Common Use Cases
As the modernization of infrastructures continues, with smart grid technologies and IoT sensors reaching 100% penetration, we can combine historical and customer data sets with examining smart meter data and real-time energy usage, including time-series analysis for such things as energy disaggregation; breaking down voltage, current, and status of appliances and other electronics information. Harnessing these real-time datasets and making use of the information flow, along with incorporating traditional sources of information through operations, can generate tremendous value for teams and organizations, without an enormous investment. SFL can enhance systems and improve the quality of data leading to reduced costs and increased customer satisfaction.
- Automated outage management system reports
- Aggregate social media, online forms, customer calls, service tickets
- Video, photo, and mapping; anomaly detection and maintenance
- Integrating and deploying cloud-based solutions across business units
Predictive maintenance techniques can incorporate sensor and machine data to help determine the condition of in-service equipment and infrastructure. Machine learning provides a strong complementary approach to maintenance planning and infrastructure upgrades by analyzing data sets of machine performance and modeling environmental variables at scale, generating actionable predictions of failure and condition analysis.
Access to the most accurate and up-to-date information can optimize business functions and improve customer experience. We can develop and integrate analytics platforms and tools to enable the real-time examination of grid, meter, and weather sensor data. By using new machine learning and time-series techniques the massive volumes of data can efficiently be used to detect patterns in consumption, optimize energy distribution, or detect failures and anomalies.
Strategic assets can be built by aggregating the experience of the organization; each customer, supplier, partner, service record, complaint, or transaction provides the opportunity to develop automated frameworks, predict problems, and reduce inefficiencies. Data from media, forms, calls, and records can be aggregated for systemic review. SFL can point out the challenges of big data-driven management and processing, building solutions to enhance marketing, sales, customer service, and other business functions.
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Our consultants and engineers have worked on a number of use cases, collecting and analyzing information to transform business practices. If there's custom solution you're interested in, contact us for more information. Technology moves fast, let's build sustainable systems.