Browsing by Author "Özemre, Murat"

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Büyük veri analitiği yöntemiyle stratejik pazar analizi
(2019) Özemre, Murat; Kabadurmuş, Özgür
Today's competitive business environment forces companies to make better predictions and decisions for their business environments. Therefore, strategic market analysis is one of the most critical tasks for companies. However, business decision-makers should absorb the high volume of data with different views before making their strategic decisions. This dissertation presents a novel and holistic methodology for strategic market analysis by using Big Data Analytics. The proposed methodology of this dissertation employs two different machine learning algorithms, Random Forest (RF) and Artificial Neural Networks (ANN), to forecast the export volumes using an extensive amount of open trade data. Then, the forecasted values are included in the Boston Consulting Group (BCG) Matrix to conduct strategic market analysis. To demonstrate the effectiveness of the proposed methodology, two hypothetical case studies of a Turkish and Chinese company exporting refrigerators and freezers to the United Kingdom are considered and the managerial implications after implementing the proposed methodology are presented.
Citation - Scopus: 1
Using big data analytics to forecast trade volumes in global supply chain management
(IGI Global, 2019) Murat Özemre; Ozgur Kabadurmus; Kabadurmus, Ozgur; Özemre, Murat
As the supply chains become more global the operations (such as procurement production warehousing sales and forecasting) must be managed with consideration of the global factors. International trade is one of these factors affecting the global supply chain operations. Estimating the future trade volumes of certain products for specific markets can help companies to adjust their own global supply chain operations and strategies. However in today's competitive and complex global supply chain environments making accurate forecasts has become significantly difficult. In this chapter the authors present a novel big data analytics methodology to accurately forecast international trade volumes between countries for specific products. The methodology uses various open data sources and employs random forest and artificial neural networks. To demonstrate the effectiveness of their proposed methodology the authors present a case study of forecasting the export volume of refrigerators and freezers from Turkey to United Kingdom. The results showed that the proposed methodology provides effective forecasts. © 2020 Elsevier B.V. All rights reserved.