An AI model optimizes utilization and detects discrepancies in real-time, automatically suggesting actions to improve efficiency and shorten delivery times. This makes the entire supply chain process faster and more cost-effective.
A leading publicly listed transport and logistics company faced a critical challenge: ship capacity utilization could only be predicted to a limited extent using existing methods.
Although historical booking data and past performance figures were available, these inputs were not sufficient to reliably capture short-term booking trends, seasonal fluctuations, or economic developments.
The result was operational uncertainty and missed revenue opportunities.
As part of an ada Future Lab (LINK) project, the company set a clear goal: to use AI to develop a forecasting model capable of providing more accurate capacity predictions and generating actionable recommendations for managing commercial measures.
This would deliver a forward-looking contribution to optimizing logistics processes and increasing efficiency in global maritime transport.
The project initially aimed to transform the company’s entire logistics operations with AI.
However, to achieve rapid, validated results, ideation workshops narrowed the focus to a single trade lane: Asia–Latin America (Asia–LATAM).
This route exhibited significant booking fluctuations, strong economic relevance, and clear potential for data-driven optimization—making it the ideal pilot project for validating the model.
The first step was a thorough analysis and documentation of existing trade management processes to evaluate their suitability for an AI-driven solution.
Harmonizing these processes was critical for building a reliable data foundation and improving internal collaboration.
In addition to historical booking data, the team integrated a variety of other influencing factors, including seasonal trends, inventory levels, economic developments, and external market data.
This integration enabled flexible, up-to-date, multidimensional capacity forecasts that go far beyond traditional single-source models.
Based on the structured processes and data landscape, the project team built a fully functional Minimum Viable Product (MVP).
The AI model accurately predicted whether a vessel would operate within a defined capacity range.
These actionable recommendations were delivered directly to trade management, where they informed day-to-day decision-making—a practical example of how AI can optimize logistics operations.
The prototype quickly produced concrete results.
Beyond operational improvements, the project also had a cultural impact.
Acceptance of data-driven decision-making increased noticeably, and the company took a tangible step forward in the digital transformation of logistics.
Close collaboration between data scientists, business units, and senior management proved crucial.
An agile project structure allowed for fast feedback loops, iterative improvements, and solution-oriented execution.
Additionally, an early and compelling presentation of the model’s potential helped secure internal support and sponsorship.
With the pilot phase completed, plans are already underway to further develop the model by:
The long-term objective is to embed the AI solution directly into day-to-day logistics operations, unlocking sustained efficiency gains.
This project demonstrates that AI in logistics is no longer a distant vision—it is already a strategic tool for capacity optimization, competitive advantage, and data-driven transformation across the entire value chain.
Volatile Lieferketten, schwankende Nachfrage, steigender Kostendruck – moderne Logistik steht unter enormem Handlungsdruck. Das ada Future Lab zeigt, wie Unternehmen in nur zwölf Wochen eine praxiserprobte KI-Lösung entwickeln, die Forecasts verbessert, Auslastung optimiert und operative Komplexität reduziert. Erfahren Sie, wie datenbasierte Entscheidungen Ihre Supply Chain resilienter, effizienter und zukunftsfähiger machen – von der Planung bis zur Umsetzung.
