PurposeThe purpose of this paper is to present a novel framework for strategic decision making using Big Data Analytics (BDA) methodology.Design/methodology/approachIn 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.FindingsThe 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/implicationsThis 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 implicationsIn 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/valueThis 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.

SUMMARYThe objective of this paper is to present a cohesive measurement planning framework based on our successful practices during the implementation of a measurement process in a leading custom software development company in the logistics service sector in Turkey. The framework established is the result of the process of understanding how to plan software maintenance measurement that conforms to existing ISO/IEC standards, including ISO/IEC 9126, ISO/IEC 14598, and ISO/IEC 15939, and also combining the theoretical aspects of these standards with the realities of the software maintenance process in order to develop a software application. The main contributions of the article include the presentation of a practical solution in ISO-based software measurement methodology and the elimination of the unnecessary and redundant ISO/IEC 9126 quality characteristics or sub-characteristics for the purpose of application of quality model to the company, and identification of the suitable base and derived measurement sets for satisfying the company's infonnation needs.

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.

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.