In the age of digital transformation, industrial organizations are tasked with moving faster than ever in an effort to better serve their customers. Yet, in many cases, it’s easier said than done. Current inhibitors, such as paper-based procedures, are restricting the necessary mindset shift from a ‘place to a pace’.
Think about this: 78% of manufacturers leverage outdated and ineffective paper documents, diminishing standardization and quality.
Industrial products are also increasingly complex, which is having a cascading effect on the value chain. For example, technicians servicing OEMs myriad machines operating in the real world are facing increasing logistical and operational hurdles.
These two challenges are front-and-center for Howden, a 150-plus year-old business providing global engineering solutions for industrial air and gas handling. Given their longevity, they have thousands of legacy products still serving in critical customer operations.
Howden’s mantra of ‘revolving around you’ alludes to the industrial giant’s dedication to customer-centricity, a relentless focus that has kept the company thriving across generations. Faced with the aforementioned challenges, Howden has recognized that the next era of customer engagement stems from capitalizing on digital transformation and leveraging innovative technologies to meet evolving customer needs.
Organizationally, Howden committed to this culture change with its ‘Data-Driven Advantage’ program that revolves around its customers’ lifecycle with its products. The digital backbone of this program begins with digitizing its paper-based documentation. Through this data unification process and creation of a digital thread internally, Howden is creating customer-facing digital twins to deliver the right information at the right time through augmented reality.
"DDA includes monitoring deployed machines and creating augmented reality experiences to improve customer service, deliver effective training, and reduce downtime. As Howden continues their transformation journey, we look forward to maintaining this close relationship,” says Matthew Sheridan, Senior Director of Field Execution at PTC, whose team has worked closely with Howden to execute on their DDA strategy.
With PTC as its partner, Howden is using emerging technologies, including industrial IoT and augmented reality, under this program and demonstrating the potential in immersive demos. Howden’s goal is to use digital twin technology to reduce business risk for customers by improving uptime of deployed products, potentially saving them millions in unplanned downtime.
This demonstration, which was initially unveiled at Hannover Messe 2019, features one of Howden’s diaphragm compressors. It was logistically challenging to transport the 10-foot-tall machines for an event – and far less immersive than an augmented reality experience. The AR demo seamlessly lays out the different functions and use cases relevant to a worker interacting with the machine in the real world. These different digital twin scenarios are simply represented in AR through these lenses common in industrial organizations.
These different industrial digital twin and AR ‘lenses’ can drive significant value across functions and enable real world industrial applications.
Customer-facing digital twins are a key element to Howden’s connected field maintenance program, Uptime. This service offering uses digital twins with underlying technologies (ThingWorx, Vuforia, etc.) to provide real-time performance data of deployed assets.
Predicting equipment performance and resolving failures in this scenario has been traditionally challenging and are a casualty of ‘break-fix’ maintenance models. To truly implement a ‘product-as-a-service’ business model, improve customer satisfaction, and solve product complexity issues, Howden is equipping its technicians with augmented reality solutions. Field engineers are now able to access step-by-step service instructions on a specific asset almost immediately. The results have been a drastic improvement in first-time-fix-rates and mean-time-to-repair.
Howden is also resolving issues for its customers faster through remote service. With IIoT built-in to their globally dispersed products, technicians or engineers are able to remotely troubleshoot, patch, or update software without physically visiting the site. This IIoT data can also feed into predictive and simulation models to further forecast failures for service organizations.
-Maria Wilson, Global Leader of Data Driven Advantage
The two key intertwined components of a digital twin are its digital definition data and physical experience. Definition data could include CAD, PLM, and other data that includes the unique asset characteristics and configurations, while physical experience usually is IIoT and real-world telemetry data captured through increasingly widespread sensors. Both sides need to be top of mind to create a full-fledged digital twin implementation that drives maximum business value to the organization.
Howden recognized the importance of definition data but hit a snag: They had many legacy assets in operation where the CAD didn’t exist or was inaccessible/in paper-form.
To subjugate this data bottleneck, Howden created a ‘theoretical data set’ as a performance baseline of its deployed assets, which included 150 years of product design data. The OEM can use this optimal state asset health baseline to measure against real-world IoT data to more accurately predict failures.
Similarly, on the physical experience side, there were some sensor inputs inaccessible yet critical to predictive maintenance models. Howden formed ‘virtual sensors’ -- essentially aggregated sensor inputs with engineering calculations – to provide real-time telemetry data for further predicting failures. For real-time simulation modules, including structural and thermal analysis, this data input is critical to inform predictions.
This comprehensive digital twin provides the framework to build on innovative use cases (predictive service) and adoption of cutting edge-technologies (artificial intelligence). This could include using deep learning in computer vision on AR to recognize Howden deployed equipment without serial numbers or predictive maintenance models using machine learning to precisely predict when a machine will fail.
Howden and similar forward-thinking industrial companies will increasingly look to build out digital twin strategies to drive near-term outcomes, quickly adapt to changing and new markets, and create a foundation to drive transformative change internally and optimize the value delivered to customers.