Forecast with confidence—even when conditions won’t sit still.
Production forecasts often struggle to keep pace with operational complexity and changing conditions. This solution applies advanced analytics and machine learning to improve forecast accuracy across assets and operating regions.
Frame delivers the solution through data engineering, analytics, and AI platform implementation services that support scalable models, scenario analysis, and continuous forecast improvement.
Energy and industrial companies rely on manual forecasting methods that cannot account for dynamic operational conditions, shifting market demand, or variability in upstream and downstream processes. This leads to inaccurate production plans, inefficient resource allocation, and missed revenue opportunities.
Forecasting models that depend heavily on historical averages and human intuition.
Limited ability to incorporate real-time plant conditions, maintenance schedules, or supply constraints.
Significant variance between planned and actual production output.
Inability to model complex interactions between assets, feedstock quality, and operational events.
Lack of unified forecasting processes across business units or production sites.
Frame builds AI-driven forecasting models that integrate operational, commercial, and market data into a unified lakehouse. These models simulate production scenarios, optimize planning decisions, and continuously learn from real-world performance to improve accuracy over time.
Lakehouse integration of SCADA, historian, ERP, maintenance, supply chain, and market datasets.
Machine learning models that predict production output under multiple operating conditions.
Simulation engines that evaluate ‘what‑if’ scenarios such as feedstock variation or equipment downtime.
Automated feature engineering to capture seasonality, asset behavior, demand signals, and operational constraints.
Forecasting dashboards that visualize confidence ranges, drivers of variance, and recommended plan adjustments.
AI‑enhanced forecasting improves plan accuracy, increases operational agility, and strengthens commercial decision‑making. Organizations can proactively allocate resources, optimize production schedules, and better align operational output with market demand.
Higher forecasting accuracy and reduced variance between planned and actual production.
Optimized operational planning that reduces bottlenecks and improves resource utilization.
Increased revenue through improved alignment of production output with market demand and pricing.
Faster response to operational disruptions through scenario‑based planning.
A scalable forecasting model that continuously improves as new data is incorporated.
Our Approach – Minimize risks and maximize return.
Frame delivers with confidence through proven, repeatable methods that reduce cost overruns, project delays, and operational uncertainty. Our AI-enhanced delivery model helps to ensure consistent execution across cloud-native, analytics-driven architectures—while hyper-local teams and nearshore scale drive efficiency without compromising quality. Frame’s prebuilt accelerators, reusable IP, and tight feedback loops fast-track speed-to-value while maintaining full transparency and shared accountability every step of the way.
Discover & Design
Start with business goals, identify the right opportunities, define the value and chart a clear plan.
Build & Modernize
Extend and modernize your data and architecture with scalable platforms and secure pipelines while complimenting your existing systems.
Scale & Evolve
Scale high-impact AI use cases, establish sustainable governance, and evolve with frameworks that support agility, adoption, and long-term value.
Value Delivery
We don’t just modernize—we deliver outcomes you can measure: faster cycles, smarter operations, and durable solutions that evolve with change.
