The model has shown promising outcomes in learning and predicting material demand in a supply chain, with high degree of accuracy.
📌 3 Key Takeaways
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Objective & Hypothesis:Genetic Cellular Neural Networks (GCNN) are adapted for predicting the required car parts quantities in a real and major auto parts supplier chain.
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Key Findings & Data:It was argued that due to the learning ability of neural networks, their speed and capacity to handle large amount of data, they have a potential for predicting components requirements.
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Conclusion & Impact:The model has shown promising outcomes in learning and predicting material demand in a supply chain, with high degree of accuracy.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters
Abstract
Genetic Cellular Neural Networks (GCNN) are adapted for predicting the required car parts quantities in a real and major auto parts supplier chain. It was argued that due to the learning ability of neural networks, their speed and capacity to handle large amount of data, they have a potential for predicting components requirements. GCNN use less stability parameters than Back Propagation-Artificial Neural Networks (BP-ANN) and hence better suited to fast changing scenarios as in real supply chain applications. The model has shown promising outcomes in learning and predicting material demand in a supply chain, with high degree of accuracy.
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Cite This Article
ZIARATI, M., STOCKTON, D., BİLGİLİ, E. (2012). GENETIC CELLULAR NEURAL NETWORK APPLICATIONS FOR PREDICTION PURPOSES IN INDUSTRY. Electrica, Cilt: 3 Sayı: 1, 683–691. https://ajindex.com/articles/1017724