Lockheed Martin and Ulbrich Stainless Steel use SAS AI to conquer IoT challenges
Manufacturing leaders Ulbrich Stainless Steel and Lockheed Martin are using SAS analytics to drive IoT innovation in their facilities. Whether manufacturing engineered stainless steel for medical implants or creating the speciality wire for solar panels, Ulbrich Stainless Steel & Specialty Metals knows its customers count on consistently high-quality products. Staying ahead of maintenance and production challenges that keep precision metals rolling out of its plants on time is a high priority for the global company. That’s why Ulbrich recently chose SAS® Analytics for IoT to analyse the data created by its plant sensors.
Ulbrich manufacturers the metals used in a wide range of specialty products from engine parts to the wire used in stringed musical instruments. “Precision and quality are key factors in manufacturing highly engineered metals that support our customers’ varied needs,” said Jay Cei, Chief Operating Officer at Ulbrich. “Collecting machine and sensor data from our factories and integrating that with ERP system data will help us understand the intricate relationships between equipment, people, suppliers and customers. Using SAS to learn what our IoT data means is critical for understanding how we can become more productive and efficient in the future.”
“Streaming analytics will not only help Ulbrich understand what is happening now with their machines, but it will also enable them to predict future events, such as when a machine needs maintenance before it breaks down,” said DJ Penix, President of Pinnacle Solutions, a SAS implementation partner.
Faster results for all users with streamlined SAS Analytics for IoT
Recent upgrades to SAS® Analytics for IoT mean enterprises have access to the latest suite of AI, machine learning and streaming analytics available.
The software provides a simplified way for any user to prepare stationary and streaming IoT data for analysis without specialised skills. Whether a data scientist, business manager, or someone in between, they can use SAS Analytics for IoT to quickly select, launch, transform and operationalise IoT data to make informed, timely decisions.
The upgraded SAS software provides open application program interfaces (APIs) to enable integration with other SAS, third-party and open-source products.
“SAS talks about ‘democratising analytics’ as the ethos behind SAS Analytics for IoT,” said Marta Muñoz Méndez-Villamil in a recent IDC Market Note. “This comes as a welcome message to a technology that seems to be stuck at precisely this stage. Tools and solutions that help simplify analytics, insights visualisation, and actioning of IoT data accelerate enterprises' time to value from their IoT implementations.”
Jennifer Major, Head of IoT at SAS UK & Ireland, commented: “IoT is playing a major role in redefining how manufacturers work. But the value of IoT is inherently tied up with the value in the data it produces – and manufacturers can only access that value if they have the ability to rapidly and accurately analyse data. As volume, variety and velocity of data increases, advanced, automated analytics are essential to allow companies to consistently derive actionable insights from shop-floor sensors.
“The possibilities are huge – with the added insight, manufacturers will be able to drive more intelligent, effective working processes. But that must be built on effective analytics. We’re glad to be working with Ulbrich as they make strides towards a more connected future.”
Mike Guilfoyle, Director of Research at ARC Advisory Group, said there’s a gulf between companies undertaking analytics-driven digital transformation and those successfully scaling their efforts. “This is due in large part to the underlying complexity of Industrial Internet of Things (IIoT) ecosystems and the many business needs related to them,” he said. “To be successful with IIoT, an organisation needs an analytics solution that can support a diversity of needs, including myriad use cases, disparate user requirements, agnostic interoperability with systems and sources, the capacity to manage data at rest and in motion, and a breadth of analytics methods.”
SAS is showcasing SAS Analytics for IoT and its embedded AI capabilities via connected patient, connected quality, and connected equipment demonstrations at SAS Global Forum, the world's largest analytics conference, April 28 to May 1 in Dallas. To learn more about the convergence of AI and IoT, download The Artificial Intelligence of Things white paper.
Lockheed Martin makes herculean strides with SAS Analytics for IoT
Lockheed Martin’s Hercules C-130 aircraft has been in production and operation for 65 years and has been continually reinvented to meet changing customer needs. To keep this aerial platform evolving, Lockheed Martin has turned to SAS, another proven platform that has been continually reinvented to meet new customer needs and requirements.
Today, C-130s are operated out of 70 nations around the world. The four-engine turboprop started as a troop transport in the 1950s but has evolved to support upwards of 100 different mission requirements in its lifetime. Its many mission capabilities include flying into hurricanes to collect weather data, landing on short and/or austere runways to deliver relief supplies, aerial firefighting, air-to-air refueling, long-range search and rescue, global peacekeeping, special operations and supporting critical military operations around the world.
With AI and advanced IoT analytics from SAS, Lockheed Martin and its customers analyse data streaming from sensors on each aircraft to predict maintenance and repair needs, and ensure the Hercules can continue to lift, fly and deliver.
“When you understand the probability of parts failure, everything changes in the way you manage and operate your fleet,” said Duane Szalwinski, senior manager with Lockheed Martin’s sustainment organisation. “With SAS, we’re developing fleet-wide practices that demand a positive culture shift for us and our customers.”
 IDC, SAS Dives Deeper into IoT Analytics, doc #EMEA44977119, April 2019
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