Industrial Internet Now

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

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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

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Via Manufacturing Business Technology


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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

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Interview W/ Alun Jones


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  • Jopie 23.10.2017 04:12

    Good article

  • Ravi. Adibhatla 03.09.2017 08:22

    The topic is spot on! Will spur many to continue with their creativity using experience as an asset and start companies. ‘Insight’ is the word to give value to experience.

  • K ABDUL HAKEEM 27.08.2017 10:45

    Good stuff

  • Shakoat Hossain 11.07.2017 21:38

    Glad read it. Sounds great to know that title with building an insight-driven business. Thanks for kind info. Let me check 31 best seo experts on social media…Keep it up!

  • ANTONIO ALVAREZ 19.05.2017 22:33


  • 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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Gartner identifies the Top 10 Internet of Things technologies for 2017 and 2018

In an article published by IOT Solutions World Congress, Nick Jones, Vice President and Analyst at Gartner, says that the IoT demands an extensive range of new technologies and skills that many organizations have yet to master. “A recurring theme in the IoT space is the immaturity of technologies and services and of the vendors providing them. Architecting for this immaturity and managing the risk it creates will be a key challenge for organizations exploiting the IoT. In many technology areas, lack of skills will also pose significant challenges.”

The technologies and principles of IoT will have a very broad impact on organizations, affecting business strategy, risk management and a wide range of technical areas such as architecture and network design. The top 10 IoT technologies for 2017 and 2018 range from IoT security to IoT standards and ecosystems.

Read more about the 10 technologies that will enable organizations to unlock the full potential of the IoT at

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Via IOT Solutions World Congress

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Managing change in the connected workplace

The move to the digital world has allowed us to create new value inside the workplace, but adopting the IoT on a wider scale poses a few challenges. Alexander Reay, Chief Digital Officer at Sodash and President of the Nordic IT Association, explores the role an organization’s structure and culture play in maximizing IoT’s potential for businesses. He also talks about the leadership issues that need to be addressed during this transformation.

The Internet of Things (IoT) is very transformative and is reshaping business models. This disruption entails a huge amount of internal change that needs to be addressed in an organization. Alexander Reay, Chief Digital Officer at Sodash and President of the Nordic IT Association, believes that the issue is connected to neither technology nor technology maturity. “It’s a leadership one. It’s about understanding the digital economy – or if you prefer, the platform economy – we’re moving into.”

This transformation through technology, he adds, is a cultural matter. “It’s the impact of how the business and the people in it work. When you’re moving systems into the cloud, the major issue is to actually lead or inspire people who are very much used to the traditional way of doing things to adopt this new method.”

Since digital transformation is something that effects every single part of a business, a leader can’t be expected to do this on his or her own and needs to invest into change agents.

Culture of change

“An organization needs to have a culture of proactive change and the individual spearheading it needs to have a completely systematic and holistic view,” continues Reay. “Leadership in larger enterprises, however, isn’t as agile and usually can’t cope with quick, radical change. By far, that is the biggest barrier in adopting new and better ways of doing things along with rigid old hat organizational structures and governance”

As companies are recast as digital enterprises, and organizations continue to adapt to the IoT and the new demands of managing the physical and the digital, this convergence mandates not only new skills but also different ways of working.

The role of chief digital officer (CDO) involves looking for business opportunities that have been enabled by the digital revolution. It also entails focusing on customers and how their needs might change because of technological developments. It is quite different to that of the chief information officer, whose job — though similarly complex — is more about following procedures and keeping a company’s IT systems running. By contrast, the digital role is to head the transformation.

“This is really a leadership understanding of the technology – how to use it, how to drive new value as result – not the technology itself. So forget thinking of IoT and digitalization as just new technologies.”

As Reay stresses, “This is really a leadership understanding of the technology – how to use it, how to drive new value as result – not the technology itself. So forget thinking of IoT and digitalization as just new technologies. For those companies that get it, digital represents an entirely new way of doing business, moving from using technology as a catalyst for efficiency or effectiveness into driving new value or even changes to entire business models, but most important, they are in the process of changing their corporate culture”.

Difference between network and community

To begin the work of digital delivery, the companies recruiting chief digital officers must break down the walls between the independent vertical structures in their organizations. Reay notes that since we’re used to really siloed innovation, “we’re just about starting to realize the potential of open innovation techniques. What we’re talking about is a completely seamless network.”

It’s one thing to have the infrastructure to create a network and fluidity – those are the technical aspects, according to Reay.

“The real value lies in using that network to create and foster a community or to look at it another way expedite a digital culture, and for that people need to be empowered. Being connected to somebody doesn’t mean that that individual is empowered to be able to add value or even inspired to do so, and that’s the difference between a network and a community, it’s about adoption rate – a network informs, where as a community acts. In a community, people are actively contributing and they’re empowered to do so. That’s why change right now is such a headache for large businesses: they are simply too siloed, lacking the ability to adopt new ways of working quickly enough.”

Security and privacy issues

Another barrier, not just in terms of operability or interoperability, involves security issues. Reay brings up privacy, which to him is an area that is very disruptive in itself. “We’ve got the EU coming out with changes and directives very frequently and cyber-attacks increasing in frequency. The move into IoT requires a completely new set of skills that are needed rather urgently, if you fulfill some of the data regulations,” he adds.

These directives, as well as the cyber security issues that result from having these systems over the internet, have yet to be fully understood because IoT is such a new area.

“Since the EU is putting in these directives, when these new things emerge, they do so within the context of a rigid government system that’s been used for eternity. The key in trying to find new ways of mitigating some of these risks is understanding what leadership roles are really needed.”

Reay observes that large organizations are now rolling out compliance managers across their business units. “This is simply not going to do,” he counters. “There needs to be a Chief Privacy Officer, someone who can run the show at a very single level inside the organization on a strategic level, we are not just talking about technology here. These individuals should be rising through the ranks from a legal perspective, and with heavy sanctions and penalties for privacy breaches, this role should under no circumstances be left to the CISO, CSO, CIO, or CDO’s responsibility”.

Collaboration between machine and human

With the usage or the ability to analyze such massive volumes of data, machine learning starts to play a role. Humans can’t analyze that amount of data, and to Reay, what’s interesting is the new value creation made possible by massive volumes of data collected from connected products and devices. This goes beyond efficiency, smart sensors or the ability to use the IoT to create more efficient productive manufacturing lines.

“For example, artificial intelligence (AI) could be used to process that data to provide insight, resulting in better informed rapid decision making. Doing so could organize a lot of these areas, from efficiency and uptime to the utilization of assets. The maintenance and management aspects are, right now, the areas where we’re going to see machine learning really utilized.”

Ultimately, Reay believes that everything comes down to a human-centric approach.

“What really stands out with the use of AI is that it makes us essentially more human. It’s the same as with digital transformations: it’s not the data we’re interested in. It’s how the data influences our decisions. The collaboration between machine and human is where the real innovations are going to start. The utilization of the interface between AI and humans is going to be where we will see how it is utilized to its full potential.”

Again, when we roll out the IoT, machine learning can be used to improve efficiency and uptime. But, as Reay concludes, “the real utilization is understanding how can we use machine learning to create a positive impact for humanity, the workplace and our customers.”

Alexander Reay works as a Chief Digital Officer at Sodash and is President of the Nordic IT Association

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Interview W/ Alexander Reay

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