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

Yıl 2021, Cilt: 27 Sayı: 2, 162 - 172, 04.04.2021

Yüksel Yurtay , Murat Ayanoğlu

Öz

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.

Anahtar Kelimeler

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

Öz

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.

Anahtar Kelimeler

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

Kaynakça

• [1] Chopra S, Meindl P. Supply Chain Management: Strategy, Planning & Operations. 6nd ed. New Jersey, USA, 2007.
• [2] Pattnaik S, Sutar MK, Govindan K. “Supply Chain integration in relation to manufacturing industries”. IEEE International Conference on Computers & Industrial Engineering, Troyes, France, 6-9 July 2009.
• [3] Yigitbasioglu OM, Velcu O. “A review of dashboards in performance management: Implications for design and research”. International Journal of Accounting Information Systems, 13(1), 41-59, 2011.
• [4] Lempinen H. “Constructing a design framework for performance dashboards”. Nordic Contributions in IS Research, 1(1),109-130, 2012.
• [5] James T. “Smart factories”. Engineering & Technology, 7(6), 64-67, 2012.
• [6] Gröger C, Stach C, Mitschang B, Westkämper E. “A mobile dashboard for analytics-based information provisioning on the shop floor”. International Journal of Computer Integrated Manufacturing, 29(12), 1335-1354, 2016.
• [7] Collins JD, Worthington WJ, Reyes PM, Romero M. “Knowledge management, supply chain technologies, and firm performance”. Management Research Review, 33(10), 947-960, 2010.
• [8] Peral J, Mate A, Macro M. “Application of data mining techniques to identify relevant key performance indicators”. Computer Standards & Interfaces, 54(2), 76-85, 2017.
• [9] Choudhary AK, Harding JA, Lin HK, Tiwari MK, Shankar R. “Knowledge discovery and data mining integrated (KOATING) Moderators for collaborative projects”. International Journal of Production Research, 49(23), 7029-7057, 2011.
• [10] Banerjee A, Bandyopadhyay T, Acharya P. “Data analytics: Hyped up aspirations or true potential?”. The Journal for Decision Makers, 38(4), 1-11, 2013.
• [11] Lau HCW, Ho GTS, Zhao Y, Chung NSH. “Development of a process mining system for supporting knowledge discovery in a supply chain network”. International Journal of Production Economics, 122(1), 176-187, 2009.
• [12] Hadighi SA, Sahebjamnia N, Mahdavi I, Akbarpour SM. “A framework for strategy formulation based on clustering approach: A case study in a corporate organization”. Knowledge-Based Systems, 49(1), 37-49, 2013.
• [13] Olson DL, Shi Y. Introduction to Business Data Mining. 1nd ed. London England, USA, McGraw Hill, 2006.
• [14] Faliu YF, Moon I. “Extended K-Means algorithm”. 5th International Conference on Intelligent Human-Machine Systems and Cybernetics, Hangzhou, China, 26-27 August 2013.
• [15] Fahad A, Alshatri N, Tari Z, Alamri A, Khalil İ, Zomaya AY. “A survey of clustering algorithms for big data: taxonomy and empirical analysis”. IEEE Transactions on Emerging Topics in Computing, 2(3), 267-279, 2014.
• [16] Chae B, Olson D, Sheu C. “The impact of supply chain analytics on operational performance: A resource- based view”. International Journal of Production Research, 52(16), 4695-4710, 2014.
• [17] Olson DL. “A Review of supply chain data mining publications”. Journal of Supply Chain Management Science, 1, 1-2, 2020.
• [18] Chaudhuri S, Dayal U, Ganti V. “Database technology for decision support systems”. IEEE Transactions on Automation Science and Engineering, 34(12), 48-55, 2001.
• [19] Ren C, Liu Y, Guo Y. “Fuzzy evaluation on supply chain competitiveness based on membership degree transformation new algorithm”. Journal of Chemical and Pharmaceutical Research, 6(2), 139-144, 2014.
• [20] Jun T, Kai C, Yu F, Gang T. “The research & application of ETL tool in business intelligence project”. International Forum on Information Technology and Applications, Chengdu, China, 1-8 May 2009.
• [21] Liu L. “Supply Chain Integration through Business Intelligence”. International Conference on Management and Service Science, Wuhan, China, 09-11 August 2010.
• [22] Tokolaa H, Grögerb C, Järvenpääc E, Niemi E. “Designing manufacturing dashboards on the basis of a Key Performance Indicator survey”. 49th CIRP Conference on Manufacturing Systems, Budapest, Hungary, 25-27 May 2011.
• [23] Akpınar MY, Gayberi M, Orman E, Öğütücü ŞG. “Kurumsal raporlama çözümlerinde bellek içi veritabanı kullanımı”. Turkish National Software Engineering Symposium, Güzelyurt, KKTC, 8-10 September 2014.
• [24] White JA, Garrett SM. “Improved pattern recognition with artificial clonal selection?”. Artificial Immune Systems, 2787(1),181-193, 2003.
• [25] Zheng J, Chen Y, Zhang W. “A Survey of artificial immune applications”. Artificial Intelligence Review, 34(1), 19-34, 2010.
• [26] Hatata AY, Osman MG, Aladl MM. “A review of the clonal selection algorithm as an optimization method”. Leonardo Journal of Sciences, 30(1), 1-14, 2017.
• [27] Babayigit B, Guney K, Akdagli A. “A clonal selection algorithm for array pattern nulling by controlling the positions of selected elements”. Progress In Electromagnetics Research B, 6(1), 257-266, 2008.
• [28] Yavuz BÇ, Yurtay N, Ozkan O. “Prediction of protein secondary structure with clonal selection algorithm and multilayer perceptron”. IEEE Access, 6(1), 45256-45261, 2018.
• [29] Vincent OR, Makinde AS, Salako OS, Oluwafemi OD. “A self-adaptive k-means classifier for business incentive in a fashion design environment”. Applied computing and informatics, 14(1), 88-97, 2018.
• [30] Gustafsson J, Karlsson E. Supplier Performance Dashboard At Volvo Logistics. MSc Thesis, Department of Technology Management and Economics Chalmers University of Technology, Göteborg, Sweden, 2012.
• [31] Development of Decision Making Systems. “Data Science”. https://www.ronaldvanloon.com (07.12.2019).
• [32] Capacent AB-MS. “Power BI Dataflows-Data Warehousing Made Simple?”. https://capacent.com/sv/about/news/2019/microsoft-power-bidataflows-data-warehousing-made-simple (14.12.2019).
• [33] Ferrari A, Russo M. Analyzing Data with Microsoft Power BI and Power Pivot for Excel. New York, USA, Microsoft Press, 2017.
• [34] Salem, R. Abdo, A. “Fixing rules for data cleaning based on conditional functional dependency”. Future Computing and Informatics Journal, 1(1), 1-2, 2016.
• [35] Ferrari A, Russo M. The Definitive Guide to DAX: Business intelligence with Microsoft Excel, SQL Server Analysis Services, and Power BI. New York, USA, Microsoft Press, 2015.
• [36] Doherty R, Sorenson P. “Keeping users in the flow: mapping system responsiveness with user experience”. Procedia Manufacturing, 3(1), 4384-4391, 2015.
• [37] Erdal M. Satınalma ve Tedarik Zinciri Yönetimi. 3. baskı. İstanbul, Türkiye, Beta, 2014.
• [38] Riggins F, Klamm, B. “Data governance case at KrauseMcMahon LLP in an era of self-service BI and Big Data”. Journal of Accounting Education, 38(1), 23-36, 2017.
• [39] Rasmussen N, Chen CY, Bansal, M. Business Dashboards A Visual Catalog for Design and Deployment. New Jersey, USA, Wiley, 2009.
• [40] Rainardi, V. Building a Data Warehouse: With Examples in SQL Server, New York, USA, Apress, 2008.
• [41] Allen, S. Terry, E. Beginning Relational Data Modeling. New York, USA, Apress, 2005.