Graphical Abstract Figure
Abstract
The maritime industry is urged to reduce greenhouse gas emissions and improve the energy efficiency of ships. A potential and relatively inexpensive solution is to implement data analytics to identify improvement areas to optimize ships' performance and fuel consumption. This study investigates barriers to data analytics for maritime organizations intending to utilize data as a means of operational enhancement. This study used the DELPHI–best worst method hybrid approach to identify and rank the barriers to data analytics for energy efficiency. The results revealed a total of 20 sub-barriers grouped into five main barriers. These barriers fall into two overarching categories: organizational barriers, including cultural, managerial, and economic, and technological barriers, comprising data management and data analysis. This study also highlights the most critical barriers within each category, revealing inadequate data governance, multiple suppliers needed to implement a comprehensive system and contracts, and restrictive clauses as the dominant barriers that hamper data analytics adoption in the maritime domain.
References
1.
IMO
, 2020
, Fourth IMO GHG Study 2020
, International Maritime Organization (IMO)
.2.
Sofiev
, M.
, Winebrake
, J. J.
, Johansson
, L.
, Carr
, E. W.
, Prank
, M.
, Soares
, J.
, Vira
, J.
, Kouznetsov
, R.
, Jalkanen
, J.-P.
, and Corbett
, J. J.
, 2018
, “Cleaner Fuels for Ships Provide Public Health Benefits With Climate Tradeoffs
,” Nat. Commun.
, 9
(1
), p. 406
. 3.
Eyring
, V.
, Isaksen
, I. S.
, Berntsen
, T.
, Collins
, W. J.
, Corbett
, J. J.
, Endresen
, O.
, Grainger
, R. G.
, Moldanova
, J.
, Schlager
, H.
, and Stevenson
, D. S.
, 2010
, “Transport Impacts on Atmosphere and Climate: Shipping
,” Atmos. Environ.
, 44
(37
), pp. 4735
–4771
. 4.
IMO
, 2023
, “2023 IMO Strategy on Reduction of GHG Emissions From Ships
”.5.
Jimenez
, V. J.
, Kim
, H.
, and Munim
, Z. H.
, 2022
, “A Review of Ship Energy Efficiency Research and Directions Towards Emission Reduction in the Maritime Industry
,” J. Clean. Prod.
, 366
, p. 132888
. 6.
Psaraftis
, H. N.
, Zis
, T.
, and Lagouvardou
, S.
, 2021
, “A Comparative Evaluation of Market Based Measures for Shipping Decarbonization
,” Mar. Transp. Res.
, 2
, p. 100019
. 7.
Acciaro
, M.
, Hoffmann
, P. N.
, and Eide
, M. S.
, 2013
, “The Energy Efficiency Gap in Maritime Transport
,” J. Shipp. Ocean Eng.
, 3
(1–2
), p. 1
.8.
Johnson
, H.
, Johansson
, M.
, and Andersson
, K.
, 2014
, “Barriers to Improving Energy Efficiency in Short Sea Shipping: An Action Research Case Study
,” J. Clean. Prod.
, 66
, pp. 317
–327
. 9.
Jafarzadeh
, S.
, and Utne
, I. B.
, 2014
, “A Framework to Bridge the Energy Efficiency Gap in Shipping
,” Energy
, 69
, pp. 603
–612
. 10.
Dewan
, M. H.
, Yaakob
, O.
, and Suzana
, A.
, 2018
, “Barriers for Adoption of Energy Efficiency Operational Measures in Shipping Industry
,” WMU J. Mar. Affairs
, 17
(2
), pp. 169
–193
. 11.
Zhang
, X.
, and Lam
, J. S. L.
, 2019
, “A Fuzzy Delphi-AHP-TOPSIS Framework to Identify Barriers in Big Data Analytics Adoption: Case of Maritime Organizations
,” Mar. Policy Manage.
, 46
(7
), pp. 781
–801
. 12.
Adoption
, I. M. O.
, 2018
, “Of the Initial IMO Strategy on Reduction of GHG Emissions From Ships and Existing IMO Activity Related to Reducing GHG Emissions in the Shipping Sector,” Note by the International Maritime Organization to the UNFCCC Talanoa Dialogue
, International Maritime Organization
, London, UK
, pp. 1
–27
.13.
Rehmatulla
, N.
, and Smith
, T.
, 2015
, “Barriers to Energy Efficiency in Shipping: A Triangulated Approach to Investigate the Principal Agent Problem
,” Energy Pol.
, 84
, pp. 44
–57
. 14.
Viktorelius
, M.
, and Lundh
, M.
, 2019
, “Energy Efficiency at Sea: An Activity Theoretical Perspective on Operational Energy Efficiency in Maritime Transport
,” Energy Res. Soc. Sci.
, 52
, pp. 1
–9
. 15.
Jia
, H.
, Adland
, R.
, Prakash
, V.
, and Smith
, T.
, 2017
, “Energy Efficiency With the Application of Virtual Arrival Policy
,” Transp. Res. Part D: Transp. Environ.
, 54
, pp. 50
–60
. 16.
Manyika
, J.
, Chui
, M.
, Brown
, B.
, Bughin
, J.
, Dobbs
, R.
, Roxburgh
, C.
, and Hung Byers
, A.
, 2011
, “Big Data: The Next Frontier for Innovation, Competition, and Productivity
”.17.
Zikopoulos
, P.
, and Eaton
, C.
, 2011
, Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data
, McGraw-Hill Osborne Media
.18.
Russom
, P.
, 2011
, “Big Data Analytics
” TDWI Best Practices Report, Fourth Quarter
, 19
(4
), pp. 1
–34
.19.
Cao
, L.
, 2017
, “Data Science: A Comprehensive Overview
,” ACM Comput. Surv. (CSUR)
, 50
(3
), pp. 1
–42
. 20.
Waller
, M. A.
, and Fawcett
, S. E.
, 2013
, “Data Science, Predictive Analytics, and Big Data: A Revolution That Will Transform Supply Chain Design and Management
,” J. Bus. Logist.
, 34
(2
), pp. 77
–84
.21.
Munim
, Z. H.
, Dushenko
, M.
, Raaness
, H.
, and Westerberg
, T. V.
, 2023
, “The Impact of Big Data Analytics Capabilities on the Sustainability of Maritime Firms,” Data Analytics for Supply Chain Networks
, Niamat Ullah Ibne Hossain, ed., Springer
, New York
, pp. 227
–242
.22.
Munim
, Z. H.
, Dushenko
, M.
, Jimenez
, V. J.
, Shakil
, M. H.
, and Imset
, M.
, 2020
, “Big Data and Artificial Intelligence in the Maritime Industry: A Bibliometric Review and Future Research Directions
,” Mar. Policy Manage.
, 47
(5
), pp. 577
–597
. 23.
LaValle
, S.
, Lesser
, E.
, Shockley
, R.
, Hopkins
, M. S.
, and Kruschwitz
, N.
, 2011
, “Big Data, Analytics and the Path from Insights to Value
,” MIT Sloan Manage. Rev.
, 52
(2
), pp. 21
–32
.24.
Alharthi
, A.
, Krotov
, V.
, and Bowman
, M.
, 2017
, “Addressing Barriers to Big Data
,” Bus. Horiz.
, 60
(3
), pp. 285
–292
. 25.
Bui
, V. D.
, and Nguyen
, H. P.
, 2021
, “A Comprehensive Review on Big Data-Based Potential Applications in Marine Shipping Management
,” Int. J. Adv. Sci. Eng. Inf. Technol.
, 11
(3
), pp. 1067
–1077
. 26.
Zaman
, I.
, Pazouki
, K.
, Norman
, R.
, Younessi
, S.
, and Coleman
, S.
, 2017
, “Challenges and Opportunities of Big Data Analytics for Upcoming Regulations and Future Transformation of the Shipping Industry
,” Proc. Eng.
, 194
, pp. 537
–544
. 27.
Thollander
, P.
, Palm
, J.
, and Rohdin
, P.
, 2010
, Energy Efficiency
, Vol. 1
, IntechOpen
, pp. 49
–63
.28.
Dalkey
, N.
, and Helmer
, O.
, 1963
, “An Experimental Application of the Delphi Method to the Use of Experts
,” Manage. Sci.
, 9
(3
), pp. 458
–467
. 29.
Dalkey
, N. C.
, 1972
, “The Delphi Method: An Experimental Study of Group Opinion,” Studies in the Quality of Life: Delphi and Decision-Making
, N. C.
Dalkey
, D. L.
Rourke
, R.
Lewis
, and D.
Snyder
, eds., Lexington Books
, Lexington, MA
, pp. 13
–54
.30.
Rezaei
, J.
, 2015
, “Best-Worst Multi-criteria Decision-Making Method
,” Omega
, 53
, pp. 49
–57
. 31.
Liang
, F.
, Brunelli
, M.
, and Rezaei
, J.
, 2020
, “Consistency Issues in the Best Worst Method: Measurements and Thresholds
,” Omega
, 96
, p. 102175
. 32.
Jimenez
, L. M.
, Polo
, J. A.
, and Duarte
, N. A.
, 2019
, “Overview of Data Governance in Business Contexts
,” IOP Conf. Series: Mater. Sci. Eng.
, p. 012023
. 33.
Weber
, K.
, Otto
, B.
, and Österle
, H.
, 2009
, “One Size Does Not Fit All—A Contingency Approach to Data Governance
,” J. Data Inform. Qual.
, 1
(1
), pp. 1
–27
. 34.
Cariou
, P.
, Ferrari
, C.
, and Parola
, F.
, 2015
, “Strategies in Maritime and Port Logistics
,” Mar. Econ. Logist.
, 17
(1
), pp. 1
–8
. 35.
Nag
, R.
, Hambrick
, D. C.
, and Chen
, M.-J.
, 2007
, “What Is Strategic Management, Really? Inductive Derivation of a Consensus Definition of the Field
,” Strat. Manage. J.
, 28
(9
), pp. 935
–955
. 36.
Hariri
, R. H.
, Fredericks
, E. M.
, and Bowers
, K. M.
, 2019
, “Uncertainty in Big Data Analytics: Survey, Opportunities, and Challenges
,” J. Big Data
, 6
(1
), pp. 1
–16
. 37.
Perera
, L. P.
, and Mo
, B.
, 2017
, “Machine Intelligence Based Data Handling Framework for Ship Energy Efficiency
,” IEEE Trans. Vehic. Technol.
, 66
(10
), pp. 8659
–8666
. 
