photo: Nexxiot, RAILTARGET/"Our purpose is to remove uncertainty from the supply chain for an easier global transport system," says Ákos Maróy, CDO at Nexxiot
RAILTARGET presents an exclusive interview with Ákos Maróy, the new CDO at Nexxiot, who discusses the company's Asset Intelligence technology and its role in improving logistics processes. We talked about the potential of AI and machine learning in the logistics industry, as well as the importance of data quality and ethical implications in AI strategy development. How do past experiences in technology, media, aviation, and biotechnology contribute to Ákos Maróy's success in his current role?
As a Chief Data Officer, how do you plan to leverage the data gathered by Nexxiot's Asset Intelligence technology to improve logistics processes and remove uncertainty around freight transportation?
Everyone is talking about Big Data, AI, and Machine Learning algorithms. The big trend in the digital supply chain space is ‘connecting the physical to the digital’. What does that really mean? Data aggregators are gathering data from multiple sources which does provide some value as a ‘proxy’ for transparency, but we get our data live and direct from the transport asset and cargo via our state-of-the-art hardware. We are combining the high-quality, precision sensor data we collect, with other data sources to drive actionable insights and process automation. This creates opportunities at a tactical (operational) level and strategic (planning) level – enabling us to provide unique, precision services for a wide range of stakeholders, clients, and partners across the logistics space.
Nexxiot’s Asset Intelligence technology is widely used by transport operators, lessors, carriers and cargo owners, for whom it is already improving their logistics processes. The visibility enabled and insights provided are used to optimize asset usage, removed inefficiencies, prevent losses and drive-up quality and accountability. We are moving towards KYC where Nexxiot will play a key role in monitoring global trade compliance or ‘Know Your Cargo’ as we are calling it. Nexxiot will continue to drive the improvement of 360-degree visibility, not only in tracking the assets but crucially the cargo itself. This is done by gathering data from an extended sensor hardware portfolio and by integrating even more external sources via API.
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How do your past experiences in technology, media, aviation, and biotechnology contribute to being successful in your new role at Nexxiot?
An interdisciplinary approach has always been a powerful advantage in tackling complex challenges. By combining knowledge and processes from multiple disciplines from my past, I can provide value to the team by challenging the view on problems and framing them in a different light.
Nexxiot is already by nature a highly interdisciplinary environment, with people from a wide range of science, technology, and business backgrounds. Picking the best practices and methodologies from a wide range of domains allows us, as a team, to build on learned experiences for more rapid iteration cycles and encourage the cross-pollination of approaches and techniques. This results in speed, agility and directness in our discussions and actions. For example, one person in our data science team was previously looking for evidence of the Higgs-Boson in the data coming from the LHC at CERN, Geneva, he is now working on locating railcars with pinpoint accuracy.
In my case, by applying bio-design-based methodologies, I can leverage concepts developed through the eons of evolutionary selection in the biological ecosystem, as a systems approach to navigate towards a circular & sustainable economy.
With the rise of Machine Learning and AI, what opportunities do you see for transforming logistics processes and delivering new services to clients in the rail, maritime, and diverse logistics sectors?
AI is accelerating and transforming entire industries. By combining the best of human & artificial intelligence, we can discover much more about our business and the surrounding ecosystem. We can create vital new insights that were previously out of reach. Leveraging this knowledge to enable and automate decision-making generates new value, reduces waste, and increases trust and efficiency between stakeholder groups and operating partners. AI enables us to create new approaches and interfaces for human-computer interaction and challenge the very concept of what ‘digitizing an industry’ means. We no longer have to tune everything to match the machine-like mechanisms of top-down engineered systems, but we can ensure emergent (bottom-up) collaboration from both directions as humans and machines ‘meet halfway’.
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Could you tell us about a specific project you have worked on in the past that you believe will be relevant to your work at Nexxiot?
AI-based flight autonomy and augmented reality (AR) in aviation are things I’ve worked on in the past. The experience I had in the highly disruptive and fast-paced aviation industry gave me a ‘can do’ mindset for disrupting established, mission-critical, safety-focused industries, and working at high speed of course. This is extremely useful in an environment like Nexxiot, where we are challenging the status quo every day.
I worked for a range of companies, from some of the largest in the world to the smaller newcomers. I learned that you can be a transformative force in a small agile company even more effectively than in many large industrial environments. It’s important to have the right intrinsic culture to innovate fast and pivot freely to achieve the goal. Working for Google & YouTube gave me a high level of confidence and knowledge about building scalable online architectures that will accelerate Nexxiot’s solution offering for the supply chain.
With your extensive experience in deploying AI in various fields, what do you think are the most important factors to consider when developing and implementing a successful AI strategy for stakeholders in the supply chain industry?
As with all AI deployments, the quality & quantity of the data is the foundation. In our case, the data is coming from our very own IoT devices. Having vertical integration in this space gives us great leverage as we can directly guarantee the quality, frequency, and nature of this data. The resulting AI models must be vetted and verified to ensure that they do indeed perform the duties expected of them. Being a responsible innovator in a safety and mission-critical environment also requires us to look at not just quality and validity but also the ethical implications of our AI solutions. Our purpose is to remove uncertainty from the supply chain. This brings business advantages and helps us to build an easier, safer, and cleaner global transport system. AI needs strong values to ensure that it generates real value for humanity in the short term and over the long term.
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