Facility Management (FM) Magazine, Australia: Cognizant’s Assistant Vice President of Manufacturing, Logistics, Energy and Utilities Consulting Says Predictive Analytics can Optimize Asset Maintenance
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“Collecting and distilling digital information and extracting meaning out of it holds great potential to enhance customer satisfaction, reduce total cost of ownership, optimize resources and improve compliance,” writes Badrinath Setlur. “Little wonder then that predictive analytics—a process of using statistical and data mining techniques to analyze historic and current data sets, create rules and predictive models and predict future events—is fast becoming a vital instrument to realize asset lifecycle cost reduction and improve the speed and accuracy of decision-making.” Excerpts:
“Between asset procurement/commissioning and decommissioning/salvage lies the productive life of an asset. Regular upkeep or maintenance is needed to maximize this life. The ability to predict a failure can help moving maintenance activities closer to the real need for maintenance and reduce over-maintenance. A system of assets, designed and scheduled for maximum productivity, can benefit immensely if a failure of an event requiring maintenance is known in advance. Alternative schedules or plans can be prepared to ensure maintaining productivity levels.
Ironically, in almost all common maintenance scenarios, the ability to predict failures and move maintenance procedures closer to failure exists. It is just that the risk of failure keeps many organizations from taking those steps.
Does building predictive analytics capability cost a lot of time, money and effort? The answer to that big question is an emphatic “no”. There is always a sweet spot where savings from predictive capabilities (reduction in maintenance expenses, capex, spares utilization, and so on) outweigh the cost of building those capabilities (software, human resource, and so on). Once done, the benefits through direct asset-related saving (fewer maintenance dollars, fewer spares, longer life span), and organizational saving (optimal teams, increased operational efficiencies) are immense.
KPIs (Key Performance Indicators) are different for different asset types, operating conditions, criticality, and so forth. Due consideration need to be given to the role of statistics and the related process changes and change management, among others, required to build a predictive maintenance culture. Data and statistics will provide quantification, but will need to be applied with business insights to derive benefits (for example, the 70% risk quantification mentioned earlier).
Traditionally, maintenance practices are classified as reactive, preventive, predictive, and reliability-centered maintenance (RCM). However, predictive and RCM approaches are the ones that best leverage predictive capabilities.
Predictive analytics is the process of moving from hindsight to insight. While data and distilling data are the key, equally important is how organizations instrument, capture, create and use data to address their strategic objectives.”
Click below to read the full article that appeared in Facility Management (FM) magazine, Australia’s leading news source for facility managers and allied property management experts.
