Fordel StudiosIndustrial Energy Consumption Analytics
This case study describes a real engagement. Client identity, proprietary details, and specific metrics are anonymized or approximated under NDA.
What needed
solving
No granular visibility into energy usage across 12 manufacturing units. Monthly utility bills were the only data source for energy management decisions. Equipment inefficiency and abnormal consumption events went undetected until billing cycles revealed aggregate anomalies.
Industrial energy metering data has several characteristics that make it different from typical IoT telemetry. Consumption patterns are equipment-specific — a press line has a different baseline and variation profile than an HVAC unit — so anomaly detection cannot use a single global threshold. Readings are affected by planned production schedule changes (a unit running at 60% capacity will have lower consumption than at 100%, which is expected, not anomalous), meaning the anomaly detection needed production schedule context to avoid false alarms during planned downtime or reduced-capacity runs. Sensor reliability varied across the 12 facilities: older meters had higher rates of null readings, stuck values, and occasional negative readings from meter rollover. The ingestion pipeline had to handle all of these without corrupting the time-series database or triggering false anomaly alerts.
How we
built it
- 01
Conducted a two-week sensor audit across all 12 manufacturing units to map energy consumption points, identify instrumentation gaps, and establish baseline consumption patterns before any optimisation work began.
- 02
Built a data ingestion layer that handled the heterogeneous sensor hardware already deployed across sites — different protocols, update frequencies, and data quality levels — normalising into a consistent time-series schema.
- 03
Implemented anomaly detection using statistical baselines per machine, per shift, and per production cycle, calibrated to distinguish genuine consumption anomalies from expected variation due to production load changes.
- 04
Designed the alerting system to surface actionable exceptions rather than threshold violations: equipment running in standby mode when it should be off, consumption patterns inconsistent with the current production schedule, gradual efficiency degradation over weeks.
This engagement built the full IoT data stack for a multi-site manufacturing energy management use case. Energy meters across 12 production facilities report to AWS IoT Core via MQTT at 1-minute intervals. A time-series ingestion pipeline writes normalized readings to InfluxDB. Grafana dashboards serve three user types: plant managers (unit-level consumption and efficiency metrics), maintenance teams (equipment-level anomaly alerts), and the central energy management function (cross-facility comparison and aggregate reporting). Anomaly detection runs on a rolling window basis and alerts maintenance teams when individual equipment consumption deviates from its baseline consumption profile by more than a configurable threshold.
What we
delivered
IoT sensor integration pipeline for real-time energy metering data, operational monitoring dashboard, and anomaly detection for equipment efficiency optimization. Provides per-unit, per-line, and per-equipment visibility at 1-minute granularity.
Measurable
outcomes
- Energy costs reduced 19% across the 12 monitored units in the first six months of operation.
- Sensor data uptime reached 99.1% with automated monitoring of sensor connectivity and data quality.
- Alert latency reached 4.2 seconds from consumption anomaly to operational notification, enabling real-time response to equipment issues.
“We were making energy management decisions from monthly utility bills. Having real-time sensor data and anomaly detection changed what was even possible — we caught equipment inefficiency that had been running for years without anyone knowing.”
— Head of Facilities Operations, Manufacturing ConglomerateReady to build
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