cancel
Showing results for 
Search instead for 
Did you mean: 

Improve Big Data analytics to improve product performance intelligence

Siemens Experimenter Siemens Experimenter
Siemens Experimenter

Throughout history, humans have built tools to observe processes and to find context and meaning. Consider Stonehenge. 


Archaeologists believe Stonehenge was built between 3,000 BC and 2,000 BC. Some popular theories identify Stonehenge as an ancient observatory to mark midsummer [1]. Recent theories suggest it was used year-round and for predicting solar and lunar eclipses.


Stonehenge provides a good metaphor for the state of business intelligence (BI) today in many companies. These companies have primitive, obsolete tools few people understand how to use, and there’s a central place to go to seek information rather than being able to independently find it.


Companies that still rely on standard reports, spreadsheets and intuition live in a place like Stonehenge. Maybe that’s where their old, failed internal IT projects also end up going to die.


Unless you’ve been living under one of the rocks at Stonehenge, you know that Big Data and the Internet of Things (IoT) are hot topics. You’ll also hear about the Industrial Internet of Things (IIoT) where machines in a factory talk to each other. And if IoT wasn’t broad enough, we now have the Internet of Everything (IoE).


The Harvard Business Review has written cover stories on these trends that accurately capture the challenges and opportunities Big Data and IoE present to companies. How do you get control over Big Data in business? And how will smart, connected products transform your business?


The answers lie in the latest industrial revolution. Every industrial revolution has driven an increase in complexity and an increase in the amount of knowledge needed. From steam-powered machines in the first industrial revolution [2] into the mass production, division of labor and electrification during the second industrial revolution [3], complexity increased, and so did the skills the workers needed.

 

Siemens PLM Big Data analytics 1

With the third industrial revolution came electronics and the introduction of IT and automation. The fourth industrial revolution is here, and we are seeing advanced manufacturing become even more complex as software and physical systems merge along with the real and virtual worlds of design and production. Through digitalization and this fourth industrial revolution, or Industry 4.0, there is an explosion of data that is creating new opportunities for Big Data analytics to make a meaningful impact.


We get this. We understand this. Siemens has been living this first-hand since its founding a century and a half ago. We are a thought leader in Industry 4.0 and advanced manufacturing. World leaders come to us learn about manufacturing’s future. Angela Merkel, Chancellor of Germany, saw this first-hand when she visited the Siemens Amberg facility in early 2015.

 

Siemens PLM Big Data analytics 2

This concludes part one of our series to improve Big Data analytics. In part two, Bill Boswell discusses how Big Data analytics gives companies the power to harness the data explosion from the Internet of Things and the Siemens PLM Big Data analytics solution.

 

[1] http://www.english-heritage.org.uk/visit/places/stonehenge/history/significance/ 

[2] http://www.telegraph.co.uk/news/science/science-news/4750891/The-power-behind-the-Industrial-Revolut... 

[3] http://meta.spcollege.edu/index.php/modern-technologies-the-second-industrial-revolution

 

Tell us: How do you think Big Data analytics would improve your business? 

 

About the author
Bill Boswell is senior director of cloud services marketing and business strategy for Siemens PLM Software, focusing on delivering PLM solutions in the cloud and the Omneo big-data analytics software as a service solution. From 2011 to 2015, Boswell was senior director of Partner Strategy. He directed go-to-market strategy for Siemens PLM Software global consulting and systems integration partners, more than 500 software and technology partners and thousands of GO PLM global academic partners. From 2004 to 2011, he led worldwide product marketing for the Teamcenter product line, which became the world's most widely-used PLM portfolio. Prior to joining Siemens, he was chief technology officer and vice president of Solutions Development and Delivery for E-Markets, Inc.

Comments
Siemens Experimenter

Thanks for introducing this topic Bill!  I agree that Machine Learning algorithms hold the promise to provide so many useful answers and insights - as long as we can FEED them a lot of big data from which to learn from.  One aspect of this worth exploring is the idea of making product engineering simulation models and data part of the equation of what is feeding into a big huge Hadoop-ian database.  Much in the same way people are already talking about for condition monitoring data & ioT-sourced data. Why? I think that validated engineering simulation models can create data that is richer, higher resolution, and smarter. 

 

By fully connecting engineering simulation models and data (I'll use the word Digital Twin) - we can pressurize the data stream like a pump - 'geysering' it into a Hadoop-ian database, allowing Big Data to become even bigger, smarter, and at a faster rate than it could have otherwise.  This, in turn - could reduce the time it takes for Machine Learning algorithms to become properly fed and generate the Analytics and insights that will transform industry.

 

okay inhale. exhale.

 

THere's a lot of stuff 'out there' - but here is the C-SPAN BookTV episode that I stumbled upon that helped it all click for me.  It opened my eyes as much as anything on the power and possibility of machine learning.

 

http://www.c-span.org/video/?328407-1/book-discussion-master-algorithm   

Siemens Experimenter

 I think #JKRA has really hit on something here. Please stay tuned to the Digital Transformations blog as Jan Leuridan will addressing this very topic this week in his Predictive Engineering Analytics blog post.