Data-Driven at Sea: Forecasting and Revenue Management at Molslinjen

Pierre Pinson (), Mikkel Bjørn (), Simon Kristiansen (), Claus B. Nielsen (), Lasse Janerka (), Jesper Skovgaard () and Kristian Durhuus ()
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Pierre Pinson: Halfspace, 1306 Copenhagen, Denmark; and Dyson School of Design Engineering, Imperial College London, London SW7 2AZ, United Kingdom; and Department of Technology, Management and Economics, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
Mikkel Bjørn: Halfspace, 1306 Copenhagen, Denmark
Simon Kristiansen: Halfspace, 1306 Copenhagen, Denmark
Claus B. Nielsen: Halfspace, 1306 Copenhagen, Denmark
Lasse Janerka: Molslinjen, 8000 Aarhus, Denmark
Jesper Skovgaard: Molslinjen, 8000 Aarhus, Denmark
Kristian Durhuus: Molslinjen, 8000 Aarhus, Denmark

Interfaces, 2025, vol. 55, issue 1, 5-21

Abstract: Molslinjen, one of the world’s largest operators of fast-moving catamaran ferries, based in Denmark, adopted a focus on digitalization to profoundly change its operations and business practices. Molslinjen partnered with Halfspace, a data, analytics, and artificial intelligence (AI) company based in Copenhagen, Denmark, to support that transition. Halfspace and Molslinjen jointly developed and deployed a successful forecasting and revenue management toolbox for the data-driven operation of ferries in Denmark since 2020. This has resulted in $2.6–3.2 million yearly savings (and a total of $5 million savings as of December 2023), a significant reduction in the number of delayed departures and average delays, and a 3% reduction in fuel costs and emissions. This toolbox relies on some of the latest advances in machine learning for forecasting and in analytics approaches to revenue management. The potential for generalizing our toolbox to the global ferry industry is significant, with an impact on both revenues and environmental, societal, and governance criteria.

Keywords: ferry operations; demand forecasting; revenue management; machine learning; Franz Edelman award (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:inm:orinte:v:55:y:2025:i:1:p:5-21

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