Application of neural networks in predicting 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 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.

Data Analytics in Supply Chain Management: A State-of-the-Art Literature Review

“…Ma et al, 2021;Wong et al, 2021;Abdella et al, 2020;Goodarzian et al, 2021;Moghimi & Beheshtinia, 2021;Nagendra et al, 2020;Kuo et al, 2021;Park, 2021), and performance management (Jiang et al 2021b;Chakraborty and Das, 2021;Li et al 2020c;Lunardi and Lima Junior, 2021;Abdelsamad et al, 2021). Supply chain collaboration and integration (Ali et al, 2021a;Gumte et al, 2021;Guillermo Muñoz et al, 2020;Xiang, 2020), supply chain network design (Zhou and Guo, 2021;Yang et al, 2021d) and transparency and traceability (Wong et al, 2021;Ping Zhang et al, 2021;Khan et al, 2020) have been discussed in recent DA publications.…”

Prospective of the circular economy in a banana agri-food chain

“…These four estimation tools were applied, which made it possible to obtain Spearman's correlation coefficients (ρ) (Table 7). Of the four estimation tools, three are robust, as stated by Muñoz et al (2020), because they have a value higher than 0.90. However, the strongest is the SVM for the regression-Linear…”

Adoption of Artificial Intelligence in Supply Chain Risk Management

“…Examples in the field of SCRM include an AI approach to curtailing the bullwhip effect inclusive of internal and external risks, as cited in a study by Aggarwal and Davè (2018). Other AI studies suggest it could model the likelihood of occurrence of risks (Ojha et al, 2018), diminish the risk in distribution management due to churn (Necula, 2017), predict and assess damage attributes in the field of logistics (Gürbüz et al, 2019), and forecast the level of integration in the supply chain to minimise risks (Muñoz et al, 2020). Advances in AI techniques and the massive growth in data generated along with the exponential rise in computing power have started benefiting the field of SCRM immensely.…”