The roots of backpropagation: from ordered derivatives to neural networks and political forecasting
The roots of backpropagation: from ordered derivatives to neural networks and political forecasting
Neural network design
Business Dynamics
AI and Simulation-Based Techniques for the Assessment of Supply Chain Logistic Performance
ANSS '03 Proceedings of the 36th annual symposium on Simulation
Linking strategic objectives to operations: towards a more effective supply chain decision making
Proceedings of the 38th conference on Winter simulation
Supply chain optimisation using evolutionary algorithms
International Journal of Computer Applications in Technology
Expert Systems with Applications: An International Journal
Service level management of nonstationary supply chain using direct neural network controller
Expert Systems with Applications: An International Journal
Using neural networks to monitor supply chain behaviour
International Journal of Computer Applications in Technology
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Supply chain management is a critically significant strategy that enterprises depend on in meeting the challenges of today's highly competitive and dynamic business environments. An important aspect of supply chain management is how enterprises can detect the supply chain behavioral changes due to endogenous and/or exogenous influences and to predict such changes and their impacts in the short and long term horizons. A methodology for addressing this problem that combines system dynamics and neural networks analysis is proposed in this paper. We use neural networks' pattern recognition abilities to capture a system dynamics model and analyze simulation results to predict changes before they take place. We also describe how eigenvalue analysis can be used to enhance the understanding of the problematic behaviors. A case study in the electronics manufacturing industry is used to illustrate the methodology.