Application of neural networks in pbkp_redicting the level of integration in supply chains


Abstract:

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 pbkp_rediction 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 which is used to pbkp_redict the level of integration in a supply chain. This type of pbkp_redictive 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 pbkp_redicting the level of integration in the supply chain from the neuronal network. This provides a coached neuron that can be applied in other studies and, therefore, pbkp_redict 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 pbkp_redict 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 pbkp_redicted as a result of the coaching of the neuron with the weights of the heterogeneous and homogeneous variables.

Año de publicación:

2020

Keywords:

  • Neural networks
  • Integration processes
  • Supply chains

Fuente:

scopusscopus

Tipo de documento:

Article

Estado:

Acceso abierto

Áreas de conocimiento:

  • Logística
  • Aprendizaje automático
  • Software

Áreas temáticas:

  • Dirección general