Improving Physical Asset Management

Regional utility with multiple segments (poles, transformers, breakers, etc.) across their energy delivery infrastructure, segmented into thousands of discrete attributes and spread across 11 systems.

Overview

Visiting your health care provider these days can be frustrating.  With the growth of large regional and national providers, resources have multiplied but many times as you are dealing with a health issue you have to repeat your entire health history. This is especially frustrating when the name over the door is on every location you visit.  If they can’t put all of your information in one place and make sense of it, how can they effectively leverage population data and clinical outcomes to move the patient experience to the next level?

Strategy and Goals

That’s just the case for one regional utility, but it could be the same for any multi-facility organization.  This client had multiple segments (poles, transformers, breaker, etc.) across their energy delivery infrastructure, segmented into thousands of discrete attributes and spread across 11 systems.  The one common factor was the experienced workers in charge of this mass of data.  It was their expertise that knew where to look in these 11 systems.

The objective was to minimize the number of systems their inventory was housed in, ensure data integrity across systems, and improve efficiency.

The Final Results

• 95% Improvement in asset utilization
• 90% Better visibility into asset cost allocation
• 70% Increase in pole attachment revenue

The Conclusion

RCG Global Services worked closely with the client team to collapse the number of systems from 11 to 3 while establishing proper data governance and standards. Using agile development processes, RCG implemented a data lake using Cloudera in a cloud environment, along with tools (Trifacta & Spark) that enabled data cleansing and analytics. This allowed visibility of assets across the system with high integrity, and the ability to run analytics to more efficiently manage operations and prepare for the future of adding Machine Learning to further optimize results.