Reduce costs, maintain business continuity and innovate.
Choose your favorite two, as the saying goes. Digital business transformation–specifically leveraging IoT technology, analytics, machine learning and cloud computing to better manage assets and improve workforce productivity–now puts utilities in the position of being able to choose their “favorite three.”
Like sand under foot in the ocean waves, market dynamics such as flat retail electricity sales and increased competition from distributed generation are eroding the traditional volumetric utility business model. And they’re forcing utilities to confront an unprecedented set of business challenges as they retool their businesses for the new reality while also keeping the lights on and their shareholders happy. In this challenging business environment, utilities must get every drop of value they can from the assets they’ve invested in–more productivity, more efficiency, more reliability and longer life.
Investor-owned utilities in the U.S. collectively manage an asset base valued at more than $1.5 trillion, according to 2016 figures from the Edison Electric Institute. And that doesn’t include the thousands of municipal utilities and co-ops that dot the countryside. These assets extend from the power plant to the edge of the distribution network. They’re geographically dispersed, heterogenous in form and function, and typically supported by legacy systems that form technology silos within the utility, inhibiting improved performance and increasing costs.
In this asset-intensive business, achieving a higher return while streamlining the workflows and increasing the productivity of the workforce that maintains them, represent key opportunities and a fundamental step on the road toward digital business transformation. But how do you get there?
This is where operational technology (OT) and information technology (IT) truly come together, aided by IoT (Internet of Things) advancements in machine learning, both edge and cloud computing, analytics, even augmented reality (AR) tools. The result is an ability to stitch together data from disparate devices and systems to provide a new and highly insightful context to data that greatly expands situational awareness and business intelligence.
This new insight enables utilities to move from a reactive and preventative maintenance approach based on limited data and operator experience to a data-driven prescriptive approach to asset management. In this environment, assets can operate closer to their ratings to increase utilization and failures are anticipated based on a rich history of asset behavior.
This goes well beyond just sensor data. IoT technology advancements also enable analysis of an increasingly rich stream of metadata (data about other data if you will) to extend awareness and understanding. For example, a sensor on a valve at a powerplant or a distribution transformer collects and delivers the data it was designed to provide (temperature, moisture, motion etc.). But the metadata — how the device behaves or how it communicates — tells a story of its own that can be as insightful as the specific metric it was designed to measure.
Add to the sensing data and the metadata the ability to leverage asset data in this analysis from upstream legacy systems (ERP, DMS, CIS, GIS etc.) and the picture is nearly complete. Being able to capture and analyze basic asset data–procurement info and age, technical specifications and ratings, maintenance history, failure history, location–rounds out a comprehensive 360-degree view of the asset base to create a contextualized business intelligence capability never available before.
Combine this data-driven awareness of asset health with a smarter workforce and interesting things begin to happen. With an aging workforce, there’s an acute need to capture the knowledge of workers nearing retirement while also investing in new technologies to drive improved productivity and entice a tech-savvy millennial workforce.
This human side of digital business transformation is every bit as critical as the technical side. The field workforce of tomorrow will leverage new technologies in new ways. From augmented reality tools for training and executing maintenance procedures to automated workflows to reduce time on task, these digitally enabled workers will be more productive and efficient, taking less time to complete tasks and less time in between tasks. By using these tools and approaches, one of our utility clients has already achieved a 20 percent reduction in work order execution time.
The journey starts by engaging a partner who can combine utility-specific domain expertise with robust data science and IT/OT system integration capabilities. The ability to deploy a vendor-agnostic technology platform capable of marshalling and managing data from multiple sources (including legacy systems) and applying the right analytics while developing solid, SME-driven algorithms are two key initial steps on the road to digital transformation and better asset performance. Sooner than later you will discover that choosing your “favorite three” isn’t as far-fetched as it once seemed.
About the author: Mazi Fayazfar is the Chief Technology Officer for telecom, media & utilities group for Atos in North America. He can be reached at email@example.com. For more information, visit: https://pages.atos.net/digital-utility/