The Potential of Big Data Analysis in the Shipbuilding Industry: A Way of Increasing Competitiveness
Journal Title: MANAGEMENT CONTROL 
Author/s: Andrea Cappelli, Iacopo Cavallini 
Year:  2021 Issue: suppl. 1 Language: English 
Pages:  22 Pg. 53-74 FullText PDF:  339 KB
DOI:  10.3280/MACO2021-001-S1004
(DOI is like a bar code for intellectual property: to have more infomation:  clicca qui   and here 

 

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.
Keywords: Big Data, yachting industry, value chain

  1. Aramja A., Kamach O., Chafik S. (2015, December), Creating a baseline model of Manufacturing Execution Systems using Petri nets: Literature overview.
  2. Brynjolfsson E., McAfee A. (2011), Race against the machine: How the digital revolution is accelerating innovation, driving productivity, and irreversibly transforming employment and the economy, Brynjolfsson and McAfee.
  3. Bruce G., Garrard I. (2013), The business of shipbuilding, CRC Press.
  4. Brun L., Frederick S. (2017), Korea and the Shipbuilding Global Value Chain, Duke GVC Center.
  5. Buffo E., Carrozzo R., Scornavacca S., Chiodini I. (2019), Customer data platform e governance dei dati, Big Data Analytics & BI Observatory of the Polytechnic of Milan.
  6. Castellano N., Del Gobbo R. (2017), Data-Mining Tools for Business Model Design: The Impact of Organizational Heterogeneity. In: Corsi K., Castellano N., Lamboglia R., Mancini D. (eds), Reshaping Accounting and Management Control Systems. Lecture Notes in Information Systems and Organisation, vol 20. Cham (Switzerland): Springer., DOI: 10.1007/978-3-319-49538-5_15
  7. Chen M., Mao S., Zhang Y., Leung V.C. (2014), Big data: related technologies, challenges and future prospects (Vol. 96), Heidelberg, Springer.
  8. Cloud Security Alliance (2013), Big data analytics for security intelligence. -- https://downloads.cloudsecurityalliance.org/initiatives/bdwg/Big_Data_Analytics_for_Security_Intelligence
  9. Confederazione Nazionale dell’Artigianato e della Piccola e Media impresa (May 2019). Dinamiche e prospettive di mercato nella nautica da diporto, 7.
  10. Cupertino S., Vitale G., Riccaboni A. (2018), L’impatto dei Big Data sulle attività di pianificazione & controllo aziendali: un caso di studio di una PMI agricola Italiana, Management Control, 3, pp. 59-86,, DOI: 10.3280/MACO2018-003004
  11. Davenport T.H. (2006), Competing on analytics. Harvard business review, 84(1), 98.
  12. Despeisse M., Oates M.R., Ball P.D. (2013), Sustainable manufacturing tactics and cross-functional factory modelling, Journal of Cleaner Production, 42, pp. 31-41.
  13. ECORYS SCS group (2009), Study on Competitiveness of the European Shipbuilding Industry. In: European commission, 20.03.2011. -- Available from http://ec.europa.eu/ enterprise/sectors/maritime/files/fn97616_ecorys_final_report_on_shipbuilding_competitiveness_en.pdf.
  14. European Commission SWD (2012) 255 final. Progress of the EU’s Integrated Maritime Policy, Brussels, 11.9.2012
  15. Fan J., Han F., Liu H. (2014), Challenges of big data analysis, National science review, 1(2), pp. 293-314.
  16. Forsyth C., Chitor R. (2012), For Big Data Analytics There’s No Such Thing as Too Big: The Compelling Economics and Technology of Big Data Computing, White Paper. San Jose, CA, Forsyth Communications.
  17. Gandomi A., Haider M. (2015), Beyond the hype: Big data concepts, methods, and analytics, International journal of information management, 35(2), pp. 137-144.
  18. Gantz J., Reinsel D. (2011), Extracting value from chaos, IDC IView, 1142(2011), pp. 1-12.
  19. Global Order Book (December 2020), Boat International.
  20. Global SAP (June 2012), Small and midsize companies look to make big gains with big data, according to a poll conducted on behalf of SAP. -- Retrieved from httpp://global.sap.com/corporate-en/news.epx?PressID=19188.
  21. Greenhalgh T., Thorne S., Malterud K. (2018), Time to challenge the spurious hierarchy of systematic over narrative reviews? European Journal of Clinical Investigation, 48(6).
  22. Kaczynski W. (2011), The Future of Blue Economy: Lessons for European Union, Foundations of Management, 3(1), pp. 21-32.
  23. Kim S., Nam M., Sun M. (2016), Global Big Data Fusion Casebook. K-ICT Big Data Center.
  24. Laney D. (2001). 3D data management: Controlling data volume, velocity and variety, META group research notes, 6(70), 1.
  25. LaValle S., Lesser E., Shockley R., Hopkins M.S., Kruschwitz N. (2011), Big data, analytics and the path from insights to value, MIT sloan management review, 52(2), 21-32.
  26. Lee Y. (2017), A reference model for big data analysis in shipbuilding industry.
  27. Lee S., Jung I. (2019), Development of a Platform Using Big Data-Based Artificial Intelligence to Predict New Demand of Shipbuilding, The Journal of The Institute of Internet, Broadcasting and Communication, 19(1), pp. 171-178.
  28. Liu F., Chen C., Wu W. (2017), Research on intelligent design of luxury yacht, Procedia engineering, 174, pp. 927-933.
  29. Manyika, J. (2011), Big data: The next frontier for innovation, competition, and productivity. -- http://www.mckinsey.com/Insights/MGI/Research/Technology_and_Innovation/Big_data_The_next_frontier_for_innovation.
  30. Marchini P.L., Medioli A., Belli L., Davoli L. (2019), Internet of Things e Industria 4.0: un case study di successo di digital manufacturing, Management Control, 3, pp. 11-34., DOI: 10.3280/MACO2019-003002
  31. Merendino A., Gagliardo E.D., Coronella S. (2018), The efficiency of the top mega yacht builders across the world: a financial ratio-based data envelopment analysis, International Journal of Management and Decision Making, 17(2), pp. 125-147.
  32. Mickeviciene R. (2011), Global competition in shipbuilding: trends and challenges for Europe, The Economic Geography of Globalization, pp. 201-222.
  33. Mills S., Lucas S., Irakliotis L., Rappa M., Carlson T., Perlowitz B. (2012), Demystifying big data: a practical guide to transforming the business of government, Washington TechAmerica Foundation.
  34. Park W., Hwang S. (2016), Big Picture of Trend in 2016, KT Economic Research Institute.
  35. Patil A., Giffi C. (2015), Big data and Analytics in the Automotive Industry. Deloitte.
  36. Porter M.E. (1985), Value chain, The Value Chain and Competitive advantage: creating and sustaining superior performance.
  37. Porter M.E. (2000), Location, competition, and economic development: local clusters in a global economy, Economic Development Quarterly, 14(1), pp. 15-34.
  38. Schroeck M., Shockley R., Smart J., Romero-Morales D., Tufano P. (2012), Analytics: The real-world use of big data, IBM Global Business Services, 12(2012), pp. 1-20.
  39. Shin S.-J., Woo J., Rachuri S. (2014), Predictive analytics model for power consumption in manufacturing. Procedia CIRP, 15, pp.153-158.
  40. Sohn T. H. (2011). A study on associations among number of bidders, contract award rate and profitability on international construction. Journal of The Korean Society of Civil Engineers, 31(2D), 247-253.
  41. Song H.J., Park K.S., Jung H. E., Song M. (2013), Trend Analysis of Korean Economy in the Economic Literature by text mining techniques, In Proceedings of the Korean Society for Information Management Conference (pp. 47-50). Korean Society for Information Management.
  42. Sowar N., Gromley K. (2011), A sharper view: Analytic in the global steel industry.
  43. Tsai C.W., Lai C.F., Chao H.C., Vasilakos A.V. (2015), Big data analytics: a survey, Journal of Big data, 2(1), pp. 1-32.
  44. Vom Brocke J., Simons A., Niehaves B., Riemer K., Plattfaut R., Cleven A. (2009), Reconstructing the giant: On the importance of rigour in documenting the literature search process.
  45. Wang H., Osen O.L., Li, G., Li, W., Dai, H.N., Zeng W. (2015, November), Big data and industrial internet of things for the maritime industry in northwestern norway. In TENCON 2015-2015 IEEE Region 10 Conference (pp. 1-5). IEEE.
  46. Webster J., Watson R.T. (2002), Analyzing the past to prepare for the future: Writing a literature review, MIS quarterly, pp. xiii-xxiii.
  47. White M. (2012), Digital workplaces: Vision and reality. Business information review, 29(4), pp. 205-214.
  48. Xhafa F., Barolli L. (2014), Semantics, intelligent processing and services for big data, Future Generation Computer Systems, 37, pp. 201-202.
  49. Xiang Z., Schwartz Z., Gerdes Jr. J.H., Uysal M. (2015), What can big data and text analytics tell us about hotel guest experience and satisfaction, International Journal of Hospitality Management, 44, pp. 120-130.

Andrea Cappelli, Iacopo Cavallini, in "MANAGEMENT CONTROL" suppl. 1/2021, pp. 53-74, DOI:10.3280/MACO2021-001-S1004

   

FrancoAngeli is a member of Publishers International Linking Association a not for profit orgasnization wich runs the CrossRef service, enabing links to and from online scholarly content