Asset management can account for a significant percentage of a transmission and distribution (T&D) company’s operating expenses and capital expenditures, with optimized operations and investments key to generating savings. New technologies can enable companies to capture these efficiencies. In fact, a recent McKinsey article explained how T&D utilities can leverage advanced analytics in their asset management strategies to unlock 10 to 20 percent in savings while improving overall reliability and performance of their networks.
This article builds on that thinking and takes a close look at a North American T&D utility, which we refer to as UtilityCo. In 2021, UtilityCo leveraged advanced analytics in asset management to unlock savings of 20 to 25 percent in operating expenses and 40 to 60 percent in capital expenditures, which could then flow as savings into the profit-and-loss (P&L) statement or be reinvested to deliver significant reliability improvement. These savings and increased investment capacity are particularly relevant given today’s increasing constraints, including pressure from customers on affordability, inflation growth, supply chain bottlenecks, and the growing need for investments in the energy transition, such as renewable-energy solutions, electric-vehicle charging infrastructure, and cybersecurity. Based on the success of the initial model, UtilityCo developed a road map that scales the asset management risk–based approach to two-thirds of the capital portfolio over two years.
The following case study highlights the results of implementing advanced analytics at UtilityCo, including the approach taken, the lessons learned, and the best practices to adopt for others embarking on a similar journey. Although this article is presented as a stand-alone example, our experience shows that the results from applying advanced analytics to asset management are accelerated when deployed as part of a broader organizational transformation.
UtilityCo: An overview
UtilityCo faced a number of key challenges that are common in the industry. For example, the utility didn’t take a risk-based approach when making asset replacement decisions or prioritizing preventive-maintenance activities, and it had decentralized asset management operations, with each operating company taking a distinct approach and methodology. In addition, although UtilityCo was able to collect valuable data, the data were underused and stored in multiple systems. Finally, UtilityCo relied on rules that oversimplified asset management decisions—for example, the “three strikes” rule, which called for replacing cables after they experienced three outages.
The results from advanced analytics
UtilityCo was able to effectively use advanced analytics in asset management in four ways. First, it optimized capital expenditures either by maintaining current risk and spending less—and letting the excess capital expenditures flow into the P&L or be reinvested to deliver more reliability—or by spending the same amount and achieving higher reliability through replacing the riskiest assets. Second, it lowered preventive-maintenance (PM) operating expenses by optimizing PM activities. When successful, this optimization can deliver similar or better reliability at lower cost. Third, it lowered corrective-maintenance (CM) operating expenses by lowering spending on CM after those riskiest assets had been replaced. And fourth, it replaced the riskiest assets to help achieve higher reliability (measured as lower SAIDI and SAIFI
performance) due to fewer failures.
Regarding capital expenditures for UtilityCo’s transmission transformers, the company underwent a paradigm shift, collecting data about each dollar’s impact on interrupted customer minutes. With this new perspective, UtilityCo determined it could reduce risk approximately two to …….