Analytics in Energy & Utilities

• Tracking grid reliability metrics (e.g., SAIDI, SAIFU, CAIDI).

• Calculating transmission and distribution losses.
• Calculating and predicting the share of energy from DERs and assessing their impact on the grid.
• Issue root cause analysis (e.g., for voltage fluctuations, outages).
• Predictive maintenance for transformers, generators, power towers, and other grid assets.
• Load demand forecasting and scenario modeling.
• Dynamic pricing optimization.
• Automated network load adjustment.
• Instant alerts on urgent and potential issues, such as outages, congestion, and capacity constraints.

• Water supply monitoring (e.g., energy consumption per unit of water produced, NRW percentage, water loss rate).

• Real-time monitoring of water supply networks across various parameters (e.g., flow rates, pressure levels).
• Leak detection and localization with prompt alerting.
• Water demand forecasting and what-if modeling.
• Predictive maintenance of network assets, such as pumps, valves, and reservoirs.

• Real-time monitoring of heating network performance across multiple parameters (e.g., load, capacity, heat consumption, flow rates).

• Predictive maintenance for heat exchangers, boilers, pumps, valves, and other equipment.
• Detecting potential heat loss reasons (e.g., leaks, inadequately sized steam traps) based on the analysis of temperature differentials, flow rates, and insulation levels.
• Heat demand forecasting based on identified heat consumption patterns and weather modeling results.
• Real-time alerts on anomalies (e.g., sudden fluctuations in temperature or flow rates).

Upstream sector analytics

• Estimation of oil and gas reserves at the potential drilling location.
• EUR forecasting.
• Real-time monitoring of oil & gas production.
• Remote equipment monitoring and predictive maintenance.
• Production rates forecasts.

Midstream sector analytics

• Continuous monitoring of storage tanks and distribution pipelines with alerts on issues like leaks or overfills.
• Analytics of the supply chain, energy consumption, and inventory management.

Downstream sector analytics

• Process efficiency analytics for petrochemicals production and refinery.
• Quality control.
• Monitoring transportation safety and efficiency.
• Predictive maintenance of shop floor and transportation equipment.
• Supply-demand forecasts and what-if models based on historical demand trends, weather conditions, and market changes.

• Specialized monitoring and analytics solutions for coal, natural gas, hydroelectric, nuclear, geothermal, wind, solar, biomass, and CHP plants.

• Analyzing energy and fuel consumption patterns within the power plant.
• Analyzing the condition and performance of assets (e.g., boilers, heat exchangers, turbines, reactors).
• Predictive asset maintenance.
• Combustion optimization insights (for coal power plants).
• Continuous safety analytics (e.g., tracking temperature and pressure within systems, gas concentration and radiation levels).
• Monitoring emission levels.
• Instant alerting on issues with automated emergency shutdowns.

• Monitoring asset KPIs, e.g., asset uptime, maintenance costs as a percentage of asset value, MTBF, MTTR.

• Continuous analytics of asset performance and state with insights into utilization optimization.
• Predictive and preventive asset maintenance.
• Asset lifecycle cost analysis.
• Identifying root causes of asset inefficiencies and failures (e.g., operating errors, environmental factors).
• Predicting the end of assets’ useful life.
• Immediate alerting on asset performance issues.

• Tracking the emissions of air pollutants (CO2, NOx, SO2).
• Water quality analytics (e.g., pH levels, turbidity, contaminants).
• Analyzing data on waste generation, composition, and disposal.
• Monitoring noise levels.
• Assessing the environmental impact of potential projects and policies through what-if scenarios.
• Comparing the level of environmental impact against thresholds set by internal policies and regulatory authorities (e.g., EPA, FERC).
• Sending instant non-compliance alerts.

• Multidimensional customer segmentation (e.g., by demographics, location, industry, company size).
• Identifying customer- and segment-specific resource usage patterns, such as peak demand periods and seasonal variations.
• Customer lifetime value analysis.
• Customer churn prediction.
• Customer sentiment analysis based on data from surveys, review platforms, and call transcripts.
• Performance insights for demand-side management (DSM) programs to optimize program design, target audience selection, and incentive structures.
• Personalized energy usage and cost analytics for customer portals and mobile apps.
• AI-powered recommendations on customer-specific billing options (e.g., fixed monthly payment plans, time-of-use pricing).

• Analyzing costs across all business directions (e.g., planned costs of maintenance and business development).
• Segmenting costs by labor, maintenance, fuel, overhead, and other expense categories.
• Revenue analytics with revenue stream attribution (e.g., by customer segment, service offering).
• Profitability analytics.
• CAPEX and OPEX budget analysis.
• Financial risk management.
• Insights into the minimization of tax liabilities (e.g., pinpointing relevant tax credits and incentives).
• Payroll analytics with employee performance vs. compensation benchmarking.
• Financial planning and forecasting.
• Financial performance and financial compliance reporting.
• Scenario analysis for optimized energy trading operations.

• Identifying cases of tampering, meter bypass, and unauthorized access by detecting anomalies and outliers in meter readings.
• Detecting fraudulent customer behavior through the analysis of resource consumption patterns, service requests, and payment history.
• Identification and monitoring of at-risk customer segments.
• Identifying discrepancies in billing data.
• Instant alerts on fraud detection.

• Continuous monitoring of inventory levels (e.g., fuel, spare parts, chemicals).
• Inventory demand forecasting based on asset condition and performance, historical demand, and environmental data.
• Tracking and optimization of logistics operations to ensure timely inventory availability.
• Supplier performance insights.
• Monitoring the adherence of SCM processes to the required ESG regulations.
• What-if scenarios of delivery routes based on historical SCM data.

• Tracking recruitment and hiring KPIs (e.g., time-to-fill, cost-per-hire).
• Identifying the sources of high-quality hires (e.g., referrals, job boards).
• Evaluating employee performance against set goals and other internal parameters and pinpointing top-performing employees.
• Workforce resource forecasting based on past and current service requests and employee vacation schedules.
• Tracking employee participation in training and development activities and evaluating their impact on productivity.
• Identifying skill gaps based on data about employee certifications and training.
• Tracking employee attrition rates and providing insights for efficient retention strategies.
• Identifying trends and root causes in safety incident data.
• Insights into employee satisfaction and engagement based on survey data analysis.