Using big data analytics to forecast trade volumes in global supply chain management

Abstract

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.