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In the technology-driven industries like automotive industry, the success of the companies depends on their ability to provide new and continuous innovations to the market. In this regard, technological capabilities lead to a competitive advantage. The purpose of this study is to identify the effective factors in the technology assessment based on the dynamic capabilities of small and medium automotive supply chains. In this study, by using the principal components analysis (PCA) method, the identification of the important factors according to the views of automotive industry experts has been addressed. The results show that from the perspective of dynamic capabilities, Absorbency and learning, Innovative capability and integration capabilities are the most effective factors. In terms of technology assessment, Processes and organizational structure and level of technology development are the most important factors identified in order to assess the technology of these companies. In addition, in this study, due to the effective factors, a new technology assessment model with dynamic capabilities approach has been proposed and a relevant questionnaire has been developed. The results of reliability analysis show that the proposed questionnaire has an appropriate accuracy and reliability in technology assessment of small and medium automotive supply chain companies.

Nowadays, the rapid growth of data in organizations has caused managers to look for a way to analyze them. Extracting useful knowledge from aggregation data can lead to appropriate strategic decision-making for the organization. This paper suggests an application of hybrid network based on amount month demand in every ATM device based on transaction mean of 9 months for 1377 devices to obtain customer behavior patterns, to do so, first designed a basic model based on an auto-regressive with exogenous input network (NARX) then, the optimization of the weight and bias of the designed network is made by the genetic algorithm (GA). As a result, finding the weights of the network represents a nonlinear optimization problem that is solved by the genetic algorithm. Paper results show that the NARX-ANFIS Hybrid network using GA for the learning of rules and to optimize the network weights and weights of the network and the fixed threshold can improve the accuracy of the prediction model. Also, classic models are more efficient and increased benefits and lower financing costs and more rational inventory cash control. As well, the designed model can lead to increase benefits and decrease costs in the bank so that, exact forecast and optimal cash upload in ATMs will lead to increase funds on the bank and rise customers and popularity the brand of the bank.