In the ideal world of Industry 4.0, data becomes the new currency, flowing wherever it is needed both within and between cooperating organizations for the benefit of everyone.
But achieving this data openness is a big challenge for many organizations, which have traditionally confined their operational data in silos. Within the organization, separate systems manage different functions with little or no exchange of information between them. And if they haven’t even been sharing information between different departments, many organizations baulk at the idea of sharing it with their equipment suppliers.
But organizations can only reap the huge rewards of Industry 4.0 by allowing data to flow freely to whichever systems can make the most of it.
The promised benefits of Industry 4.0 touch on every area of business, but the ability to transform asset management and maintenance operations provides a good illustration of the vital role played by data.
Ditching reactive or even planned maintenance in favor of a Predictive Maintenance regime can slash the number of plant breakdowns and unplanned downtime, as well as helping manufacturers target maintenance efforts where they’re needed. This in turn boosts overall equipment effectiveness and productivity.
It’s pretty obvious how this provides a boost for end users, but Predictive Maintenance also offers big opportunities for the companies supplying production systems because it enables them to switch to a servitization business model. By guaranteeing that their equipment will deliver, say, a certain number of welds per shift or bottles filled per week, suppliers can extend their business model and enjoy the increased revenues associated with advanced services, as well as building stronger, long-term relationships with their end-user customers.
This is only possible if suppliers can access information about the condition of assets operating in the field. Without live plant data, suppliers are working blind, making it impossible for them to predict with any accuracy when maintenance is needed to prevent future breakdowns.
That’s why Senseye’s Predictive Maintenance solution, Senseye PdM, has always been an open solution. While it can be supplied as a complete package, it is also available with an extensive set of APIs that are fully documented and supported.
Senseye PdM can use existing data sources or dedicated condition monitoring sensors to analyze machine performance while plant operations continue as usual. It operates in the background to analyze normal machine behavior as well as historic data if available and continually learns from sensor and operational data. After a short time - around two weeks - it’s then ready to provide insights to drive a scalable predictive maintenance program. Senseye PdM’s unique algorithms can turn data into a prediction of the Remaining Useful Life (RUL) of manufacturing assets – a technique known as prognostics.
As Industry 4.0 gains momentum, such obvious benefits will far outweigh any initial challenges that organizations face in opening up their access to operational data.