The Industrial Internet of Things may well be the ultimate expression of where the data sphere is heading in the next couple of years.
It combines Big Data sets with real time and predictive analytics (Operational Intelligence) capabilities through the Cloud to influence decision making and to create and tailor a variety of services to augment customer satisfaction, increase efficiency, and minimize latency.
Its capacity to do so was substantially increased with the recent release of Equipment Insight, a solution from GE Intelligent Platforms that is specifically created to discern real-time (and future) insight into machine performance for Original Equipment Manufacturers (OEMs) looking to collect and monitor data about the performance of their products.
In addition to taking a proactive approach towards critical operational features, such as maintenance, troubleshooting, and customer satisfaction monitoring, it also has the potential to facilitate additional revenue streams, create jobs, and enable OEMs to do more with additional resources.
As such, it serves as the perfect paradigm for the potential of the Industrial Internet and a continual connectivity that inherently shifts the way that jobs are performed. According to GE Intelligent Platforms Solutions Ecosystem Leader Steve Pavlosky:
“We talk about the Industrial Internet in terms of four elements: connected machines, connected data, connected insights and connected people. We have analytics capabilities that allows people to analyze these vast amounts of data that’s coming out of these industrial processes to understand what the health of their equipment is. And once you’ve analyzed that data you have to get it to people who are responsible for reacting to it.”
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Insights into Equipment
Equipment Insight performs a number of valuable functions for OEMs attempting to monitor the performance of their products once they are in the hands of their customers. It collects massive quantities of data which it buffers, encrypts, and sends to the Cloud (hosted by Amazon’s Elastic Cloud) to perform real-time and/or predictive analytics. The results of those analytics are typically sent to the OEMs in a variety of ways which can impact how their equipment assets are handled. The particular features that Insight Equipment has related to product management include:
- Role-specific alarms: These alarms are generated based on predictive models and the actual operational data produced by a product, and can include an analysis of the likelihood of a particular type of malfunction, a reminder to implement service maintenance, and other types of warnings.
- A Work Flow Engine: This engine is largely dedicated to identifying and monitoring the carrying out of standard operating procedures which might be linked to any number of aspects of the product from diagnostics to maintenance.
- Notes: Notes provide a way for end users to communicate directly about the condition of any particular machine, and include an option for graphic representations such as pictures to denote specificity.
By taking such an active approach to the management of a fleet of machines that might be as diverse as jet engines, packaging machines, boilers, or any other type of industrial equipment, OEMs are able to anticipate failure and reduce its instance by more rigorously enforcing the proper maintenance of their products. The immediate effects are less down time, greater communication between OEMs and customers, increased performance and increased customer satisfaction. They also herald a shift in the traditional customer service model. Pavlosky mentioned:
“This is a dramatic change from the relationship OEMs have today where the data is stranded on the machines, and they’re relying on that customer unhappily calling them saying, ‘the equipment’s down, when can you get here’. The flipping of the relationship from a reactive to a proactive relationship has a huge benefit on the OEMs perception in the end user’s eye.”
Another less immediate, yet not less important, effect of OEM’s monitoring and provisioning of analytics for the data from their products is the production of new streams of revenue. At one point, the OEM industry was largely based on selling goods to end users and offering a limited warranty, which was perhaps augmented by offering the sale of extended warranties. With the real time monitoring of the copious quantities of Big Data engendered by industrial machines, however, OEMs can sell the option for end users to enroll in such monitoring programs.
Additionally, the former can generate greater revenue by providing more efficient maintenance (and by notifying customers of when maintenance is needed), in addition to simply selling parts if an end user has its own maintenance facilities. The potential to generate revenue for maintenance for distributed machines (such as pumps in an oil field) that are generally unattended and can significantly reduce productivity for companies if they are not at optimal performance levels can be particularly valuable, and mean the difference between a minor repair and a complete overhaul due to catastrophic failure. The same potential exists via the predictive analytics capabilities that can facilitate predictive maintenance.
Implicit to the aforementioned processes, pertaining to additional revenue streams and to the prudent implementation of Equipment Insight in general is the sharing of data between end users and the OEM. The nature of that data sharing is largely facilitated by the customer. Generally such data simply involves operational data regarding the functionality of the various machines employed; customers can choose to select what data is shared, OEMs can choose to have the analytics performed onsite rather than the Cloud if they so choose, and customers can also have access to the raw data and its analytics, rather than simply the data aggregates. Pavloski observed that:
“What we’ve found is there’s enough of an exchange of value either in the improvement in the service level, or the OEM could actually grant the customer access to the system so that the end customer can get visibility into the health of their assets just like the OEM has. There’s enough of an exchange of value where most end users will allow the OEM to collect the data.”
Ease of Use
The implementation of Equipment Insights is fairly straightforward and involves a collaborative effort between the OEM and their customers. The former is responsible for equipping their customers with Field Agents—data collection devices that can interact with most industrial machines via either a proprietary protocol or commonly used technologies such as semantics—which in turn send their data to the platform (either hosted or on-premise). The end user is responsible for working with the OEM to preconfigure the products so that there are specific parameters to determine performance efficacy relevant to their organization for which data will be collected. Once that template is created, it can be replicated on the various machines in use. All of the various capabilities that Equipment Insight provides are already configured within the servers.
Finally, the results of the analytics and operational intelligence are “published” in what is termed an asset map, which functions similar to a dashboard and is nearly as mutable. These maps determine what specific data related to the machines in use a particular user will have access to, and also determine the different ways those results are published via a plethora of visibility options. Organizations can literally choose how the results of GE Intelligent Platforms appear on either computers or mobile devices. Pavlosky noted:
“This concept of remote monitoring diagnostics and analytics is so key to GE’s interest in the Industrial Internet. As a major asset manufacturer, we differentiate ourselves in the market both by new assets and on a service basis by being able to collect data off of those assets, perform analytics, and optimize their performance and then deliver higher reliability of those assets.”
Expanding the Internet of Things
The significance of the products and services provided by GE Intelligent Platforms and its Equipment Insight goes well beyond OEMs and the cost-effective, proactive diagnostic and management of vital, expensive pieces of equipment and the bevy of additional revenue streams those services can create for organizations. It attests to the confluence of Big Data, analytics, the Cloud, and the potential of the Internet of Things itself. That potential has the ability to transcend the scope of the enterprise and to substantially impact daily living with a host of appliances and applications for smart homes and vehicles, tailored to monitor personal and domestic assets that provide the same degree of functionality and personalization that could soon typify the impending age of Data Management.