The Potential of Big Data Analysis in the Shipbuilding Industry: A Way of Increasing Competitiveness

2021 ◽  
pp. 53-74
Author(s):  
Andrea Cappelli ◽  
Iacopo Cavallini

It is possible to exploit potentials of Big Data in the shipbuilding industry in order to increase efficiency and company performance. Big Data analysis will probably have a great impact on strengthening the competitiveness in the whole sector, providing various types of benefits and effective support to the decision-making system. Academics maintain that analysis methods and algorithms can offer spe-cific guidelines to managers and practitioners in order to satisfy their information needs. Even though it is recognized that the techniques for Big Data analysis are relevant, only a few studies provide practical guidelines on how to apply these techniques in specific industries like shipbuilding. This preliminary study aims to develop a conceptual framework of Big Data anal-ysis based on the value chain approach. By using a deductive methodology, the framework is built taking into consideration four phases of the value chain in the shipbuilding industry - i.e. pre-production, design, production, and post-production. For its relevance, the study considers the pre-production phase, trying to classify data sources, analysis methods, and algorithms for the main activities of this node and also providing various suggestions to shipbuilding managers and practitioners. The researchers develop the framework by considering secondary data collected from the literature analysis. Our results can successfully support decision making in shipbuilding companies, making processes and operations more cost-effective and helping companies be more competitive. Specifically, in the pre-production node this will lead to real-time demand forecasting and a more reliable estimation of initial production costs.

2020 ◽  
Vol 3 (1) ◽  
pp. 17-35
Author(s):  
Brian J. Galli

In today's fiercely competitive environment, most companies face the pressure of shorter product life cycles. Therefore, if companies want to maintain a competitive advantage in the market, they need to keep innovating and developing new products. If not, then they will face difficulties in developing and expanding markets and may go out of business. New product development is the key content of enterprise research and development, and it is also one of the strategic cores for enterprise survival and development. The success of new product development plays a decisive role both in the development of the company and in maintaining a competitive advantage in the industry. Since the beginning of the 21st century, with the continuous innovation and development of Internet technology, the era of big data has arrived. In the era of big data, enterprises' decision-making for new product development no longer solely relies on the experience of decision-makers; it is based on the results of big data analysis for more accurate and effective decisions. In this thesis, the case analysis is mainly carried out with Company A as an example. Also, it mainly introduces the decision made by Company A in the actual operation of new product development, which is based on the results of big data analysis from decision-making to decision-making innovation. The choice of decision-making is described in detail. Through the introduction of the case, the impact of big data on the decision-making process for new product development was explored. In the era of big data, it provides a new theoretical approach to new product development decision-making.


2021 ◽  
Author(s):  
Chaojie Li

From self-driving vehicles, voice recognition based virtual digital assistants, smart thermostats to recommendation systems, Artificial Intelligence (AI) is becoming a crucial part of the carbon neutral society that has drawn considerable interest from energy supply firms, startups, technology developers, financial institutions, national governments and the academic community. The emergence of AI initiates numerous opportunities to transform energy industry to AI-powered smart system which can revolutionize traditional approaches of creativity thinking, strategical operation, and solution seeking, especially for accelerating carbon neutrality of our society. This survey provides a comprehensive overview of fundamental principles that underpin applications of big data analysis in Energy Internet (EI), such as smart energy supply and consumption, smart health and Fintech. Next, we focus on intelligent decision-making for the energy industry and inform the state-of-the-art by thoroughly reviewing the literature. Subsequently, cybersecurity issues for AI system related to EI are discussed with recent advancements from vulnerability analysis of AI system to differential privacy and to blockchain based security technology. To our knowledge, this is one of the first academic, peer-reviewed works to provide a systematic review of AI applications for EI research and initiatives in terms of big data analysis, intelligent decision-making and AI related cybersecurity These initiatives were systematically classified into different groups according to the field of application, methodology and contribution Afterwards, potential challenges, limitations for existing research and opportunities for future directions are discussed, ranging from emerging explainable AI, to localized multi-energy marketplaces, self-driving electric vehicle charging and e-mobility. This paper can help us understand how to build smart cities and critical infrastructure for a climate-changed world towards the UN’s sustainable development goals.


Author(s):  
А.В. Михеев

В статье рассматриваются возможности применения методов анализа больших данных для принятия решений по инновационному развитию в энергетике. Выполнен библиометрический обзор научных исследований по использованию анализа больших данных для задач в сфере энергетики на основе публикаций международной базы Scopus за 2010-2020 гг. Приведены содержательные задачи мониторинга, прогнозирования и оценки перспективности технологических решений в энергетике на основе семантического анализа больших данных. The article discusses the feasibility and possible applications of big data analysis for making decisions on innovative development in the energy sector. A bibliometric review of scientific research on the use of big data analysis for problems in the energy sector was carried out based on publications of Scopus database for 2010-2020. The substantive tasks of monitoring, forecasting and evaluating the prospects of technological solutions in the energy sector based on semantic analysis of big data are presented.


Author(s):  
Khadija Ali Vakeel

This chapter elaborates on mining techniques useful in big data analysis. Specifically, it will elaborate on how to use association rule mining, self organizing maps, word cloud, sentiment extraction, network analysis, classification, and clustering for marketing intelligence. The application of these would be on decisions related to market segmentation, targeting and positioning, trend analysis, sales, stock markets and word of mouth. The chapter is divided in two sections of data collection and cleaning where we elaborate on how twitter data can be extracted and mined for marketing decision making. Second part discusses various techniques that can be used in big data analysis for mining content and interaction network.


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