Can digital ecosystems mitigate risks in sea transport operations? Estimating benefits for supply chain stakeholders

Decision-making supported by digital ecosystems has been increasingly studied during recent years, especially due to improved technical capabilities to collect, store, and analyze large amounts of data. The literature recognizes that these systems can reduce response time of managers and enhance a cost-efficient recovery of supply chains. However, there is a lack of methodological frameworks to evaluate the benefits of these platforms. In addition, there is still little understanding of the risks in ocean container transport and their implications for supply chains. This paper proposes and applies a mathematical model for evaluating the impacts of digital platforms, with a focus on solutions to mitigate risks in sea transport operations. The model is based on scenarios and decision tree models to evaluate the impacts of a supply chain digital ecosystem on full containers shipped from Asia to Europe implemented by four companies. Results show monetary savings per scenario in the range from €3448 to €79,242. The most significant savings are expected on unplanned transshipments, cargo damage, export inspections, container detention, and container release.

• Record URL:
  https://doi.org/10.1057/s41278-020-00163-6
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/14792931
• Supplemental Notes:
  • Copyright © 2021 Macmillan Publishers Ltd.
• Authors:
  • Urciuoli, Luca
  • Hintsa, Juha
• Publication Date: 2019-9

Language

• English

Media Info

• Media Type: Web
• Features: Appendices; Figures; References; Tables;
• Pagination: pp 237-267
• Serial:
  • Maritime Economics & Logistics
  • Volume: 23
  • Issue Number: 2
  • Publisher: Palgrave Macmillan
  • ISSN: 1479-2931
  • EISSN: 1479-294X
  • Serial URL: https://link.springer.com/journal/41278

Subject/Index Terms

• TRT Terms: Containerization; Data analysis; Data collection; Data sharing; Decision trees; Mathematical models; Ocean shipping; Risk management; Supply chain management
• Subject Areas: Data and Information Technology; Marine Transportation; Planning and Forecasting;

Filing Info

• Accession Number: 01777068
• Record Type: Publication
• Files: TRIS
• Created Date: Jul 23 2021 3:23PM