GreenSteam CTO & co-founder Daniel Jacobsen on why data and machine learning are crucial tools for shipping companies

“We’ve noticed two major things in shipping: decision-making can often be a drawn-out process, and there has traditionally been a reluctance to embrace change. But, if companies are to survive, there needs to be a shift,” says GreenSteam CTO Daniel Jacobsen.

Daniel Jacobsen

30 Apr 2019

Technology is continually changing how we live. From smartphones to contactless card payments, cryptocurrencies to 3D printing, we have become accustomed to having our daily routines enhanced by innovations designed to make life simpler.

However, this culture of constant innovation and improvement is by no means limited to speeding up coffee payments or allowing us to access Twitter on the move; it is also central to the ever evolving business landscape.

And that includes the vast and varied world of shipping.

By using the right technologies at the right time, companies around the world are finding that they can make their day-to-day operations more efficient, more cost-effective, and more capable of tackling unforeseen changes and challenges.Quote-01-[Insert]I would go as far as to say that companies failing to make the most of technological advances will soon find themselves usurped. They will be overtaken by rival organizations more willing to invest in, develop, and embrace technology. 

That’s a main reason why my co-founders and I started GreenSteam. We understood the power of machine learning, and foreseeing the challenges of 2020, built Greensteam to be able to meet them and deal with them. 

But such a bold statement begs an obvious question: how exactly can shipping companies ensure they utilize technology—particularly machine learning—to ensure they succeed, and ultimately flourish, in a period of major industry transition?

A mentality shift in shipping

Shipping has, traditionally, been a conservative sector. Technological advances were often incremental, and evolution was slow. Many companies were reluctant to embrace wholescale or mass change, largely because the incentive wasn’t there. If your operations are profitable, then why take a risk on changing what isn’t broken?Quote-02-[Insert]In recent times, however, this mentality has started to alter, and that’s largely because shipping organizations have no option but to welcome change with open arms. Escalating fuel prices, increased levels of competition, as well as the global sulphur cap which comes into effect at the beginning of 2020, have forced shipping companies to modernize or die.Info-Box-Out-[Box-out]At GreenSteam, we’re all about enabling change for the better. We use data and machine learning technologies to discover where shipping companies are operating inefficiently, and then come up with solutions and tactics to cut costs and reduce unnecessary waste. Our way of working is extensive and thorough; we don’t just assess the big challenges, but also take time to observe and refine the little things. 

However, through this approach, we end up creating something of a paradox. As I stated, the goal of GreenSteam is to increase shipping efficiencies so as to minimize fuel waste, and ensure that fuel usage is reduced and operating costs are cut. But such efficiencies will, ultimately, lead to shipping becoming an even more popular means of transporting goods, subsequently increasing the use of maritime transportation.

As I said, this is only something of a paradox. Shipping is already the most fuel-efficient and cost-effective means of moving goods over long distances, so any shift towards shipping and away from its alternatives—namely road haulage, rail, and air transportation—can only be regarded as a good thing. As problems go, it’s a pretty nice one to have.

But how exactly does GreenSteam utilize data and advanced technology to enable shipping companies to become more efficient?

Putting trust in data

Data is everywhere. Companies across all industries and of all sizes know that when looking to streamline operations and increase efficiencies, accurately capturing and then utilizing data is of critical importance.

In the world of shipping, it’s no different.

At GreenSteam, we often talk about the importance of marginal gains. Remember when you were little, and your parents told you that if you looked after the pennies, the pounds would look after themselves? With marginal gains, there’s a similar sort of logic; by taking care of the small problems and ironing out tiny inefficiencies, you can realize significant benefits that will make a big difference.

The data capture journey

The key to attaining such gains is data. All organizations create masses of data, and so the first step is to figure out what is worth capturing, and what isn’t. This means having the right tools in place to gather the data in the first place, and to ensure it can be aggregated and harnessed.

You need to be able to understand what data will give you the information necessary to make the changes, tweaks and renovations that will help your vessels operate more effectively.Quote-03-[Insert]Once that’s done, it’s time to analyze and assess. What does the data actually mean, and how can you make use of it? Data is rarely simple to interpret, so this step can be a challenge, especially when considering that poor interpretation of data can lead to companies making decisions that are detrimental and incredibly costly. 

The next stage is to make use of the data you’ve interpreted. What changes can you make immediately that will lead to instantaneous results? Where should you focus your energies? Do you need to commit to a major financial outlay—new equipment or software, for example—and, if so, what impact will this have in both the short- and long-term?

Of course, this is all far easier said than done. Adequate data interpretation is no mean feat, especially if you have nothing to compare the data to. Without baseline statistics or figures, it can be incredibly difficult to understand exactly what even the most meticulously analyzed data really means.

Which leads us to machine learning.

By gathering data from numerous and various sources, more robust lessons can be learned, and therefore more accurate conclusions can be drawn. We aggregate data from all our clients—ensuring all the while that everything is confidential and secure—because this makes our machine learning solutions as valuable as possible to all shipping companies, no matter their size. 

The power of machine learning

Machine learning needs data. Just as humans require blood pumping around their bodies to stay alive, machine learning needs a continual stream of data to ensure it can unearth inefficiencies and discover suitable solutions.

As the name suggests, machine learning allows ships to become smarter. By recognizing patterns and trends based on past performance, it ensures that mistakes are not repeated, and instead enables advantageous, data-driven decisions to be made.

From trim optimization to predictive maintenance—where individual pieces of machinery are monitored to ensure they are repaired or replaced at the optimal time—machine learning has the power to fine-tune innumerable inefficiencies, ensuring that vessels are improved from bow to stern.

And, because machine learning processes become more accurate over time as increased levels of data are gathered and compared, it should be regarded as an invaluable long-term investment with the power to enhance operations year on year.

A necessary evolution

The combination of data and machine learning, when used in the correct way, can drastically improve how shipping companies operate. By being able to make the right predictions and decisions at the right time, operating and maintenance costs can be cut significantly, and efficiencies can be enhanced across the board.Quote-04-[Insert]Machine learning is a long-term commitment, and will only work optimally if the right data is collected, assessed, and successfully implemented. By working with GreenSteam, you can make sure the efficiency of your maritime operations is enhanced, while also ensuring that you are geared up to face any future logistical or strategic challenges that may arise.

Given that the 2020 sulphur cap regulations come into effect in less than a year, there has never been a more pertinent time to seize the plentiful opportunities offered by data and machine learning.New call-to-action


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