IoT and EAM: Revolutionizing Energy Efficiency in Metalworking Plants

How metalworking plants use IoT and EAM to cut energy, downtime and emissions.

Why metalworking plants need IoT-enabled energy and maintenance visibility

Metalworking plants - whether they focus on forging, machining, casting or surface treatment - sit at the intersection of high energy use, demanding customers and tightening sustainability expectations. Furnaces, ovens, compressors and heavy machinery consume large amounts of electricity and gas, while unplanned downtime can halt entire value chains serving automotive, aerospace, rail or construction sectors.

At the same time, European manufacturers face rising energy prices, stricter environmental regulation and growing pressure from OEMs to document the carbon footprint of components. In this context, many plants still operate with limited visibility. Energy is tracked via monthly invoices and occasional meter readings, equipment health via local alarms and operator experience, and maintenance plans via spreadsheets or basic CMMS tools.

The result is familiar: unexplained energy spikes, recurring equipment failures, compressed maintenance windows and difficulty building robust business cases for upgrades. IoT-based energy monitoring and integrated Enterprise Asset Management (EAM) offer a practical path out of this blind spot.

By installing sub-meters and sensors on critical circuits and machines, manufacturers can see, in real time, where energy is going, when assets are idling unnecessarily and which conditions precede failures. Early adopters in forging and metalworking have shown that simply visualising consumption per line, product or shift can uncover double-digit savings opportunities without major capital projects. When this telemetry is connected directly to an EAM platform, insights turn into action: anomalies create work orders, improvement projects are tracked and savings are documented in a way that speaks to both plant managers and CFOs.

For companies operating multiple sites, the benefits multiply. A shared SaaS platform like Nextbitt allows corporate engineering and sustainability teams to benchmark plants, replicate successful configurations and ensure that local initiatives contribute to group-level CSRD and ISO 50001 or ISO 55001 strategies. Instead of isolated “smart factory” pilots, organisations can build a coherent, multi-year roadmap that combines digitalisation, energy efficiency and reliability into a single, measurable programme across Europe and beyond (Nextbitt smart factory platform overview).

Designing an IoT-enabled energy and maintenance architecture for metalworking

To unlock these gains, metalworking firms need an architecture that links machines, utilities and maintenance workflows in a coherent way. The starting point is a clear map of where energy is used and where failures hurt most. Typical hotspots include furnaces and ovens, compressed air systems, CNC machine spindles, paint and powder-coating lines and large motors for pumps and fans. A layered IoT design helps bring order: at the sensing layer, energy meters and sub-meters are installed on main feeds and critical circuits, complemented by sensors for vibration, temperature and pressure on key assets.

Above the sensor layer, a robust connectivity and data platform is essential. Depending on the plant layout and IT policies, that might combine industrial Ethernet on production lines with wireless networks in harder-to-reach areas. Data from meters, PLCs and standalone sensors flows into a central time-series database or IoT platform, where it is normalised and enriched with metadata such as asset, line, product and shift.

EAM sits on top of this data foundation as the orchestrator of action. Instead of treating IoT insights as standalone dashboards, anomalies in energy consumption, vibration or temperature become triggers for maintenance and improvement work orders. For example, persistent baseload consumption outside production hours on a furnace line can automatically generate an investigation task; a trend of rising vibration on a critical motor can initiate a condition-based inspection before failure. Nextbitt’s integrated model - combining multi-site asset registers, IoT telemetry and sustainability analytics - matches this need, helping metalworking firms standardise asset data, centralise alerts and ensure that every improvement opportunity is captured, prioritised and tracked (Nextbitt platform overview).

Scaling IoT and EAM across metalworking and heavy manufacturing networks

Rolling out IoT-enabled energy and maintenance across a network of metalworking plants requires a pragmatic roadmap that balances ambition with the realities of legacy equipment and tight production schedules. The first phase is to select one or two pilot sites and concentrate on a limited set of high-impact use cases, such as furnace optimisation, compressed air leak detection and critical rotating equipment monitoring.

For each use case, you define clear KPIs - energy per tonne, unplanned downtime hours, scrap rate - and baseline them using historical data. Once pilots demonstrate savings and improved reliability, the next phase is standardisation and scale. Develop a reference design for sensors, data models, dashboards and EAM workflows that can be replicated plant by plant.

Governance and change management are the final pieces. Plant managers, maintenance teams and sustainability leaders must agree on how IoT alerts are triaged, who can change thresholds, how savings are validated and how successes are recognised. Establishing a multi-plant steering group that reviews KPIs quarterly, shares best practices and aligns investments with CSRD and ISO 50001 or ISO 55001 objectives helps keep the programme on track.

Platforms like Nextbitt make it easier to support this governance by providing a shared view of assets, consumption and work orders across sites, so leadership can see which plants are leading, which are lagging and where targeted support or investment will deliver the greatest return.

Over time, metalworking networks can evolve from basic visibility to advanced optimisation - using AI to forecast energy demand, simulate the impact of schedule changes or recommend the best timing for refurbishments and replacements. In doing so, they not only cut costs and emissions but also build a more resilient, data-driven industrial base that can adapt to volatile energy prices, stricter regulations and customer expectations for low-carbon products.