There is an increasing need for efficient cost optimization, green energy, and integrating decentralized renewables in the current energy industry.
FREMONT, CA: Organizations are gathering an overwhelming amount of data in multiple asset classifications, resulting in a complex company structure. With the ongoing shift towards integrating decentralized renewables and other projects, there is a constant requirement for one consistent, common enterprise asset management (EAM) data model structure. Even if firms are using the same asset management tools, various definitions and nuances make integration an insurmountable hurdle.
Therefore, the priority is to clean up the existing data model. This could be attained with a customized machine learning solution that analyzes several patterns across different data models and consolidates everything into a single model. It becomes possible to determine which processes and present and future products are vital to the portfolio. Finally, with trustworthy data throughout the network, firms can use the ability of their entire asset landscape by deploying a holistic EAM suite that effectively uses accompanied cloud solutions. This will allow managers in the electric utility segment with much quick and more efficient decision-making operations.
As the world becomes more dynamic and data-driven, the energy sector must change influencing factors and framework conditions through different digitization operations. Essentially, all asset-intensive sectors face disruptive hurdles from changing business models and higher cost pressures. The competition in this segment is even stronger than ever. Nevertheless, new revenue streams can be generated by deploying novel business models like maintaining micro-grids and renewable energy sources for prosumers, specifically those implemented by retail households and small industries.
Over the past years, the evolution of maintenance routines is also revamping the energy sector, with the major objective to ensure asset uptime and raise profitability by avoiding unplanned and unwanted maintenance in the energy supply chain. Decision making around maintenance strategies for a firm’s asset base can be spontaneous, driven by intuition rather than facts. With all this data at hand, it is feasible to calculate risk and determine alternative maintenance strategies. Strategies can include condition-based or predictive maintenance.