Design of an innovation platform for manufacturing SMES

This paper reports on the conception of a collaborative, internet-based innovation platform with semantic capabilities, which implements a new methodology for the adoption of a systematic innovation process in globally-acting networked SMEs. The main objective of the innovation platform is to stimulate the generation of ideas, the selection of good ideas and their ultimate implementation. The platform will support SMEs to manage and implement the complex innovation processes arisen in a networked environment, taking into account their internal and external links, by enabling an open multi-agent focused innovation system, facilitating customer, provider, supplier and employee- focused innovation. The solution is specifically focused on the needs of manufacturing SMEs and will observe product, process and management innovation. The paper presents the key elements of the innovation model and makes references to a novel approach concerning the development of a robust and flexible Central Knowledge Repository for the innovation platform

Design and development of an emulated human cognition using novel 3D neural networks

This paper describes the development of an Emulated Human Cognition (EHC) which is designed and based on a replicated human brain with a right- and a left- hand lobe, one a deductive side and the other a generic one. Right-hand lobe consists of a newly designed Artificial Neural Network (ANN) with a multi-hidden layer topology. Left-hand lobe is a newly designed 3-dimensional cellular neural network. The input variables presented to the EHC are immediately analysed for it to decide which lobe should be activated. The EHC, when fully developed, has almost an unlimited memory capacity and is capable of immediate recall of any data in its almost unlimited memory locations. EHC has been used in several applications where neural networks have been used to establish relationship between two or more sets of variables. In this paper the EHC has been used to forecast demand for a given product

Development of a neural network mathematical model for demand forecasting in fluctuating markets

Research has shown that Neural Networks (NNs) when trained appropriately are the best forecasting system compared to conventional techniques. Research has shown that there is no system to accurately forecast sudden changes in demand for a given product. This paper reports on the development of a recovery method when a sudden change in demand has taken place. This error in forecasting demand leads to either excessive inventories of the product or shortages of it and can lead to substantial financial losses for the company producing or marketing the product. Two recovery methods have been developed and described in this paper: RZ recovery and Exponential Smoothing (ES). In the RZ recovery once a sudden change has taken place, a ‘soft’ Poke-Yoke (PY) system is setup warning the company that the normal forecasting system can no longer be relied upon and a recovery system needs to be initiated, with re-forecasting initiated

Optimisation of Economic Order Quantity Using Neural Networks Approach

Bu makalede. Geri yayılımlı Yapay Sinir Ağ (YSA) yapıları gerçek bir araba parçası sağlayan bir firmaya uygulanmıştır. Klasik yaklaşım, istenen talepleri Ekonomik Sipariş Miktarı (ESM) ile belirlemektir. YSA’nm eğitilebilir olması ve büyük meblağlı setleri paralel ve hızlı çözebilmesi geleceğe dönük siparişleri tahmin etme şansı doğurmaktadır. Burada gerçek bir firmanın akış şeması, ana satıcı firmalarla bağıntıları YSA yaklaşımı ile optimise edilmiş ve yeni bir YSA önerilmiştir. Sonuçların çok doğruya yakın bulunmuş olması, YSA modelinin gelecek vadetmesini sağlamaktadır.In this paper, a Back Propagation-Artificial Neural Network (BP-ANN) has been adapted for predicting the required car parts quantities in a real and major auto parts supplier chain. The conventional approach to determine the parts requirements is the Economic Order Quantity (EOQ) method. The ability of neural models to learn, particularly their capability of handling large amounts of data simultaneously as well as their fast response time, are the characteristics desired for predictive and forecasting purposes. Here, the actual data obtained from a major auto parts supplier chain, involving a multi-layer system of supplying auto parts to car dealers, have been used to optimise and develop a BP-ANN model. The model has shown promising results in predicting parts orders with high degree of accuracy

Genetic cellular neural network applications for prediction purposes in industry

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

Design and Development of Material and Information Flow for Supply Chaıns Using Genetic Cellular Networks

Son yıllarda, geri yay ılım tekniğine dayanan yapay sinir ağı (Ziarati and Ucan, January 2001) modeli ile gerçek bir firmanın malzeme tedarik zincirinde geleceğe dönük malzeme talep miktarı tahmin edilebilmiştir. Yapay sinir ağlarının hızlı olması, büyük miktardaki verinin ele alınabilmesi, malzeme akış diagramlarında geleceğe yönelik tahminlerde potensiel bir model olmalarını sağlamaktadır. Bu makale, (Ziarati and Ucan, January 2001) makalesinin geliştirilmiş biçimidir. Burada yapay sinir ağ (YSA) yapısı yerine Genetik Hücresel Yapay Sinir Ağ (HYSA) modeli konulmuştur. Söz konusu yaklaşım daha az parametre ile kestirim yapabilmekte ve dolayısıyla hızlı değişimli gerçek tedarik zincir problemlerine daha hızlı uyum sağlamaktadır. Önerilen modelin, tedarik zinciri problemlerinde, gerek eğitim sürecinin kısaltılmasında gerekse malzeme istek kestirimde üstün başarım göstermesi beklenmektedir.In a recent paper by authors (Ziarati and Ucan, January 2001) a Back Propagation-Artificial Neural Network (BP-ANN) was 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 and establishing associated scheduling throughout a given supply chain system. This paper should be considered a continuation of the first paper as the neural network approach introduced in this paper replaces the BP-ANN by a new method viz., Genetic Cellular Neural Network (GCNN). The latter approach requires by far less stability parameters 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

Design and Development of Material and Information Flow for Supply Chaıns Using Genetic Cellular Networks

Son yıllarda, geri yay ılım tekniğine dayanan yapay sinir ağı (Ziarati and Ucan, January 2001) modeli ile gerçek bir firmanın malzeme tedarik zincirinde geleceğe dönük malzeme talep miktarı tahmin edilebilmiştir. Yapay sinir ağlarının hızlı olması, büyük miktardaki verinin ele alınabilmesi, malzeme akış diagramlarında geleceğe yönelik tahminlerde potensiel bir model olmalarını sağlamaktadır. Bu makale, (Ziarati and Ucan, January 2001) makalesinin geliştirilmiş biçimidir. Burada yapay sinir ağ (YSA) yapısı yerine Genetik Hücresel Yapay Sinir Ağ (HYSA) modeli konulmuştur. Söz konusu yaklaşım daha az parametre ile kestirim yapabilmekte ve dolayısıyla hızlı değişimli gerçek tedarik zincir problemlerine daha hızlı uyum sağlamaktadır. Önerilen modelin, tedarik zinciri problemlerinde, gerek eğitim sürecinin kısaltılmasında gerekse malzeme istek kestirimde üstün başarım göstermesi beklenmektedir.In a recent paper by authors (Ziarati and Ucan, January 2001) a Back Propagation-Artificial Neural Network (BP-ANN) was 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 and establishing associated scheduling throughout a given supply chain system. This paper should be considered a continuation of the first paper as the neural network approach introduced in this paper replaces the BP-ANN by a new method viz., Genetic Cellular Neural Network (GCNN). The latter approach requires by far less stability parameters 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

Yapay sinir ağları yaklaşımı ile ekonomik düzenin optimizasyonu

In this paper, a Back Propagation-Artificial Neural Network (BP-ANN) has been adapted for predicting the required car parts quantities in a real and major auto parts supplier chain. The conventional approach to determine the parts requirements is the Economic Order Quantity (EOQ) method. The ability of neural models to learn, particularly their capability of handling large amounts of data simultaneously as well as their fast response time, are the characteristics desired for predictive and forecasting purposes. Here, the actual data obtained from a major auto parts supplier chain, involving a multi-layer system of supplying auto parts to car dealers, have been used to optimise and develop a BP-ANN model. The model has shown promising results in predicting parts orders with high degree of accuracy.Bu makalede, geri yayılımlı Yapay Sinir Ağ (YSA) yapıları gerçek bir araba parçası sağlayan bir firmaya uygulanmıştır. Klasik yaklaşım, istenen talepleri Ekonomik Sipariş Miktarı (ESM) ile belirlemektir. YSA’nm eğitilebilir olması ve büyük meblağlı setleri paralel ve hızlı çözebilmesi geleceğe dönük siparişleri tahmin etme şansı doğurmaktadır. Burada gerçek bir firmanın akış şeması, ana satıcı firmalarla bağıntıları YSA yaklaşımı ile optimise edilmiş ve yeni bir YSA önerilmiştir. Sonuçların çok doğruya yakın bulunmuş olması, YSA modelinin gelecek vadetmesini sağlamaktadır

Genetik hücresel yapay sinir ağ yapıları kullanılarak tedarik zincirleri için bilgi ve malzeme akışının geliştirilmesi

In a recent paper by authors (Ziarati and Ucan, January 2001) a Back Propagation-Artificial Neural Network (BP-ANN) was 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 and establishing associated scheduling throughout a given supply chain system. This paper should be considered a continuation of the first paper as the neural network approach introduced in this paper replaces the BP-ANN by a new method viz., Genetic Cellular Neural Network (GCNN). The latter approach requires by far less stability parameters and hence better suited to fast changing scenarios as in real supply chain applications.Son yıllarda, geri yay ılım tekniğine dayanan yapay sinir ağı (Ziarati and Ucan, January 2001) modeli ile gerçek bir firmanın malzeme tedarik zincirinde geleceğe dönük malzeme talep miktarı tahmin edilebilmiştir. Yapay sinir ağlarının hızlı olması, büyük miktardaki verinin ele alınabilmesi, malzeme akış diagramlarında geleceğe yönelik tahminlerde potensiel bir model olmalarını sağlamaktadır. Bu makale, (Ziarati and Ucan, January 2001) makalesinin geliştirilmiş biçimidir. Burada yapay sinir ağ (YSA) yapısı yerine Genetik Hücresel Yapay Sinir Ağ (HYSA) modeli konulmuştur. Söz konusu yaklaşım daha az parametre ile kestirim yapabilmekte ve dolayısıyla hızlı değişimli gerçek tedarik zincir problemlerine daha hızlı uyum sağlamaktadır. Önerilen modelin, tedarik zinciri problemlerinde, gerek eğitim sürecinin kısaltılmasında gerekse malzeme istek kestirimde üstün başarım göstermesi beklenmektedi