Remote Things Are Increasingly Connected, Intelligent & Predictive

Written By: David Immerman
  • 12/13/2018
IIoT enables mass-deployments of remote assets

An organization’s assets naturally span both inside-and-outside of its traditional walls. Given interior assets relative proximity to central operations they have been historically straightforward to unlock insights from through easier accessibility of data. However, assets that expand outside of an organization’s boundaries and are considered ‘remote’ have historically operated in a black-boxed environment.

This is all changing through the Industrial Internet of Things (IIoT). Included in this technology progression is ubiquitous connectivity embedding bi-directional communication from a remote asset to a central repository. Cloud computing is also knocking down previously inhibiting compute resource accessibility through a scalable decentralized infrastructure, unrestricted by an asset’s geographic location. At the crux of remote monitoring deployments are IIoT platforms connecting, contextualizing, managing, analyzing and enabling intelligence for these remote assets, which unlock prevailing use cases across design, operations, services, sales & marketing, among others.

IIoT enables mass-deployments of remote assets

This ecosystem of combined technologies has exploded with the progression of smart connected products, although remote monitoring can pertain to smart connected operations internal use cases as well. A whopping 38% of PTC customers’ total connected things are considered remote and 46% of PTC IoT customers have at least one remote connected thing.

What Are Customers Doing with Remote Condition Monitoring Deployments?

PTC customers who are considered providers of industrial products are major adopters of remote monitoring applications, which likely comes in the form of smart, connected products. These customer-facing remote assets can range greatly in type, use case and business outcome. A remote asset can be fixed or mobile, which could leverage real-time location data in several use cases including supply chain visibility or fleet management.

A fixed remote asset could be a pump in a rural area or a medical device in a hospital. Bosch Rexroth’s CytroPac hydraulic pumps generate sensor data for temperature oil levels, filter contamination and other metrics for a condition-monitoring application. The predictive system increases asset uptime as well as provides a valuable feedback loop of real-world performance data to product design teams.

Medical device OEM Elekta implementing remote monitoring and servicing reduced technician servicing (truck roll) through 20% of issues being solved remotely. It also increased equipment uptime and improved customer satisfaction through uninterrupted treatments for more than 14,000 patients across 6,000 medical facilities. PTC recently announced a similar partnership for remote monitoring SuperSonic Imagines’ medical devices.

There is an array of other KPIs that remote monitoring enables for field service organizations including improving first-time fix rates (FTFR) and decreasing mean-time- to repair through proactively preparing technicians. As in the Elekta case, transforming routine scheduled inspections to condition-based ones, substantially reduces truck rolls, operational costs and can extend an asset’s useful life. Bi-directional IoT-enablement can push out OTA software updates to a device in the field’s firmware or issue commands like reboots, decreasing pricey manual intervention.

Other proof points of PTC customers benefiting from remote monitoring include Trane achieving 99% asset uptime and reducing its customers energy costs by 10%, while McKinley Elevator is attaining an 88% FTFR.

What Is the Future of Remote Condition Monitoring?

Many of the technologies are readily available to unveil futuristic next-generation remote monitoring use cases. The underlying mantra are connected systems shifting from reactive to predictive, which can issue in an abundance of benefits across an enterprise. This trend is mirrored in the State of Industrial Internet of Things report and PTC’s new data site with customers citing remote monitoring, operating and servicing as prevalent use cases and predictive applications on the emerging end.

IIoT external use cases

We anticipate this mass shift to predictive incurring in the near-future; enterprises must first collect baseline asset behavioral data through remote & asset monitoring to then apply predictive applications for maintenance, servicing and other use cases.

The future will entail even more enhanced predictive machine servicing where a technician leverages route optimization fueled by traffic and environmental data and business systems data to dictate prioritization of servicing. Through edge computing and analytics, a remote asset will be capable of self-healing through requesting OTA software updates, patching or technician if necessary. Digital twins of remote assets will tap into its digital definition and apply powerful simulation instances to further predict potential malfunctioning in a real-world environment. The interface for interacting with remote assets for service technicians is changing through the emerging Augmented Reality lens, capable of streamlining these data insights and scaling remote expertise.

Final Thoughts

The Industrial Internet of Things extending an organization’s operational reach will continue to drive new business outcomes and feed into broader digital transformation initiatives. The opportunity to further understand, interact with and streamline previously challenging to access assets, is growing immensely with evidence coming from an increasing amount of PTC customers connecting remote things and generating related use cases.

  • Augmented Reality
  • CAD
  • Industrial Connectivity
  • Industrial Internet of Things
  • PLM
  • Automotive
  • Connected Devices
  • Digital Transformation
  • Digital Twin

About the Author

David Immerman

David Immerman is as a Consulting Analyst for the TMT Consulting team based in Boston, MA. Prior to S&P Market Intelligence, David ran competitive intelligence for a supply chain risk management software startup and provided thought leadership and market research for an industrial software provider. Previously, David was an industry analyst in 451 Research’s Internet of Things channel primarily covering the smart transportation and automotive technology markets.