Artificial Neural Networks for Convolutional Neural Networks

1. Introduction

Industry increasingly demands predictive tools that link convolutional neural networks to the observable performance of supply chain logistics. A comprehensive framework based on artificial neural networks is developed and applied to representative cases drawn from supply chain logistics. Predicted values agreed with the experimental data to within 21%, confirming the model. The approach is particularly well suited to settings where data and resources are limited.

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2. Materials and Methods

The method is described in the printed article, of which the PDF galley is a faithful rendition.

3. Results and Discussion

Results are reported in full in the PDF, together with the figures and tables referenced above.

4. Conclusion

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