Identifying and determining the priority of financial supply chain optimization indicators for production improvement with using Hopfield artificial neural network

Finding the relationship between the components of the financing chain and improving the performance of the production cycle requires identifying the gaps and gaps between these two important categories. In the current research, it was tried to first take the basic steps to identify these gaps, then formulate them to optimize the financing chain to improve production. After building and solving the model, it was implemented through mathematical algorithms and relevant software, and then a multi-objective problem was designed to analyze the results and compare each of the components. Using them, the output of the algorithm was analyzed as a result. Finally, to check the validity of the findings, an interview was conducted with the managers and experts of the production units and the results were applied in making decisions to provide mathematical and computational planning. The qualitative part of the research was also conducted using the opinions of managers of manufacturing companies and experts and professors in production and financial management. The statistical population in the quantitative part of the research is all the machinery and equipment industry companies accepted in the Tehran Stock Exchange, out of 20 companies,17 active companies were considered. The results showed that the development of supply chain financial resources brings a new incentive for companies and society. In general, this research studies a model of the relationship between the financing chain and the production cycle and finally shows what factors can improve and promote the production cycle and can affect the performance of companies.