But while this wealth of new information presents numerous benefitsfor advancing APM capabilities, it begs a question that has wide-ranging implications for businesses, from financial to legal to competitive and beyond-who actually owns this data?
This article explores how these advancements in technology are creating new operational dilemmas and why the question of data ownership willbecome increasingly important as time and APM capabilities move forward.
The Data Explosion and Migration to the Cloud
In APM, the transition from preventative maintenance (PM), centered on calendar-based service intervals, to predictive maintenance (PdM), based on actual run times or machine cycles, was the beginning of CBM and it was facilitated by increased data. The additional information associated with any given asset was one or two additional data points for the time or cycle count accrued. As CBM continued to evolve and additional data points were obtained through vibration monitoring and analysis, lubricant analysis and thermography data, additional information was accumulated on the most critical assets.
Combined with the advent of wireless and digital sensors, leading asset operators can now collect a magnitude of more information. In this realm, it is not uncommon to collect ambient information, such as temperature and humidity, operating information, such as operating pressures, temperatures, flows and pH for process equipment, as well as real-time vibration, load, speeds and feeds data. Consequently, instead of a few bytes of additional data for each asset, theremay be dozens of additional data points being gathered every second.
The Cloud's Increased Computing Capacity
Whether it's customer buying trend data, pricing information, or APM intelligence, the data capture and analysis capabilities advanced by IoT and big data are outpacing the capacity of on-premise computing power.
Within APM, the natural evolution to this trend has been to move the analysis of APM data to the Cloud. While high capacity, in-memory computing architectures may change this scenario over time, for today, the vast majority of asset owners find that cloud-based analytics of CBM data is the most economical approach for extracting value from their data.
Aggregation in the Cloud
Today, traditional CBM application providers, enterprise asset management (EAM) providers and equipment providers all offer on-demand, cloud-based APM/CBM solutions that assist asset owners in optimizing their maintenance activities. In doing so, many of these providers have realized that better decisions can be made if they aggregate the data from all the asset owners into a single database. Rather than trying to make a maintenance recommendation based on the data from a single piece of equipment in one factory and its historical performance, they can take all the data from all their subscribers and aggregate it to produce a far better model of overall equipment performance. While this presents an exciting advancement in data analysis and decision-making, this deviation from the traditional data ownership model can lead to new concerns.
The IoT Is Enabling OEMs to Join the Aggregation Bandwagon
While some manufacturers of production equipment-notably heavy construction and industrial equipment manufacturers-have been equipping their mobile equipment with a rich array of onboard sensing technology to provide better diagnostic capabilities as a service for some time, the IoT has enabled any equipment manufacturer to do the same at an extremely low cost. Control system providers routinely offer the original equipment manufacturers (OEMs) who use their platforms the functionality to collect, aggregate and analyze performance data from their machinery at very affordable price points. Who better to make service recommendations about proper maintenance intervals on a piece of equipment than the company that manufactured it? Even more so considering the manufacturer has performance data from virtually every machine it has delivered.
New Business Models Are Emerging
As OEMs gain better insight into the performance and maintenance needs of the equipment they manufacture, they are beginning to expand beyond simply selling maintenance recommendations as a value-added service. With enhanced knowledge about the equipment, they are now in a position to actually provide asset care services for the equipment they sell. This lifts the requirements burden from manufacturers to have a highly trained maintenance staff to support the dozens of different brands and types of assets they operate. Since the OEM can focus on its equipment, it can offer a highly skilled workforce at a competitive price point.
For some time now, large OEMs have gone a step further and sold capacity instead of capital. For example, a top aircraft engine supplier bills the airlines for the use of their jet engines and provides all the parts and service as part of the deal.
The IoT and cloud analytics are also enabling even small OEMs to pursue the same capacity instead of capital paradigm. This trend of OEMs large and small moving into a role with increased intelligence and influence holds numerous benefits for APM, but also has broad implications for the business environment at large.
Who Will Own the Data?
That's the big question. The changes wrought by the IoT and cloud analytics give rise to a problem few companies have even begun to address. As companies move more and more data to the Cloud and allow third parties to use that data to make asset performance recommendations, provide asset care services, or even sell capacity instead of requiring the purchase of capital goods, who will own all this data?
In most situations, maintenance or operations departments have contracted with either an OEM or a cloud-based CBM analytics provider to provide CBM inputs into their EAM platform or computerized maintenance management system (CMMS). In some cases, the providers are aggregating the asset owner's data, while in other instances, it is kept separate. Unless IT has been part of the process, it is rare that data ownership issues are even addressed in the service agreements. This may ultimately prove to be a problem for many companies. In the event of an accident, will the information about the process and equipment be shared with the other litigants if there is a legal issue? Will regulatory bodies get ready access to asset and operational data in the event of a potential violation? Will competitors be able to glean insight into processes if they can access performance data?
All of these are valid questions and the answers clearly depend on who owns the data. In a scenario where a company is just shipping data off to an analytics engine with no aggregation and can control what gets sent and when, then they probably can make a case for owning the data. But what about when the OEM is in control and a company's data is aggregated with others? Or when the OEM sells a company capacity? In these cases, the answer is not so clear.
As APM evolves, the data ownership question will likely emerge as on of the biggest challenges companies will have to deal with. Before companies go too far down the path of leveraging all that rich CBM data coming from the IoT, they need to spend some time thinking about how they will deal with the central question of APM data ownership.
Dan Miklovic joined LNS Research in May of 2014 and is now a Principal Analyst with his primary focus being research and development in the Asset and Energy Management practices. Dan has over 40 years of experience in manufacturing IT, R&D, engineering, and sales across several industries. Dan holds a BS in Electrical Engineering from the University of Missouri, as well as an MS in Management from the University of Southern California. www.lnsresearch.com
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