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How to Hack a Port

How to develop digital solutions in a port environment, where things happen fast, and many actors must operate together seamlessly? This is the challenge Konecranes presented to developers at Maritime Hack organized on November 26 and 27 in Helsinki.

The third hackathon organized by Konecranes took hackers into a completely new environment – into a port, where the level of automation can be significantly higher than in a factory environment.

Maritime Hack was special also in another way –the event included three separate challenges and was organized by Industryhack in cooperation with Rolls-Royce, the City of Helsinki and Konecranes. The whole event gathered dozens of developers and designers to Arctech Shipyard in Helsinki.

“The whole maritime industry needs a lot of disruption, and there is a great demand for digitalization. It is a good example of an industry with many different players, regulations, and rules. The different players need to find new ways to work together to fully benefit from digitalization,” said Industryhack CEO Petri Vilén, who will be participating in the World Economic Forum in Davos in 2017.

The attending teams were chosen from a great number of applicants and granted access to the application programming interface (API) provided by the event organizers.

This time the teams had a unique opportunity to visit the Port of Helsinki on the week before the actual event. The visit included info sessions by organizing companies and a guided tour to the different areas of the port – the pier, the yard, and to the gate.

Full automation is the big dream – many things can be automated already today

The teams were presented with three different challenges that all concerned the use of digital information flow in port operations: vessel unloading, yard operations, as well as in-and-out land traffic.

“There is still a lot of manual intervention in container port operations. With automated information flow we can reduce the amount of manual work and also the risk of information being faulty. Full automation is the big dream, but there is a lot of smaller solutions that can be applied to existing operations already today,” explained Konecranes Sales Manager Ville Hoppu.

The teams received specialist coaching from a number of Industryhack coaches. The coaching was especially useful in developing the creative ideas into concrete solutions that create value for the customer. As the second day turned into night, many teams still continued to develop their ideas.

Winner team creates an application to optimize truck traffic

On the final day, each team got to present their solutions to the jury. At the demo session, the teams presented solutions to challenges ranging from weather conditions to container yard operations of small ports.

“The whole maritime industry needs a lot of disruption, and there is a great demand for digitalization. It is a good example of an industry with many different players, regulations, and rules” -Petri Vilén, Industryhack

Team Nortal developed the winning solution. The team presented a mobile application for truck drivers that strives to optimize time spent on cargo pickup. Currently there is hardly any communication between the port and the truck driver. This is something the winning team wants to change.

The winning application is set to optimize cargo pickup. The demo included a calendar view and a function that allows the truck driver to schedule an optimal time for cargo pickup. The application is set to improve efficiency and waiting time as well as cut costs. Reducing waiting times by one per cent can result in significant savings as cargo volumes are high.

Juha Pankakoski, Konecranes Chief Digital Officer, was surprised to see that all teams had focused on one theme – improving the information flow related to material flow at the port.

Pankakoski explained that the winning team was able to develop a very practical and well-functioning solution to a very practical problem.

“The winning solution has good applicability, and it can be easily deployed and distributed in actual working environment. It can also be easily developed further,” Pankakoski said.

The key to victory was the insight to tackle a very specific problem and develop a very concrete solution.

“The idea was quite simple, but we spent a lot of time calculating the business case behind it. We wanted to make sure that the solution actually creates value for Konecranes,” the winning team explained.

Tricky challenge, great ideas

Maritime Hack was different from the two previous hackathons organized by Konecranes because it took place in a complex port environment with a lot of different actors, regulations and rules. The two previous hackathons took place in a factory environment.

“Ports are complicated logistics hubs, where many actors have to work together and communicate with each other. Ports are also very controlled environments where functions take place on very designated areas. Different equipment and systems need to work together seamlessly. The environment is demanding and hectic,” Juha Pankakoski explained.

Pankakoski gave credit to the teams for finding novel solutions despite the tricky challenge.

“We noticed that this is by far a more challenging area than that of the two previous hackathons. All the more, I’m happy to see that the teams were able to come up with innovative ideas.”

To see all the videos from the three-day event, go to Konecranes’ YouTube channel http://youtube.com/user/liftingbusinesses

Find out more at http://konecranes.com/hackathon

Image credit: Daniel Taipale / Industryhack

by Industrial Internet Now

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The business viewpoint of securing the Industrial Internet

According to Daniela Previtali, Global Marketing Director, WIBU-SYSTEMS AG, the tone of the many discussions about the Industrial Internet of Things (IIoT) varies widely from one viewpoint of unbridled optimism about the seemingly endless possibilities to another filled with apocalyptic doom and gloom about the safety of the planet and everyone on it. “Two extreme views perhaps, but there is no denying the fact that security is an issue that needs to be carefully addressed before any of those endless possibilities can become a reality in the industrial internet,” she writes in Industrial Internet Consortium (IIC).

Though there is a growing awareness and concern for IIoT security, the ability to address these concerns with step-by-step roadmap has not been well coordinated until now. The framework and detailed approach will be published in the coming months in the Industrial Internet Consortium’s Industrial Internet Security Framework Technical Report (IISF).

Read more about the IISF document and its accompanying white paper here:

http://blog.iiconsortium.org/2016/09/the-business-viewpoint-of-securing-the-industrial-internet.html

Image credit: Konstantin Yolshin / Shutterstock.com

Via Industrial Internet Consortium

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Predictive Maintenance Part 1 of 5: Predictive maintenance in the automotive industry

Predictive maintenance is a hot buzzword in the automotive industry, but what does it take to implement it and what are its benefits? Ken Elliott, Global Director of Analytics within Enterprise Services at Hewlett Packard Enterprise, explores these questions on the Business Value Exchange blog.

“Predictive maintenance is about finding the sweet spot that lets you get the most life out of your equipment while minimizing the risk of failure. It involves gathering large quantities of data – such as maintenance records and data from sensors on the equipment – analyzing the data, and creating a predictive model to determine the optimal time for maintenance tasks to be performed on each individual piece of equipment,” he writes.

Read the first article of the blog series at http://businessvalueexchange.com/blog/2016/06/29/predictive-maintenance-part-1-5-predictive-maintenance-automotive-industry/

Image credit: gyn9037 / Shutterstock.com

Via Business Value Exchange

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IoT: Meeting manufacturing’s next-generation challenges and opportunities

According to Rajaram Radhakrishnan, Vice President and Head of Manufacturing and Logistics, and Prasad Satyavolu, Head of Innovation for Manufacturing and Logistics, both at Cognizant, meeting consumer demands for personalization, increasing productivity despite the skills shortage and generating new revenue opportunities are all major strategic issues facing manufacturers.

In an article posted on Manufacturing Business Technology, Radhakrishnan and Satyavolu list benchmarks such as Advanced Predictive Analysis and Integration of Semantic Data, among others, to see to it that their IoT implementations provide the data and business intelligence required to succeed in today’s business environment.

Read more about how IoT can equip manufacturers with higher flexibility to respond to changing market dynamics at  http://www.mbtmag.com/article/2016/09/iot-meeting-manufacturings-next-generation-challenges-and-opportunities

Image credit: Nikkolia / Shutterstock.com

Via Manufacturing Business Technology

2 Comments

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  • Prasad Reddy Saragada H V D 06.12.2016 08:59

    Want to know more about IoT for energy meters property management and tax payments maintenance of Street lights

  • Prasad Reddy Saragada H V D 06.12.2016 08:57

    Want to know more about IoT for energy meters, property management and tax payments, maintenance of Street lights

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Building an insight-driven business

Having huge amounts of information is one thing. Harnessing data to create new business models is another. Alun Jones, Data Scientist at Konecranes, talks about utilizing machine learning, understanding security issues and improving operational efficiency as steps in building an insight-driven business.

Technology evolves rather quickly, while products inevitably do not. There is a challenge to figuring out how to make certain that older pieces of equipment talk to newer ones.

The Internet of Things (IoT) is relatively new. On the technical side, a lack of standards and protocols persists mainly because of its nature as a converging market place made up of competing players who believe that their individual technologies should be the standard.

Then there is what can be viewed as vendor discrepancy, or where a vendor adds something to a standard because they see it as the way forward. There may be little cause for concern about compatibility when using a manufacturer’s proprietary equipment. Keeping up with updates, however, can prove to be a challenge when combining equipment from different manufacturers. Cultural issues surround the ownership and usage of data, which are internal to organizations.

Utilizing machine learning

Machine learning is really where the benefit of having huge amounts of data comes from. What machine learning does is that it can very quickly go through a very large data set and pick up salient points which you then have to verify in real terms. Using algorithms that learn from data in an iterative manner, machine learning allows computers to detect hidden insights without being explicitly programmed where to search. Machine learning methods drive much of modern data analysis across fields such as engineering and the sciences, and commercial applications.

“Machine learning methods drive much of modern data analysis across fields such as engineering and the sciences, and commercial applications.”

Machine learning will tell you that there is a correlation between this and that, which then implies that if this changes in the future then something else is going to happen. I believe that this has to be verified, always. We have machine learning at a very high level that then picks out the relevant patterns in the data which we can then validate against other sources as well.

While machine learning is already an incredibly powerful tool that can solve difficult classification problems, certain points must still be kept in mind. Increasingly, the role of human input in these automated business processes will involve overseeing and tweaking the machine learning algorithms. After all, algorithms are only as smart as the intelligence put into them. This is where the potential for misunderstanding can arise.

Machine learning depends on small “errors” being made by the machine and has to be done and redone over and over.  A data-driven hypothesis is first derived before it is tested against new data. When the machine hypothesis is found to be incorrect, the machine then refines the algorithm or hypothesis to suit both the new and old data. The process is an ongoing one.

Don’t worry about security

First, don’t be overly anxious about security. Assume security is going to work because there are professionals who will sort matters out, so don’t allow that be a barrier to doing something.

Second, start small. Pilot – and test and test and test. It would also be wise to learn from other people’s experiences. It can become a bit difficult if you pin your hopes on a form of technology and invest in it, only for it not to work. Try and be as flexible as you possibly can. A particular type of technology may be widely used today, but it might change rapidly. As big data introduces a new level of integration complexity, integration technologies then require a common platform that supports data quality and profiling.

Improving operational efficiency

The next thing, which is the crucial part, is harnessing all this data and coming up with new business models. For instance, the steel industry in the UK is going through a very bad patch. Steel plants have had to close in England, enabling former employees to sell their expertise within the industry. The knowledge that these individuals have, as well as the data they have access to, can be used to improve the steelmaking process and optimize operations.

Once operational efficiency has been attained, the next move would be to think about what else all this data allows you to do.

Another way that data can potentially improve the service-based economy is by making manufacturing more flexible and more small-scale. There are opportunities for small-scale manufacturing using Industry 4.0 or IoT in general. Rather than having to build 10,000 of something, for example, you can build 10. Or you can then have a tailored experience.

Generating value from Big Data, and using it as a key component in a business’s growth strategy, is a matter of connecting data to insights to action in a quick, repeatable way. There are more options we haven’t even thought about yet.

Alun Jones works as a Data Scientist at Konecranes

Image credit: ImageFlow / Shutterstock.com

Interview W/ Alun Jones

2 Comments

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  • ANTONIO ALVAREZ 19.05.2017 22:33

    Ok.

  • Eliahu Gal-Or 13.11.2016 08:14

    Very thankful to whomever twitted about you and reached me; I really appreciate your site.

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