A Reference Model for Big Data Analysis in Shipbuilding Industry

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A Reference Model for Big Data Analysis in Shipbuilding Industry
Lee, Yonghyeok
Lee, Changyong
Reference Model; Big Data Analysis; Shipbuilding Industry
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Graduate School of UNIST
Global shipbuilding industry has gone through a tough time due to the reduction of shipping order quantities and shipbuilding tonnages since the global financial crisis of 2008. To overcome the challenges, big data analysis is expected to be an effective solution to increase the practical efficiency in the shipbuilding industry. After an organization applies big data analysis, benefits, such as better aimed marketing, more straightforward straight-forward business insights, client based segmentation, and recognition of sales and market chances, are anticipated. In the future, the key for competitiveness is finding an appropriate way of applying big data analysis. Although numerous studies for big data analysis are conducted, the studies tend to focus on the technical aspect of analyzing data including method, algorithm, and architecture. Therefore, it is required to study how to applying the analysis technique in the practice, specifically shipbuilding industry in this study. In this thesis, the reference model for big data analysis in shipbuilding industry is developed. The proposed reference model provides the big data analysis guideline according to four phases such as contract, design, production, and service. They are categorized based on value chain of shipbuilding industry. Each phase consists of three levels of big data analysis, e.g., category of analysis, analysis method, and detailed algorithm. Moreover, the importance of the analysis method is determined in order to increase the applicability of the reference model. To verify the validation of the model, experts of the shipbuilding industry consulted the model it is consulted by the experts of the shipbuilding industry.
Department of Management Engineering
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