Yapay bağışıklık sistemi ve veri madenciliği yöntemlerini kullanarak tedarikçi değerlendirmede gösterge paneli uygulama modeli

Abstract

Küreselleşme, bilim ve teknolojideki hızlı gelişmeler, rekabetin artmasına, üretim yöntemlerindeki amaçların farklılaşmasına neden olmaktadır. Hızla değişen ve farklılaşan ihtiyaçların karşılanabilmesi, üretim yapan işletmeleri, teknolojik olarak yenilenmek ile karşı karşıya bırakmıştır. Özellikle elektronik ortamlarda biriken verinin kullanımı ve bilgiye erişimin kolaylaşması, işletmeleri, bilgisayar sistemleri ve üretim yönetimi noktasında gözden geçirmeye zorlamaktadır. Üretim yapan işletmelerin karar alma süreçlerinde, ihtiyaç duyulan bilgiyi karşılayabilmesi için, veri tabanlarında analiz edilen verilerin görselleştirilmesi, uygun bir çözüm olarak ortaya çıkmaktadır. Bu bağlamda gösterge paneli, özellikle üretim yapan işletmeler için, hızlı ve doğru karar alma noktasında, iyi bir destek aracı olarak görülmektedir. Bu makale, gösterge paneli başlığı altında, yapay bağışıklık sistemi ve veri madenciliği tekniklerini kullanarak, üretim yapan işletmelerde biriken verilerin analizi ve paylaşımı için, yeni bir model yaklaşımı sunar. Modelde, klonal seçim algoritması ile veriler çoğaltılır ve eğitilir. Analiz aşamasında k-means algoritması ile veriler kümelenir. Ağırlıklı ortalama ile performans göstergeleri hesaplanarak, veriler görselleştirilir. Elde edilen görseller, gösterge paneli kuralları ile karar vericilere destek olan, bir uygulama ile paylaştırılır. Yaklaşımımız, veri koleksiyonları birleştirmek, çözümlemek ve görselleştirmek için yeni bir yaklaşım modeli sunar.

Keywords

• Gösterge paneli
• Tedarik zinciri yönetimi
• Veri madenciliği
• Yapay bağışıklık sistemi

Dashboard application model in supplier evaluation by using artificial immune system and data mining methods

Abstract

Globalization and rapid developments in science and technology lead to an increase in competition and diffraction in the objectives in the production methods. In order to meet the rapidly changing and differentiated needs, manufacturing businesses are left against technological renewal. Especially usage of the data that is collected in electronic media and the ease of access to information forces businesses to review computer systems on point of production management. Visualization of the data analyzed in the databases is a suitable solution in the decision-making processes of the manufacturing companies. In this context, the dashboard is seen as a good support tool especially for the manufacturing businesses, at a fast and accurate decision-making point. This article represents a new model approach to accumulated analysis and its sharing for the manufacturing businesses by using the artificial immune system and data mining techniques under the title of the dashboard. In the model, data is increased and handled with clonal selection algorithm. In the analysis stage, the data is clustered with k-means algorithm. The data are visualized by calculating the weighted average and the performance indicators. The visuals that have been obtained will be shared with an app which supports the decision makers with the dashboard rules. Our approach provides a new approaching model to unite, analyze and visualize the collections of data.

Keywords

• Dashboard
• Supply chain management
• Data mining
• Artificial immune system

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