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George Marinakis: Data Analytics is giving rise to a new economy. How is it re-shaping the shipping industry?

17 Ιανουαρίου 2019.

marinakisgeorge1George Marinakis

Managing Director SetelHellas

Goes without saying that the data are becoming the new raw material for all business, from the small to the enterprise. Today, flows of digital information have created new operation models, new collaborations, new possibilities and—of course—new business routes.

However, although data will play an important role in driving sustainable business growth, don’t forget that in its raw form is just bits and bytes. Like the gold, it must be extracted first and then to processed further in order to deliver pure value. This may require a combination of processes, technology and tools to be performed on the ore. In the same way- the data, with the right analysis process, technology and extraction tools, can become the “gold” of the new digital economy delivering precious value.

Well- as the data science continue its growth, the digitalization is changing the way our industry address on going management challenges and improve efficiencies. For example, ship owners and operators initially used the data set they collect from their vessels to a limited extent -primarily to planning the main business resources and control the status of business commitments- and, they often didn’t know how to obtain this technology to drive insights in real time and improve their ability to sense and respond. But now-following the progression of analytics, they have discovered that the vessel-data can be turned into any number of incredible analytics services able to bridge the gap between digital information and the reality of practical decision making.

The evolution of data analytics is ranging from descriptive to diagnostic and culminating with the predictive and prescriptive analytics, pairing each stage with a question to be answered: what happened, why did it happen, when is possible to happen again and how I can prevent it to happen again?

For the first time in information technology history, it’s possible for computer systems to learn from experience and penetrate the complexity of digital information to identify associations and behaviours. “The rules are changing, re-shaping the market we serve …” Mr. Marinakis Said.

Each year, several types of maritime incidents take place around the world because of mission critical equipment anomalies. The anomalies of these critical systemsare the very risk that must be mitigated if not eliminated, before they become disruptive to operations. As with all business practices, managing vessel critical equipment requires accurate data and transparent process to ensure that operational risk reduction is predictively and prescriptively pursued in addition to all other pertinent business objectives.

At this point- I would like to encourage you to imagine a world in which:

Υour vessel's systems or piece of equipment anomalies and unexpected behaviours can be detected if not predicted well ahead of time, based on identified patterns derived from sensor data!

•The anomalies or unexpected behaviours detection enables you through data-driven insights to mitigate failures in critical equipment or eliminate these, so that unwanted impact on safety, regulatory compliance, cost, or operational throughput can be prevented!

•Any identified systems or sub-systems anomalies can be targeted and shown in a mimic diagram, displaying exactly which component in a specific location is damaged through a pictorial view, ensuring the correct and reliable identification in order to minimize disruption in vessel operations and maintenance!

Yes, it is no longer science fiction! Due to the growth in Big Data, the expansion of the internet of things (IoT) and the adequate processing power, machines learn by studying data to detect previously unknown patterns and generate remarkably accurate predictions!

But, the idea the machine could be autonomously creative seems far-fetched. The machine will not spontaneously come up with new ideas or hypotheses from data not in evidence.

Remember: the outcome of a machine learning algorithm is entirely dependent on the data quality. Inaccurate data, inaccurate results.

The problem is that not all data is always accurate data. Sometimes, data is noisy data, out of bound or out of range data, stuck or incoherent data and requires re-engineering. In many cases, data acquired on board the vessel suffer from discrepancies that if not addressed timely and effectively, corrupt the outcome and thus lead to misjudgement and -more important, wrong decisions!

So, the breakthrough innovation could not have come at a better time!

Together with its partners, SetelHellas is seizing the rise of Machine Learning (ML)- a branch of Artificial Intelligence (AI),to forge the key for a sustainable business environment in which anyone in our industry could extract “gold-value” from trusted data, creating stronger control over their vessel’s operation, maintenance and equipment replacement costs.

Our solution is designed to support the entire data life cycle in one platform. With our guidance, our clients can integrate advance analytics into their organization strategy – and apply practical, real-word scenarios, incorporated their business knowledge and their business practices from day one.

All these things mean for our SeeMBox-V© clients that it's possible to quickly and automatically use trusted models, that can analyse the growing volumes and varieties of carefully considered vessel-data,in order to deliver faster and more accurate precisions based on their business goals with confidence!



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185 37 Piraeus, Greece

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