The hidden cost of inefficient machine control

(See Beckhoff at MachineBuilding.Live, 14 October 2026, on stand 214)

While a narrative of industrial decline persists, UK manufacturing has quietly become more productive. According to analysis by FourJaw Manufacturing Analytics, real manufacturing output has increased by six per cent since 2020, while output per worker has risen by ten per cent over the same period. Meanwhile, the UK manufacturing workforce has fallen by four per cent, suggesting this progress has been at least partly driven by greater adoption of automation, robotics and data-driven manufacturing.

Today, machines are expected to produce more with fewer resources, adapt quickly to changing production requirements and generate the operational data needed to support continuous improvement. Replacing motors and upgrading mechanical components is important here but is unlikely to unlock the next step change in efficiency alone. Achieving this requires intelligent control of the machines on the shop floor.

Control as the foundation of efficiency

Machine control plays a key role in determining how effectively a system operates. Even the most efficient hardware can underperform if control logic results in unnecessary movements, excessive cycle times or machines running when not adding value.

Inefficient sequences, poorly optimised motion profiles and excessive machine idle time can all introduce hidden costs for manufacturers. For example, a robotic arm used in a pick-and-place application may complete thousands of movements a day. However, if the robot travels further than necessary between operations or waits unnecessarily for other processes to complete, even those small inefficiencies can snowball into significant losses in cycle time and energy consumption.

By introducing more intelligent control architectures, manufacturers can optimise these processes at source. Integrated automation platforms allow motion, logic, safety, measurement and data acquisition to work together, resulting in a more coordinated approach to machine operation.

Rather than simply instructing a production machine or robot to complete a task, modern control systems can analyse how that task is performed and identify opportunities to improve performance. This may include reducing unnecessary axis movement, adjusting machine parameters dynamically or coordinating different parts of a production cell more effectively.

Continuously optimising with data

As more machine data becomes available, this creates fresh opportunities for manufacturers to optimise performance beyond traditional automation approaches, which often prioritise programming machines to complete predefined tasks efficiently. By capturing information from machines in real-time, engineers can better understand how their equipment operates and identify areas for improvement.

Beckhoff developed TwinCAT Analytics to help manufacturers analyse their machines' operational data and uncover patterns and inefficiencies that may not be immediately obvious. By examining factors such as cycle times, energy consumption and machine behaviour, TwinCAT Analytics' out-of-the-box algorithms can help identify bottlenecks, excessive idle time and inefficient operating stations, allowing manufacturers to make evidence-based decisions to improve processes and maximise equipment performance.

Importantly, this data supports condition monitoring strategies by highlighting changes in machine behaviour before they lead to unexpected issues. For example, by identifying changes in vibration, temperature or operating patterns, maintenance teams can address potential problems earlier, minimise unexpected disruptions and support more predictable production.

The recent analysis by FourJaw Manufacturing Analytics offers hopeful insight into the future of UK manufacturing, especially its productivity. That said, the pressure to improve efficiency will only intensify, and the focus must extend beyond individual hardware upgrades. The way machines are controlled, monitored and optimised is just as important in determining their overall performance. By combining intelligent automation with real-time data analysis, manufacturers can reduce unnecessary energy consumption, improve productivity and extend their equipment's operational life.