Using Digital Twins to Optimize WAGES in Manufacturing
Water, Air, Gas, Electricity, and Steam are among the highest cost and sustainability drivers in manufacturing. Together, they form WAGES, a category that directly affects operating expenses, emissions reporting, and regulatory compliance.
Most manufacturers can see total WAGES consumption. Far fewer can explain why it changes, where inefficiencies originate, or which actions will reduce usage without disrupting production. This is where digital twins for sustainable resource management deliver immediate value.
Why WAGES is the right starting point for digital twins
WAGES data already exists in most plants through meters, historians, and utility systems. Unlike broader sustainability initiatives, WAGES optimization is closely tied to daily operational decisions.
It is a strong starting point because:
- Consumption is continuous and measurable
- Variability often exists under identical production conditions
- Improvements generate both cost savings and sustainability gains
In many facilities, the same product produced on the same line can show significant differences in energy or water use. Without operational context, this variation remains hidden.
What a digital twin changes
A digital twin creates a contextual model of resource consumption. It connects WAGES data with production context, such as product, line, operating mode, and shift.
This allows manufacturers to:
- Define expected WAGES consumption for each production scenario
- Detect deviations as they occur
- Compare current performance to the best demonstrated performance
- Quantify improvement potential before taking action
Instead of asking whether consumption is high or low, teams can assess whether it is appropriate for the current operating conditions.
Moving beyond real-time monitoring
Traditional monitoring answers a single question: how much are we using right now?
A digital twin supports deeper operational decisions:
- Is this consumption normal for this product and line?
- Which process variables are driving excess usage?
- What will happen over the next hours if nothing changes?
- Which adjustments reduce consumption without impacting quality or throughput?
In more advanced deployments, digital twins can support automated or semi-automated control actions, such as adjusting setpoints, shifting energy-intensive steps, or reducing utility use during non-production periods.
Supporting sustainability reporting and compliance
WAGES optimization directly supports sustainability and regulatory requirements by linking plant-floor actions to enterprise metrics.
With a digital twin, manufacturers can:
- Trace operational decisions to emissions and water footprint outcomes
- Reduce manual data collection and reporting effort
- Demonstrate continuous improvement rather than periodic corrections
As reporting requirements tighten across regions, this operational traceability becomes increasingly important.
The business impact
Digital twins for WAGES optimization deliver value across operations:
- Lower utility costs through reduced waste and variability
- More predictable performance and fewer operational surprises
- Clear accountability for sustainability outcomes
- Better decisions under production, cost, and regulatory pressure
Most importantly, sustainability becomes part of normal operations rather than a separate reporting exercise.
WAGES optimization is where sustainability becomes operational. A digital twin transforms utility data into actionable insight, helping manufacturers reduce resource consumption while maintaining production goals. For organizations looking to move from visibility to results, this is often the most effective place to begin.
This article is based on insights shared during the IIoT World Manufacturing & Supply Chain Day panel discussion “Building a Digital Twin for Sustainable Resource Management”, featuring:
The session was moderated by Hamish Mackenzie.