Will The Business Case For Predictive Analytics In Industrial Maintenance Solutions Work In Commercial Real Estate?
Improving maintenance procedures through predictive analytics has recently seen many notable developments. Siemens is integrating IBM Watson Analytics into its MindSphere platform to help implement predictive maintenance schemes. Rockwell Automation recently launched its predictive analytics capability for factories and machinery. And PTC and Deloitte Digital announced that they are jointly developing predictive maintenance solutions for factory operations.
These developments are hardly surprising. Predictive analytics enables users to detect if, and when, machinery is likely to breakdown. For example, predictive analytics enabled Duke Energy to identify a slight increase in the turbine vibration of a steam turbine – after maintenance was performance. The subsequent repairs resulted in $4.1 million of potential power generation loss being prevented. Facility managers at plants and factories are therefore likely to be looking for such solutions to keep things running.
But is predictive analytics a natural sell in the world of commercial real estate? There is certainly some evidence of this. Since 2009, UK-based retailer Sainsbury’s has used software provider Verisae's (now part of Accruent) predictive maintenance solution to reduce product loss from refrigeration failures. Since 2016, engineering firm KONE has partnered with IBM Watson to embed intelligent analytics in its elevators and escalators to improve their performance and reduce instances of unplanned maintenance.
Nevertheless, such examples are not necessarily the norm. According to our 2016 Global Energy Leaders Survey, 46% of the 250 facility managers surveyed said improving the collection, analysis and reporting of energy data from their electrical assets is a very important priority. In contrast, more granular asset-level energy management was only considered very important by 22%. And in our 2015 Green Quadrant for Building Energy Management Software, only 18% of the customer panel we interviewed considered maintenance scheduling and predictive maintenance to be a very important asset management functionality. The average facility manager is therefore more likely to be concerned with basic data capture than advanced solutions like predictive analytics.
So, when will predictive analytics make a widespread impact in commercial real estate? Verdantix predicts it will be more than five years before this type of solution gains broad acceptance. While the technology makes this prospect entirely possible, there are quite a few more things that need to come together. It will require a different business model to that seen in industrial plants and factories, where the solution can be applied to a few large pieces of machinery. Energy and maintenance directors need to become more familiar with the solutions. In the Verisae and KONE examples above, this relatively immature solution required a willingness to test trial its deployment. Other factors include usability / ease of delivery, cost of solutions (which is coming down), and the current focus on the implementation of simpler solutions that do not deliver 100% of the same benefits but remain an excellent value proposition such as remote monitoring.