PurposeIn this paper, the authors adopt the theory of the circular economy to study the transitions that take place in three case studies in Mexico and Ecuador. The work is aimed to systematize a circular economy assessment tool that fosters opportunities for improvement in business practices.Design/methodology/approachThe methodology is based on a descriptive quantitative analysis, where a checklist is made with 91 items and nine study variables. This is from the study of the bibliography and business practice. Furthermore, the neural network method is used in a case study to predict the level of circular economy and the importance of each variable according to the sensitivity by the Lek’s profile method.FindingsIt is based on a descriptive quantitative analysis, where a checklist with 91 items and nine study variables is made, defined from a bibliographic study and business practice. Furthermore, the neural network method is used in a case study to predict the level of circular economy and the importance of each variable based on sensitivity.Research limitations/implicationsThe application of the tool requires prior knowledge of the circular economy approach, which is why specialized personnel are needed for its application. This makes research more expensive in time and human resources.Practical implicationsThe practical and methodological contribution of this work lies in the feasibility of the tool that favors the definition of improvement actions for the implementation contribution to the circular economy in business practices.Social implicationsThe social contribution is framed in the gradual transition to circular economy approaches in underdeveloped countries.Originality/valueThe use of the neural network method to predict the level of circular economy in a case study allows making decisions in a predictive way. This encourages the development of the circular economy according to the context needs.
Purpose: This investigation is based on the theoretical analysis of the application of neural networks to the design and manage supply chains, along with an empirical approach, this investigation its developed with the prediction of the level of integration in the supply chain through neural networks.Design/methodology/approach: The methodology designed and used for the processing of data was the instruction of a neural network wich is used to predict the level of integration in a supply chain. This type of predictive application appears in the literature reviewed on supply chains. This analysis was carried out in a comparative way with the heterogeneous and homogeneous weights of the neuron training.Findings: The main results of this research focus on predicting the level of integration in the supply chain from the neoronal network. This provides a coached neuron that can be applied in other studies and, therefore, predict the outcome. On the other hand, it is shown that if the weights of the integration level variables are not homogeneous, the procedure presents different results depending on the context in which it is developed.Research limitations/implications: Among the limitations of the implementation of neural networks it should be noted, the necessary adaptation to the characteristics of the supply chains and the areas of performance of the business organizations under study, in the framework of activities productive or service itself, in addition to analyzing its corporate purpose in relation to the satisfaction of certain needs of the target markets.Originality/value: The literature shows multiple theoretical sources that refer to studies of neural networks in supply chains, observing the opportunity to apply this technique to predict the level of integration due to its benefits for decision making. The originality of this scientific work lies in the possibility of comparing the historical data of the level of integration and those predicted as a result of the coaching of the neuron with the weights of the heterogeneous and homogeneous variables.
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