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How Service Relationship Management and the IIoT Are Keeping Transportation on the Right Road
As the Industrial Internet of Things steps out of the shadows and sheds the chains that have kept data from being holistically actionable, we're seeing the beginning of a new commercial asset-management paradigm.
Apr 02, 2017—
In 2016, the Internet of Things went mainstream, but in 2017 we expect the Industrial Internet of Things (IIoT) to transform operations across numerous industries. Also known as the Industrial Internet, the IIoT enables machine-to-machine (M2M) communication of usage, performance and health metrics. There is quite a buzz around using this data, along with machine learning and other predictive algorithms, to help anticipate and eliminate potential causes of downtime.
Nowhere is this more evident than in the commercial asset service ecosystem. Trucking fleets, construction assets, agriculture and power-generation equipment are all susceptible to costly unscheduled downtime and generally long repair cycles.
Effectively leveraging the IIoT is critical to commercial assets, because it has the opportunity to introduce automation and efficiencies that currently have significant negative bottom-line impact. This is especially true in transportation, for which the average downtime for an asset is around four days but only around 10 percent of that time is attributable to actually fixing the issue. However, capturing machine data is just one piece of the process, especially when it requires multiple disconnected phone calls and emails to actually start to engage on a repair event.
To effectively manage the repair and maintenance process requires contextual access to asset diagnostics (including severity, likely cause and repair plans), along with service history, warranty coverage, maintenance status, build details, pending operations and service campaigns, and other information, which is typically scattered across multiple online and paper-based systems. To make matters worse, communications, updates and approvals are also challenged by manual-, phone- and paper-intensive processes. This all results in extended asset downtime, mountains of paperwork and a lack of transparency into the service supply chain.
Collecting IIoT-based information from these assets is a good starting point and can, over time, be used to improve the specificity and severity around certain types of fault codes, and eventually be used for predictive (or condition-based) maintenance. However, having access to this information alone is not enough—this data has more value when it's part of a closed-loop service event management process that paints a bigger picture to help facilitate faster repairs, less downtime and better decision-making.
Getting More Value from IIoT Data
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