SMART BUILDINGS


From energy data to factory efficiency


Rising energy costs are driving manufacturers to look more closely at how their factories consume power. But while most understand site-wide usage, fewer know where energy is being used or wasted at machine level. Here Beth Ragdale explains how real-time analytics can improve efficiency and reduce operational costs.


Beth Ragdale www.beckhoff.com/en-gb


T


Software manager at Beckhoff UK


it may indicate that equipment is not entering standby mode correctly, or at all. On a packaging line, for example, infeed conveyors or rotary tables may continue to run when the machine is blocked further down the line. Equally, a motor that suddenly requires more power than usual may be signalling the early stages of mechanical wear. The same principle extends beyond


he UK government has announced the British Industrial Competitiveness Scheme, which will help to


cut energy bills by up to 25% for more than 10,000 manufacturers in early 2027. This will be welcomed by the companies, but it shows how serious the energy issues are at the moment. Without visibility into individual


assets and processes, manufacturers can spend thousands on electricity without knowing which machines are operating inefficiently, where maintenance issues are emerging or whether equipment is consuming power when it is producing nothing at all.


The biggest opportunity lies in


treating energy as another source of operational data, rather than simply another utility bill. If you know how much energy a machine should use to produce a part, any deviation tells you something. It could be an overworked motor, a compressed air leak, a vacuum pump running continuously or simply a machine that’s still consuming power when it should be idle. This shift from measuring total


energy consumption to analysing energy at machine level is changing how manufacturers approach both efficiency and maintenance.


Beyond the meter Real-time analytics allows manufacturers to build a performance profile for every machine. Instead of simply recording how much electricity a factory consumes over a day or month, engineers can compare energy use against production output, operating conditions and machine health. If a production line continues to draw significant power during downtime,


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electrical consumption. Modern automation platforms can combine energy data with other signals from the machine, such as torque, speed, vibration, temperature, valve states, flow rate, environmental conditions and machine state to provide a far richer picture of machine performance. For example, increasing vibration alongside higher energy consumption often points towards equipment operating outside its normal condition. Rather than waiting for a failure, manufacturers can identify these trends early and investigate before production is affected. Compressed air is another example.


Often described as one of the most expensive factors in manufacturing, even small leaks can waste significant amounts of energy. By monitoring pressure, flow and machine state in real time, manufacturers can detect changes that suggest leaks or air being supplied during idle periods, reducing both energy use and operating costs.


Whole factory effi ciency Energy optimisation does not stop at the production line. While manufacturers often focus on improving machine efficiency, building management can have an equally significant impact. Heating production areas while loading doors remain open, lighting unused areas, running extraction when the process is inactive, or failing to recover excess heat all contribute to unnecessary energy consumption. Modern automation systems


increasingly allow manufacturers to combine machine data with building management information. Instead of treating production equipment and facilities as separate systems, they become part of a single energy strategy.


Smarter maintenance Energy analytics also supports predictive maintenance, one of the fastest-growing applications of industrial data. For manufacturers producing high-


volume consumer goods, even short periods of downtime can be extremely expensive. Rather than reacting after equipment fails, predictive maintenance uses operational data to identify developing problems before they stop production. Engineers can compare how a machine behaves over time. A drive that needs more current for the same movement, a gearbox that is running hotter than normal, or a fan that shows increasing vibration can all suggest that equipment is beginning to deteriorate. Instead of discovering a worn bearing after a breakdown, manufacturers can spot gradual changes in performance, schedule maintenance during planned shutdowns and avoid unexpected production losses altogether. The result is not only greater


reliability but also improved energy efficiency, as machines operating within their intended parameters typically consume less power than poorly performing equipment.


Data into decisions The challenge for many manufacturers is no longer collecting data. Modern factories generate enormous volumes of information every second. Now, it’s about how to make sense of it. Artificial intelligence is increasingly


becoming the link between raw industrial data and practical decision- making. Rather than engineers manually reviewing thousands of measurements, machine learning algorithms can identify patterns across energy use, compressed air demand, temperatures, vibration, motion profiles and production performance.


Those insights help manufacturers


understand relationships that would otherwise remain hidden and create increasingly accurate predictions of future performance. Looking ahead, manufacturers will


move towards creating a digital profile for every machine. By combining operational history with live analytics, each asset will effectively develop its own performance passport, allowing operators to understand exactly how it should run for maximum efficiency, product quality and minimum energy consumption.


Visibility drives effi ciency While sustainability targets and environmental reporting continue to influence investment, commercial pressures remain the primary driver for many manufacturers. Rising energy prices have made efficiency impossible to ignore. The companies that gain the


greatest advantage will not necessarily be those investing in entirely new equipment. Instead, they will be those extracting more value from the machines they already have by understanding how they perform in real time. Energy analytics provides that


visibility. Once manufacturers know where energy is being used, why consumption changes and how operational decisions affect efficiency, they can make improvements that reduce costs, improve reliability and support long-term sustainability. As manufacturers continue to


balance rising energy costs with productivity and sustainability targets, data will become one of their most valuable assets. By combining real-time analytics with intelligent automation, manufacturers can uncover hidden inefficiencies, make better operational decisions and reduce energy consumption without compromising output. ■


EIBI | SEPTEMBER 2026


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