• AMT

[Webinar] Avoid the Data Swamp: Creating a Clear IIoT Data Lake Using Standards Such as MTConnect

Updated: Jun 4

Webinar

June 8, 2020

3:00 p.m. EDT

Join AMT and Lockheed Martin for an invaluable presentation on how LM’s strategies for data management, security, and analysis can benefit your business.


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The speaker: Jan de Nijs oversees Lockheed Martin’s manufacturing production data collection and management at the F-35 plant in Ft. Worth, Texas and is team leader within the Lockheed Martin Digital Transformation Program. In 2019, he was awarded the prestigious distinction of being appointed a LM Technical Fellow for his contributions to manufacturing engineering, business systems integration, and Industrial Internet of Things.


The problem: Modern machines are built with a multitude of sensors that track the machine’s condition and work. Manufacturers using transformative technologies gather that raw data from these inputs and outputs. However, because a production process includes many machines and software from different manufacturers who use different codes and programs, users must be proficient – if not experts – in these different languages if they intend to take advantage of the gathered data. This is highly impractical. As a result, the raw data piles into a swamp of unclear, cluttered, and ultimately useless data points. 

MTConnect: MTConnect is an open-source standard that processes the different programming languages and allows them to be stored, analyzed, and presented in a dashboard. Users may then manipulate settings, monitor machine efficiency, plan for maintenance as machinery wears down, and much more. This significantly improves the user experience and increases production efficiency. 

Lockheed Martin: With nearly 55,000 scientists and engineers in 375+ facilities around the globe, Lockheed Martin generates and processes huge volumes of manufacturing data. Their approach to Industrial Internet of Things (IIoT) data management leverages a cloud-based data lake, custom and off-the-shelf data collection software and hardware, secure networking devices, and a huge variety of industrial protocols in order to minimize data complexity and ambiguity. Understanding and evaluating the LM approach is useful for deploying similar technology stacks at any scale, knowing trends and current best practices in manufacturing, and of course for any vendors selling into the company and its supply chain.