Browsing by Author "Ozemre, Murat"

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Citation - WoS: 36
Citation - Scopus: 51
A big data analytics based methodology for strategic decision making
(EMERALD GROUP PUBLISHING LTD, 2020) Murat Ozemre; Ozgur Kabadurmus; Kabadurmus, Ozgur; Ozemre, Murat
Purpose The purpose of this paper is to present a novel framework for strategic decision making using Big Data Analytics (BDA) methodology. Design/methodology/approach In this study two different machine learning algorithms Random Forest (RF) and Artificial Neural Networks (ANN) are employed to forecast export volumes using an extensive amount of open trade data. The forecasted values are included in the Boston Consulting Group (BCG) Matrix to conduct strategic market analysis. Findings The proposed methodology is validated using a hypothetical case study of a Chinese company exporting refrigerators and freezers. The results show that the proposed methodology makes accurate trade forecasts and helps to conduct strategic market analysis effectively. Also the RF performs better than the ANN in terms of forecast accuracy. Research limitations/implications This study presents only one case study to test the proposed methodology. In future studies the validity of the proposed method can be further generalized in different product groups and countries. Practical implications In today's highly competitive business environment an effective strategic market analysis requires importers or exporters to make better predictions and strategic decisions. Using the proposed BDA based methodology companies can effectively identify new business opportunities and adjust their strategic decisions accordingly. Originality/value This is the first study to present a holistic methodology for strategic market analysis using BDA. The proposed methodology accurately forecasts international trade volumes and facilitates the strategic decision-making process by providing future insights into global markets.
Using Big Data Analytics to Forecast Trade Volumes in Global Supply Chain Management
(IGI Global, 2022) Murat Özemre; Ozgur Kabadurmus; Kabadurmus, Ozgur; Ozemre, 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. © 2022 Elsevier B.V. All rights reserved.