The Conundrum of Digital Transformation in the Maritime Industry
Under the Auspices of H.E. the President of the Hellenic Republic, Mr. Prokopios Pavlopoulos
27th Annual Conference of the International Association of Maritime Economists (IAME)
CALL FOR PAPERS
An IAME2019 Special Session on
The Conundrum of Digital Transformation in the Maritime Industry
Ioannis N. Theotokas – University of the Aegean, Greece
Ioannis N. Lagoudis – University of the Aegean, Greece
Mohamed M. Naim – Cardiff University, United Kingdom
Okan Duru – Nanyang Technological University, Singapore
The transportation industry, let alone the maritime industry, is traditionally expected by shippers to readily adapt to market capacity requirements (Lagoudis, Naim and Potter, 2010). The maritime industry has traditionally suffered from the dynamic changes in demand requirements experiencing the so called “maritime cycles” (Stopford, 2007). These changes in demand create a turbulent business environment, where those who have the most resilient systems in place survive over time (Harlaftis and Theotokas, 2009). Shipowners / operators make decisions related to three main pillars, namely; asset financing, asset operation and shipper selection. These pillars are a function of a cost-based strategy applied by operators, which have been dominant in the maritime world. An integral part of the asset operation pillar is asset play (Theotokas and Harlaftis, 2004). By implementing anticyclical strategies (Thanopoulou, 1996) in buying and selling of ships, shipping companies are able to increase their return on investment. Thus, existing business models rely heavily on the success of applying cost-based and asset-play strategies, with the latter taking advantage of the maritime cycles, both contributing to the companies’ profitability (Thanopoulou and Theotokas, 1997; Duru, 2016; Kavussanos, and Alizadeh, 2002;). However, very limited research on business models has been published (Lagoudis, Lalwani, Naim, 2004; Lyridis et al., 2005).
The new business environment demands the maritime industry to shift from the traditional business model of selling capacity to one that will offer higher value to users. With the increasing need of global supply chains for seamless flow for goods and services, Digital Business is a key enabler to maritime companies today, since data is growing in velocity, volume and variety (Nguyen, et al., 2017; Larson and Chang, 2016). Swift decisions on spare part management, bunkering, maintenance and more, demand for real time systems, which will enable decision makers to make data driven decisions during the entire life cycle of the assets (from the newbuilding phase all the way to scrapping). For this reason, such systems require well-structured data lakes, quality data and visual simplicity (Kwon, Lee and Shin, 2014). In addition, man-machine harmonization is key to the success of such systems with training and skill development being critical (Jin et al., 2015). Market reports and announcements show that the major liner companies are moving ahead in collaboration with information technology providers in order to respond to the challenges of the new technological environment. More recently, A.P. Moller – Maersk, CMA CGM, Hapag-Lloyd, MSC and Ocean Network Express announced a plan to establish a container shipping association aiming at enhancing digitalization, standardization and interoperability in the container shipping industry.
During the IAME2019 conference in Athens, Greece, we intend to organize a session on the theme of digitalization in shipping. We seek full paper contributions and extended abstracts referring to any sector of the maritime industry, which will increase our understanding in any but not limited to the following issues:
- Conceptual business models in digitalization
- Digitalization of payment systems
- Cryptocurrency systems and applications
- Human resource management and digitalization
- Big data and data analytics models
- Predictive maintenance systems
- Machine learning
- Digital system representations (Digital Twin)
- Blockchain applications
- Supply chain visibility in shipping
- Additive manufacturing systems in the maritime digital space
- Case studies in digital applications
- Decision-making challenges with the use of business analytics (e.g. spares, inventory, chartering, supplies, bunkering)
- Cyber security models
- Building Information Modeling
Potential contributors are asked to submit an abstract following the regular submission schedule as published on the IAME2019 conference website https://www.dept.aueb.gr/en/iame2019 . You can opt for a full paper or extended abstract. During the uploading of the abstract kindly mention that the abstract is related to the special session. You are invited to concisely follow the author guidelines of IAME2019 which can be found online. It would be helpful if you could also inform the session organizers on the submitted abstract via e-mail to firstname.lastname@example.org, email@example.com, firstname.lastname@example.org and email@example.com.
Duru, O. (2016) "Motivations behind irrationality in the shipping asset management: Review of fundamental theories and practical challenges", Maritime Business Review, Vol. 1, No. 2, pp.163-184.
Harlaftis, G. and Theotokas I. (2009), “Maritime business during the twentieth century: continuity and change”, The handbook of maritime economics and business, 2nd Edition, The Grammenos Library.
Jin, X., Wah, B.W., Cheng, X., and Wang, Y. (2015), “Significance and Challenges of Big Data Research”, Big Data Research, Vol. 2, pp. 59-64.
Kavussanos, M.G. and Alizadeh, A.H. (2002), “Efficient pricing of ships in the dry bulk sector of the shipping industry”, Maritime Policy & Management, Vol. 29 No. 3, pp. 303-330.
Kwon, O., Lee, N. and Shin, B. (2014), “Data quality management, data usage experience and acquisition intention of big data analytics”, International Journal of Information Management, Vol., 34, pp. 387-394.
Lagoudis, I.N. Lalwani C.S. and Naim, M.M., (2004), “A Generic Systems Model for Ocean Shipping Companies in the Bulk Sector”, Transportation Journal, Vol. 43, No. 1, pp. 53-76.
Lagoudis, I.N., Naim, M.M. and Potter, A.T. (2010), “Strategic flexibility choices in the ocean transportation industry”, International Journal of Shipping and Transport Logistics, Vol. 2, No. 2, pp. 187-205.
Larson, D. and Chang, V. (2016), “A review and future direction of agile, business intelligence, analytics and data science”, International Journal of Information Management, Vol. 36, pp. 700-710.
Lyridis D.V., Fyrvik, T., Kapetanis, G.N., Ventikos, N., Anaxagorou, P., Uthaug, E., and Psaraftis, H.N. (2005), “Optimizing shipping company operations using business process modelling”, Maritime Policy and Management, Vol. 32, No. 4, pp. 403-420.
Nguyen, T., Zhou, L., Spiegler, V., Ieromonachou, P. and Lin, Y. (2017), “Big data analytics in supply chain management: A state-of-the-art literature review”, Computers and Operations Research, In Press, on line July.
Stopford, M. (1997), Maritime Economics, Routledge, London.
Thanopoulou E. and Theotokas I. (1997), “Pools in a bulk shipping perspective: asset play vs synergy benefits”, Occasional paper No. 46, Cardiff University, mimeo.
Theotokas, I. and Harlaftis G. (2004), “Greek Shipping Companies, 1945-2000: Management and Strategies”, Efpombi, Athens.