Solution
Energy Optimization
Measure energy consumption at the machine level — tracking kWh, voltage, current, and power factor across shifts, lines, and production runs — to identify inefficiencies, detect abnormal usage, and build a credible case for targeted efficiency investments.
Business challenge
Energy costs are significant in industrial operations, but most factories only track energy at the plant or meter level. That level of aggregation hides the real source of waste — individual machines running inefficiently, consuming power during idle states, or exhibiting abnormal electrical behavior that indicates early-stage equipment degradation. Without machine-level granularity, energy management becomes a finance exercise rather than an operational one. Teams cannot identify which asset is responsible for a spike, cannot compare energy consumption across shifts for the same machine, and cannot correlate energy anomalies with production events. Cost reduction initiatives stall because there is no evidence to justify targeted action on specific assets or production patterns.
Factobrain solution pattern
Factobrain connects to energy meters deployed at the machine level and captures electrical parameters — kWh consumption, voltage, current, and power factor — at regular intervals throughout every shift. This data is structured against your production hierarchy, making it possible to compare energy usage by machine, by line, by shift, and by production run. Time-based charts reveal patterns: baseline consumption during normal operation, spikes during specific production sequences, abnormal draw during idle periods, and progressive drift that signals mechanical degradation. Configurable thresholds trigger alerts when any machine exceeds its expected energy profile, allowing operations and maintenance teams to investigate before a cost or quality problem develops. Historical trend data supports sustainability reporting and efficiency investment decisions with actual, asset-level evidence.
ROI signals to monitor
- Reduced energy waste by identifying machines with abnormal or inefficient consumption patterns
- Faster detection of energy anomalies that signal mechanical issues or electrical faults before they escalate
- Ability to compare energy use across shifts for the same machine — revealing operator or process differences
- Structured data to support sustainability reporting and energy reduction commitments with asset-level evidence
- Better investment decisions for efficiency upgrades, grounded in actual machine consumption history
- Lower operating cost per unit produced by connecting energy data to production output and shift performance
Move from use case to deployment plan
Book a demo to map this use case to line selection, KPI baselines, and rollout milestones.