Industrial Analytics Blogs

We all Need a Pair of Blue Jeans and a Shovel

Who Actually Struck it Rich?

The most memorable stories from the California Gold Rush focus on the lucky souls who struck it rich. Perhaps just as interesting were those who supplied the tools that the miners needed to be successful -- the picks, shovels, and pans. Some of these suppliers were amazingly innovative and entrepreneurial: Levi Strauss (along with partner Jacob Davis who patented the copper rivet reinforcement in the heavy-duty pants) famously supplied denim jeans. More than 150 years later, Strauss’ company is still successfully supplying jeans, and more, to people around the world.

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Production, People, and Partners: A Review of 2017

2017 is the year we’ll remember as the “end of the beginning” for Industrial Digital Transformation. It was the year when the definition of “digital transformation” itself was argued about, accused of being a “buzz word,” and featured as the headline for many conference presentations, papers and articles.  The market is finally, albeit slowly, shifting from endless proofs of concepts of cool new technologies and toward a greater focus on true digital transformation solving real business problems.

We’re beginning to see production grade projects being rolled out in industrial companies, everything from individual site based deployments to enterprise projects federating data from dozens of sites and systems across an industrial fleet.  At Element Analytics we’re hopeful that this is a harbinger of companies looking to truly transform their operations to achieve important business outcomes.

For us, 2017 can be summed up in 3 words: Production, People, and Partners.

Architecture of a Digital Twin Service

In part one of this blog series, Andy defined digital twins and their importance for asset operators, and in part two, Sameer gave an overview of the path of digital twin evolution. In the final post of this three part series, I'm going to discuss the process for building and managing Digital Twins (DT’s) throughout their evolution in order for them to provide value to asset operators.  I’ll touch on the four main stages for building and managing DT’s: Data Collection, Data Pipelines, Data Egress, and Data Integrity.

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Why Asset Operators Should Have Their Own Digital Twins

Digital Twins are a hot topic, landing on Tech analyst Gartner’s famed Hype Cycle and named a Top Ten Strategic Technology Trend for 2017.  Not a new idea (credit to Dr. Michael Grieves, 2002, Univ Michigan), Digital Twins are now at the forefront of Industrial /Internet of Things zeitgeist because they are essential to unlocking I/IoT value by enabling analysis, simulation and control of physical things and systems.

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Using Asset Data Models to Empower Your Industrial Organization

When I speak with CIOs and their staff, the topic of digital transformation always leads to a discussion around how time-series data is the starting point, but it’s difficult to work with and organize in a way that represents how equipment and assets exist in the physical world.

Industrial companies have begun to address the problem by adopting Asset Data Models, which represent the physical structures and relationships of industrial equipment and processes. Asset Data Models are crucial for equipment benchmarking, cross-site comparisons, and underpin every kind of analytics. 


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