Asset downtime has always been a big burden to most of the Utilities and Manufacturing firms. The emergency repairs and heavy maintenance takes away enough productive time from these firms
Redefining Asset Maintenance & Failures in Utilities with Predictive Analytics
Asset downtime has always been a big burden to most of the Utilities and Manufacturing firms. The emergency repairs and heavy maintenance takes away enough productive time from these firms, hitting revenues and productivity of the firm. Research suggests that 58% of utilities want a mechanism for asset maintenance in all phases, starting from installation to decommissioning of the asset.
All companies are looking for answers to the following:
- Is it possible to know upcoming issues in assets before they become real?
- Can we reduce maintenance costs on assets?
- Can we ensure the business will see fewer downtimes?
According to research by ARC Advisory Group, only 18% of assets have a failure pattern that increases with use or age. This means that Preventive maintenance alone is not enough to avoid failure in the other 82 percent of assets and a more advanced approach is required. These issues can be minimized to a large extent by performing Predictive analytics on a large amount of data being generated by smart sensors attached to Utility/Manufacturing assets. Making the right prediction of failures beforehand turns out to be one of the most efficient ways to keep a watch on the health of critical assets.
Using machine learning algorithms and data mining techniques, Utility companies can leverage present and historical data to create data analytics models to take timely decisions pertaining to asset health.
Cherrywork® Predictive asset analytics app. can:
- Perform data acquisition and storage either on the cloud or on-premise systems
- Perform data analysis/transformation i.e. conversion of raw data for machine learning models
- Evaluate Asset health i.e. generating diagnostic records based on trend analysis
- Generate predictions of failure through custom machine learning models, and estimating assets life
- Generate decision support system i.e. recommendations of best actions based on given inputs
- Visualize i.e. Making information accessible in an easy-to-understand format
In our experience, we see all this work resulting in a good cost-saving for our customers where they are moving rapidly from Reactive maintenance or Preventive Maintenance to Predictive Maintenance.
As per Mckinsey’s Digital Utilities report, adopting advanced analytics to power predictive maintenance offers a new avenue to improve performance, while reducing asset-management costs by as much as 10% to 20%, plus conservative estimates supported by various use-case analysis suggest that such advanced analytics can boost profitability by 5-10% while increasing satisfaction for customers.
Our experience shows that such ‘predictive asset analytics apps’ can help utilities sector navigate the ever-shifting landscape, take advantage of new opportunities arising from these developments, and manage new challenges.
About the Author:
Sandeep is Business Architect with Incture. He is a seasoned professional focused on increasing IT solution footprints for global clients and passionate to drive digital growth for the business. He has formidable experience in leading Solution Consulting & IT Programs in Data Analytics & Supply Chain space. He has strong exposure to working in multiple geographies.