How AML Sheffield Identified About £15,000 in Annual Electricity Savings With Machine Level Monitoring

Electricity cost becomes harder to manage when facility level totals cannot show which machines, operating states, or behaviors are creating the expense. AML Sheffield wanted to reduce electricity spend and carbon impact across its CNC assets. The team suspected that machines remained energized during breaks and weekends, but it needed higher resolution data to measure the opportunity and guide practical changes.

AML paired SensPro Industrial IoT with SensIt Energy Monitoring. According to the public AML Sheffield Success Story, the project identified about £15,000 in yearly electricity savings, about £507 in potential savings per machine, and same day deployment and data processing.

Key takeaways

• The project identified about £15,000 in yearly electricity savings.

• Potential savings were measured at about £507 per machine.

• Deployment and data processing began on the same day.

Why facility energy totals were not enough

A monthly electricity bill can show whether total spend increased or decreased, but it cannot explain which production assets created the change. AML needed to see where power was being used on the shop floor and whether machines were consuming energy during periods with little or no productive output.

That distinction matters because energy cost per good part depends on the relationship between consumption and production. A machine that remains energized during a break can add operating cost without adding sellable output. The SensFlo guide to reducing operational costs in manufacturing places energy waste alongside downtime, scrap, rework, and underused capacity as a measurable plant floor cost lever.

Building machine level energy visibility

SensFlo instrumented priority machines with the SensPro IoT Box and SensIt Energy Monitoring. Sampling frequency was tuned by asset, ranging from milliseconds to minutes. This allowed the monitoring approach to reflect the type of activity each machine produced and the level of detail needed for review.

The implementation also used GCODE monitoring through an API to correlate electrical load with specific program steps. Data streamed to the cloud for a multiweek review by AML, Riscon Solutions, and SensFlo. The team identified idle periods, spikes associated with ESTOP release, and operator habits that could be addressed through straightforward policy changes.

This method connected energy data with production context. Instead of reviewing consumption as an isolated facility expense, AML could see when it occurred and how it related to machine activity.

How the data supported practical policy changes

The value of the project came from converting energy patterns into actions that people could apply. The team’s original concern centered on machines remaining energized during breaks and weekends. High resolution monitoring made those periods measurable.

The review also identified ESTOP release spikes and operating habits that contributed to consumption. Those findings gave AML a factual basis for coaching and policy decisions. The changes could be focused on observed behavior rather than broad assumptions about where energy was being wasted.

Manufacturers beginning with basic machine visibility can review FloControl Lite. Teams that need deeper operator workflows or controller information can review FloControl Pro, while organizations connecting machine data with broader systems can review FloControl Premier.

Connecting energy savings with production economics

The public AML case study reports about £15,000 in yearly electricity savings and about £507 in potential savings per machine. These figures show how smaller machine level opportunities can accumulate across a group of assets.

The bottom line effect comes from reducing electricity consumption that does not contribute to production. There can also be a capacity benefit when energy review reveals idle behavior that overlaps with utilization losses. The case study does not publish changes in throughput, revenue, or OEE, so those outcomes should not be assumed.

The clearest financial model compares the cost of monitoring with verified energy savings. If the same monitoring data also supports utilization, downtime, or maintenance decisions, those benefits can be evaluated separately using measured results. The SensFlo ROAI Calculator can help manufacturers model the value of time and output improvements alongside direct cost savings.

What manufacturers can learn from AML Sheffield

AML’s experience shows that energy improvement begins with asset level context. Facility totals provide a financial signal. Machine level data explains the operating behavior behind that signal.

A practical starting approach is to instrument the machines with the highest energy use, longest energized periods, or greatest production importance. Once the data is available, teams can compare working periods, breaks, weekends, startup behavior, and program steps. That review can reveal policy changes that reduce cost without requiring new production equipment.

Frequently asked questions

How did AML Sheffield reduce electricity costs?

AML Sheffield used SensPro Industrial IoT and SensIt Energy Monitoring to measure electricity use on priority CNC assets. The team correlated electrical load with machine activity and GCODE program steps, then reviewed the data over multiple weeks. This identified idle periods, ESTOP release spikes, and operator habits that supported practical policy changes.

How much electricity savings did AML Sheffield identify?

The public SensFlo case study reports about £15,000 in yearly electricity savings and about £507 in potential savings per machine. These are published project figures. The exact value for another facility will depend on its energy rates, machine mix, operating schedule, and current idle energy behavior.

What is machine level energy monitoring?

Machine level energy monitoring measures electrical consumption at the individual asset level and connects that information with operating states or production activity. It helps teams determine whether energy is being used during productive cycles, idle periods, breaks, weekends, startups, or other machine conditions that deserve review.

Can energy monitoring be deployed quickly?

The AML Sheffield case study reports same day deployment and data processing. The implementation used SensPro IoT Boxes and energy monitoring on priority assets. Actual deployment time for another manufacturer will depend on machine count, connectivity, sensor requirements, and the depth of system integration.

Manufacturers that want to discuss energy monitoring or machine visibility can contact SensFlo or compare the available FloControl plans.