Embracing the data revolution in shipping : A vision from Anglo-Eastern

Swapnodeep Mondal, group director of operations reflects on the true potential of AI that goes beyond automation and creates new ways of doing business.

In the dynamic world of shipping, integrating data analytics, Machine Learning (ML) and Artificial Intelligence (AI) represents an upgrade and a fundamental transformation in how we operate and manage complex systems. At Anglo-Eastern, we are working towards a new era where data-driven decisions can be used more effectively to optimise operations. 

Traditionally, shipping companies have operated within rigid, siloed structures that inhibit growth and slow responsiveness. At Anglo-Eastern, we have been steadfastly working on using our systems such as FOS, JiBe, NetSuite, etc., thus moving towards a horizontally connected organisational model designed to leverage an integrated data foundation. This shift allows for the rapid deployment of analytical data, facilitating exponential growth in scale, scope, and learning capabilities. 

The core of our approach is to industrialise our handling of data and analytics. Rather than merely tweaking existing processes for efficiency, we are re-architecting them to be inherently digital and, in the future, ML/AI-driven. This transformation means that our processes are more scalable and capable of connecting seamlessly with a myriad of digitised businesses, creating powerful opportunities for learning and improvement.  

The deployment of data analytics at Anglo-Eastern is changing every facet of our operations. By digitising critical processes such as inspections, PMS, procurement, voyage optimisation, etc., we eliminate traditional bottlenecks, enabling unprecedented scalability and efficiency. Our analytics convert vast amounts of internal and external data into actionable insights, guiding or even automating operational decisions.  

We are working towards a model where data knows no functional boundaries. This environment demands and fosters close multifunctional collaboration, improving outcomes while mitigating risks. With centralised data, our experimentation capability is decentralised, allowing team members across the organisation to test hypotheses and implement changes rapidly, driving continuous improvement and innovation. 

In this new paradigm, managers at Anglo-Eastern are not just administrators; they are designers, shaping and refining the digital systems that sense and respond to customer needs. They are also innovators, envisioning how digital systems will evolve and integrators, connecting disparate systems and uncovering new ways to meet customer demands. For instance, we are working on: 

  • ML to Assist with Root Cause Analysis 

We are working on using the capabilities of GPT models to carry out Root Cause Analysis (RCA). By training these models on our datasets comprised of industry-approved RCA samples and years of our own archival data, we are trying to streamline the RCA process, allowing our teams more time to focus on implementing effective solutions.  

  • Large Language Models (LLMs) for extracting data from our Safety Management Systems (SMS) 

We are working on using Large Language Models (LLMs) to look into our extensive SMS and data libraries accumulated over the years to efficiently extract actionable insights from the documentation without the labour-intensive process of manually sifting through volumes of records.  

  • Developing a Vessel Risk Matrix 

We are working on a targeted approach to maritime risk management by developing a comprehensive “Vessel Risk Assessment Matrix.” It will utilise datasets from Planned Maintenance Systems (PMS), Flag State records, Port State Control (PSC) inspections, Vetting, Procurement, Crewing etc., all consolidated within our ERP system, JiBE. This innovative matrix will prioritise risks based on their impact and likelihood, enabling us to strategically focus our resources on the most significant risks and vessels.  

The advent of AI is rapidly making traditional, vertical organisational structures obsolete, shifting competitive advantages towards capabilities in data sourcing, processing, and algorithm development. At Anglo-Eastern, we are committed to adapting to the change and implementing operating models that make automated decisions, ensuring our continued growth and competitiveness in a rapidly evolving industry.  

Our vision is clear: To not only be at the cutting edge of this development in our industry, but also make real strides forward to improving quality, lowering costs-to-serve, and creating real value for our clients/service offerings by using AI/ML. 

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