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Digital Thread Resources
May 29, 2015
When Siemens snapped up Camstar last year to round out its Product Lifecycle Management (PLM) business, some analysts played down the manufacturing execution (MES) systems Camstar was best known for, instead focusing on a more obscure offering that provided Siemens PLM Software with an entrée into cloud computing and big data analytics.
At the time, Gartner, in particular, called the Omneo quality management platform “the most valuable aspect” of the Camstar acquisition, providing Siemens with the ability to drive a “feedback loop from customer usage aftermarket services into product and process design for end-to-end product quality management.” Omneo, which launched in April 2014, was still in its infancy at the time of the Siemens acquisition and was actually run as a separate startup within the broader Camstar organization.
“When we acquired Camstar, we saw Omneo as a foundational opportunity,” said Bill Boswell, Siemens PLM Software’s senior director of cloud services marketing business strategy. While the rest of Camstar was brought under the manufacturing wing of Siemens business, the Omneo piece was carved out and put under the new cloud services team to create an enterprise product that would fit with the rest of Siemens’ cloud strategy, Boswell explained.
Performance Analytics Enable Proactive Problem Solving
That vision is starting to become a reality as Siemens launched its first application built on the Omneo platform earlier this month to address what it calls Performance Analytics. Available as a Software as a Service (SaaS) offering, Omneo Performance Analytics is designed to monitor data across the entire supply chain and customer experience. It can analyze billions of data combinations in seconds (under three seconds, in fact, according to Siemens officials) to discover hidden intelligence that can help organizations proactively pinpoint the source of product issues as opposed to finding them months, maybe even years after a product has been in use in the field.
The solution draws data from a variety of sources — for example, CRM, ERP, PLM and other enterprise platforms along with field service and manufacturing systems and Internet of Things (IoT) data — creating the basis for intelligence into product performance that was previously unattainable with traditional business intelligence solutions, Boswell says. Via its discovery capabilities, Omneo PA rapidly combines and analyzes all possible data sets, helping companies clearly identify and display the highest contributing factors to data anomalies and get answers to questions they previously had no capacity to ask, he explains.
“Today, companies are making decisions on a pretty short set of data because traditional business intelligence can’t take advantage of all this big data,” Boswell said. For example, an electronics manufacturer might be able to look at the design of a hard drive in the PLM system to drill down into performance issues, but that’s a far more limited view than being able to see all the performance aspects of all the hard drives out there in the field among all of its customers. “This field quality data combined with engineering data helps them ask a different set of questions,” Boswell said. It lets them start looking at everything from identifying supplier quality issues leading to costly recalls to improving the customer experience by spotting trends and addressing them in future products, he added.
Along with the discovery capabilities, Omneo has a graphical monitoring capability that provides a complete view of product performance across the value chain while letting companies track current and emerging trends related to their products. The user-friendly dashboard workspace lets non-data scientists create custom data analytics definitions and key performance indicators (KPIs), which help deliver insights far more quickly than having to wait days or weeks for answers.
Watch this video to learn how the Omneo Big Data technology can help manufacturers build better products.
About the Author
Beth Stackpole is a contributing editor to Digital Engineering. Send e-mail about this article to [email protected].Follow DE