Provide a Model in Determining the Impact of Block-Chain Technology on Supply Chain Performance using Fuzzy Inference Systems in the Automobile Industry

The purpose of this study is to provide a model in determining the impact of block-chain technology on supply chain performance in the automobile industry using a fuzzy inference system design. In terms of purpose, this research is considered as applied research. The statistical population is the managers of three automobile industrial companies in Iran, including IranKhodro, Saipa, and ModiranKhodro. The statistical sample size of 36 people was selected by a combination of two methods Nonprobability sampling (Judgmental) and Snowball sampling. Using library and field studies, the factors and sub-factors of the research have been selected. After library studies, seventeen sub-criteria in three main criteria of attenuating, amplifier, and possibility were identified as a conceptual model. After Gaussian fuzzy, 125 rules are set, based on experts' opinions. According to the results, if the changes in the attenuating interval are more than 0.5, it will reduce the performance, and if it is less than 0.5, it will not have much effect on the performance. If the changes in the amplifier and possibility intervals are less than 0,5, it creates a moderate level of change in performance. And if greater than 0.5, it will increase performance output. To validate and ensure its efficiency, Extreme Condition test has been used. The results of test for three SUB-FIS and all the designed FIS, showed reasonable behavior towards the limit’s values of the inputs, which indicates the validity of the designed model and that means the system has good accuracy and validity of the evaluation.