NI – Getting complex products to market faster

NI (formerly National Instruments), headquartered in my hometown of Austin, Texas, is a company I’ve followed for a long time. In 2020, National Instruments shortened its name to ‘NI’, along with a cool new logo and business strategy.

NI is best known for its automated test and measurement tools that help research and validate new technologies. Recently, the company has reinvented its business model to focus on software-defined test systems and the addition of technologies such as data analytics and machine learning. This article is a progress report on how the company is helping customers modernize the product development process with software and the company’s vision for the future of test and measurement.

New product development aims to ensure that the end customer receives a quality product and that neither the customer nor the manufacturer incurs recall costs due to product failures after delivery.

As we have evolved to the next level of digital maturity, the complexity of systems has increased significantly. Examples include the move from 4G to 5G in the wireless space, from fifth to sixth generation fighter jets in aerospace and defense, to autonomy in transportation, and the proliferation of intelligent connected electronics with the rise of IoT devices.

Bringing complex, higher-performing products to market at a lower cost with little to no defects depends on an enterprise-wide product data strategy that seamlessly integrates product data and analytics throughout the product lifecycle.

To compete effectively, companies must use all data to improve the product development process, extract maximum value, and use test data to identify critical issues that impact quality and performance.

Customers deal with test data from NI hardware and other products at different stages of a product’s lifecycle. NI’s goal is to pragmatically aggregate data so that customers can perform basic analysis and gain insights to improve the product development process. Basically, to use test data to help improve product performance.

Fusion of the real and virtual world

Using virtual reality to create digital twins and digital threads opens new possibilities for the future of test engineering. A digital thread is a virtual universe housing the digital twin model. A digital thread enables interconnected models throughout the lifecycle of a system, with all models synchronized through a shared API.

As the name suggests, a digital twin is a virtual model that accurately represents a physical object. The object is equipped with sensors to produce data on performance aspects, such as energy yield and temperature, which can then be applied to the digital copy. The digital twin can run simulations, study performance issues, and generate possible improvements to the original physical object.

A digital wire with interconnected models can replace real world testing. Testing becomes faster and cheaper and in some cases reduces the environmental impact. By moving more of the design into the virtual world, complex products can be examined more quickly and reliance on costly, time-consuming physical prototypes is reduced.

These are not futuristic ideas. Siemens Mobility Rail Solutions builds high-speed and commuter trains – expensive expensive systems with thousands of components. Siemens uses NI hardware, TestStand software, VeriStand software and the LabVIEW FPGA module to build a fully functional digital twin of an entire train. More details about this story can be found here.

NI’s vision of the future of test data usage

NI develops a modular software platform that is assembled via APIs to meet the specific needs of each customer. All software building blocks run on-premises or in the public cloud. The platform uses security and resiliency frameworks and DevOps tools to deploy products in a simple and lightweight manner, whether in the data center or in the cloud.

The data ingestion service ingests data from all connected systems for analysis. Once ingested, the data is subjected to the appropriate persistent data service (an API implemented via REST), be it time series, waveforms, parametric, or other future data formats. The persistent data service uses polyglot persistence to use the correct storage, be it SQL, NoSQL, or object stores.

The architecture will include an analytics and machine learning (ML) layer consisting of a collection of ML frameworks such as Kubeflow and Spark R used to build machine learning models. NI will provide models tempered to reflect industry best practices. You can choose from well-known analytical tools such as Microstrategy, Tableau and Power BI.

The visualization layer is connected or embedded in standard tools and a UI framework layer. The UI framework consists of Angular and other building blocks to provide a simplified user experience in any browser or mobile device.

Again, this is not all futuristic. General Motors is working with NI on the battery cell engineering process to provide insight into test data to make decisions about optimizing product performance. GM invests in web-based cloud computing toolset; staff; and a data platform with NI SystemLink™ software as part of the architecture. The scalable solution saves thousands of hours of manual work by automating the end-to-end process, from data ingestion to making it available on-demand. You can find this and other stories here.

Shut down

Shifting the NI company’s strategy to modernize test and measurement with software, cloud, and machine learning capabilities creates value for customers. In addition, it was also a brilliant move to move the entire organization to focus on four markets: semiconductors, transportation, aerospace, defense and government.

Increasing product complexity requires more detailed insights into the product and its behavior. Test data provides many answers, but this valuable asset is often underutilized.

There is power in test data when it is used effectively, rather than just being stored on a hard drive. Test data can optimize production processes and even improve product design. Test data can be fed into simulations to improve accuracy, identify production bottlenecks, increase product quality and reduce time to market.

The NI charter is to take responsibility for collecting and storing test data and turning it into a powerful asset that adds value back to the organization. Unlocking the value of data throughout the product lifecycle from design, validation, manufacturing and deployment is critical to success and competitiveness. NI has a wide range of tools throughout the design process that integrate seamlessly with third-party tools a customer may already have in design labs and simulation environments.

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Moor Insights & Strategy Founder, CEO and Principal Analyst Patrick Moorhead is an investor in dMY Technology Group Inc. VI, Dreamium Labs, Groq, Luminar Technologies, MemryX and Movandi.