Technological awareness” mostly affect the adaptation of BCT in SCM. Conversely, “Market dynamics”, “Scalability” and “Cost” do not influence the intention to adopt the technology.Originality/valueOnly few studies have endeavored to ascertain the BCT adoption in SCM of SMEs in developing country like India. Thus, the study is filling a momentous gap of mapping BCT dimensions in the scholastic literature. The findings are expected to enable SMEs to understand important factors to be considered for adopting BCT in their curriculum. Furthermore, the study may benefit the BCT developers and suppliers to endure customized solutions based on the findings.
The machining of Aluminum-based Metal Matrix Composites (Al-MMCs) is challenging due to their inhomogeneity, anisotropic nature, and dynamic cutting forces. In this paper, the effect of machining parameters, including cutting speed, feed rate, and depth of cut, on surface quality (Ra) and cutting forces (Fc) during turning of Al-MMCs under different cutting conditions (DRY, WET, and MQL) was investigated. Statistical analysis tools were used to analyze the experimental results, and ANOVA and RSM techniques were used to model the relationships between machining parameters and responses. The results showed that feed rate had the most significant effect on both Ra and Fc for all machining conditions. The optimum feed rate of 0.03 mm/rev was found to produce the best surface finish and lower cutting forces in all conditions. The DRY mode of machining was found to be optimal for surface finish, and the MQL mode was found to be effective in reducing cutting forces due to its cooling and lubrication properties. Future research should focus on investigating the effect of different cutting tool materials and geometries on the machinability of Al-MMCs and developing more effective and sustainable machining strategies.
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