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The energy & utilities and oil & gas industries have traditionally been slower in adopting next-gen technological interventions. The unique challenges and changed circumstances in recent times are forcing organizations to revisit their operational models.
- The operating margins of E&U businesses have been declining steadily as the industry continues to be plagued by increasing downtimes, glaring skill gaps that exist between field personnel and trained workforce, and falling workforce and asset utilization—resulting in lower quality of services.
- Lack of relevant, real-time, data-driven insights into operations are impacting asset health and performance. Ageing infrastructures, poor asset reliability, and limited visibility into asset conditions are resulting in fluctuating load patterns that ultimately impact power reliability.
- The generated insights get stored into silos, hampering utilization of OT-insights in building predictive models that can enable operational efficiencies. Continued usage of paperwork, manual processes, and siloed software systems regularly drive down operational efficiencies.
- Hazardous field operations increase human costs of inspection of assets located in dangerous terrains. Substitute inspection methods using helicopters are expensive and have also resulted in accidents. Meanwhile, vegetation management and changing regulations are a major cause of concern for the E&U industry as they bring in massive penalties.
- Frequent outages due to rising floods, storms, or wildfires continue to hamper reliability, lowering resilience and quality of service delivery—with more than 73% of outages lasting between a few minutes to more than an hour, at least.
The financial impact of outmoded operations, lack of real-time data-driven decision-making, and easily avoidable penalties, put massive strain on the bottom-line of utilities and oil & gas businesses.
- US Utilities spend up to USD54 Bn on ageing infrastructure annually—rerouting loads through less utilized infrastructure can reduce incidence of outages, while bringing down costs drastically.
- Field crew and helicopter driven asset inspection methods and maintenance of T&D lines cost US Utilities about $6Bn to $8Bn annually, on an average.
- Penalties from wildfires caused due to vegetation encroachment on high-power T&D lines can reach up to ~$30 Bn+ in damages, even leading to bankruptcy filings.
- Approximately 400 Utilities in the US market are invariably in the midst of grid modernization and will establish ADMS test labs in a phased manner in the next few years, thus underlining the importance of maximizing utilization of ADMS platforms, and precise and powerful ADMS testing approaches that can result in higher cost savings.
- For critical organizations such as manufacturing, healthcare, and data centers, outages can cost anywhere between USD100,000 to USD300,000 for a single hour of downtime. Small franchises are hit worst, with 70% reporting losses, therefore, financial losses are not just limited to the utilities and O&G industries, but to the end-customers as well.
For a primarily end-customer facing industry such as E&U, their customer experience score is one of the lowest globally. This results from a combination of factors that include, but are not limited to, poor service delivery, low reliability and resilience, a lack of digital interventions, and more frequent outages.
- 64% healthcare, 52% data centers, and 53% manufacturing organizations have expressed dissatisfaction at power reliability from their utilities service providers.
- Natural calamities causing outages or disruptions in a specific area in the T&D network, or along gas pipelines, can result in long outages and downtimes, impacting customer satisfaction.
- Customer expectations have risen dramatically, supported by socio-economic shifts in demographics and lifestyles—reliable “always on” electricity is a minimum qualifier.
- Real-time, data-driven, testing interventions ensure that the distribution and outage management systems need to be robust enough to stand up to stress from heavy load—based on real-time insights—thus delivering reliability and resilience in the face of dynamic environmental conditions.
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