Energy Costs Are Forcing Energy Management Up The Agenda

In the current geopolitical climate, effective energy management for industrial firms has shifted from a valuable capability to something approaching an operational necessity. Soaring energy prices currently leave the European market the most exposed – with the region still recovering from previous shocks – but other geographies are beginning to feel the strain (see Verdantix Fractured Foundations: The factors driving global instability). The pressure is also highly industry-specific, with mining and metals, chemicals, pharmaceuticals, and electronics operations among those most impacted by rising costs. Industry leaders consistently highlight energy usage and management as a key priority, with 85% reporting that reducing energy consumption at an asset and plant level is a high or medium priority (see Global Corporate Survey 2026: Industrial Transformation Budgets, Priorities And Tech Preferences).

As such, energy management software (EMS), while always valuable, has gained increasing importance in recent years. Historically these tools’ main use case has been monitoring and reporting, rather than actively influencing production decisions that impact margins. Now, embedding energy management within manufacturing operations management (MOM) platforms gives industrial firms a way to contextualize energy data and extract greater operational value from this information. Operators can identify machine health issues and process changes that influence underlying energy consumption and therefore cost. Decision-makers can then act quickly on this information within a closed-loop system.

Integrating this functionality within MOM platforms also reduces the complexity of traditional standalone systems. Instead of operating in isolated applications, energy data become part of the single operational backbone of production alongside quality, scheduling, maintenance and production management. This unified data foundation helps eliminate silos and allows energy performance to be analysed directly in the context of operational decisions.

Predictive analytics further amplifies the value of this embedded capability. MOM solutions can detect anomalies that signal system failures or inefficiencies and automatically adjust more energy-intensive elements of production processes. Integration with maintenance systems also strengthens predictive maintenance strategies, as abnormal energy draw can indicate emerging reliability issues and feed contextualized data directly to maintenance teams.

Meanwhile, automated load shedding embedded into the execution layer can be used during periods of energy price spikes, throttling non-essential assets rather than forcing operators to monitor dashboards and reactively adjust processes. Organizations can address ‘phantom loads’, with MOM platforms identifying these moments and automatically shutting equipment down when idle. Combined with peak shaving and production load levelling, the ability to coordinate production around off-peak windows and dynamically adjust production sequencing can significantly improve energy efficiency while maintaining throughput.

At the end of the day, soaring energy costs cannot be fully solved by energy management software alone. The issue runs deep within the cost structures of many industrial operations. However, these tools can act as the critical lifeline between shutting down a plant or riding out the latest market shock.

Energy management might not be the first module industrial firms pursue when deploying MOM platforms, but it is something that should be increasingly apparent on organizations’ radars. In an environment of tightening margins and persistent volatility, the ability to directly link energy consumption with operational decision-making is progressively more important. For more in-depth discussions on these issues, schedule an analyst inquiry call.