Appsbroker CTS: Predictive Maintenance and Efficiency

Ryan den Rooijen, Chief Strategy Officer, Appsbroker CTS
Ryan den Rooijen, Chief Strategy Officer, Appsbroker CTS
Ryan den Rooijen, Chief Strategy Officer at Appsbroker CTS on how manufacturers can use predictive maintenance to enhance efficiency and results

What challenges are manufacturers facing today that are driving the need for greater efficiency?

Manufacturing organisations continue to face significant pressures. Shareholders are demanding higher profits, customers’ expectations are increasing, financing costs are high, supply chains remain volatile, and a potential recession looms on the horizon. In this climate, efficiency is not just a nice-to-have, it is the key to survival.

How can manufacturers increase efficiency? What are you seeing that is making a significant impact?

For industrial and manufacturing businesses, one of the best ways to unlock efficiency is through predictive maintenance and asset optimisation. These solutions help to address the unexpected equipment failures that can seriously hurt businesses.

When a critical machine breaks down, it is not just the immediate repair costs that sting. Production grinds to a halt, orders are delayed, and workforces must change their schedules to handle the fallout. This enforced down time can also incur customer penalties or regulatory fines. Predictive maintenance aims to disrupt this costly cycle.

What are some of the specific benefits of predictive maintenance?

By continuously monitoring the data generated by industrial assets, predictive maintenance solutions can spot the early warning signs of trouble. These models consider factors like vibration patterns, temperature fluctuations, or unusual energy consumption.

Rather than waiting for something to snap, you get a heads-up that a component may be wearing down or is on its way to failure. Data can also provide a valuable insight into the overall health of your physical assets.

The value is tangible, as businesses can schedule maintenance during planned downtime, minimising disruptions. Parts procurement becomes proactive, eliminating last-minute scrambles.

And ultimately, the workforce focuses on what they should be doing – adding value – not putting out fires. In fact, a recent McKinsey Report found that across industries, predictive maintenance can reduce downtime by up to 50% and increase asset lifespan by 30%-40%, as well as reduce energy usage.

Yet, arguably the most exciting aspect of predictive maintenance is the treasure trove of data there is to be unlocked. Modern industrial assets are packed with sensors, constantly collecting information.

Sadly, much of this data languishes unused. In one factory I visited, terabytes of data were collected but the end result was just four charts showing basic operational statistics such as cycle time.

This available data was subsequently used to recommend major operational improvements. If predictive maintenance had been used the factory could have benefited from high quality insightful data which would have helped them make far more informed decisions.

What other benefits can manufacturers gain from predictive maintenance? For example, are there any environmental benefits?

There certainly are. For instance, an unexpected equipment failure can have severe environmental consequences. Consider the recent case of Southern Water in the UK. The utility was hit with a record £UK90m fine for thousands of illegal sewage discharges.

By pinpointing potential leaks and failures, predictive maintenance solutions could have helped prevent such situations – protecting both the environment and the company's bottom line.

How can predictive maintenance support Industry 4.0’s vision for smart manufacturing?

With secure, scalable cloud technologies, deploying predictive maintenance capabilities is easier and more affordable than ever before. And by analysing data, you can spot patterns that reveal areas to refine operations for greater efficiency and output.

This is a foundational element of Industry 4.0’s vision for smart manufacturing. The more machines are used, the more insights you gain.

Companies that harness a data flywheel approach create a powerful competitive advantage. As assets become smarter, they generate more insightful data, leading to better predictions and further optimisations.

This frees up resources for more investment, which can be channelled into even more sophisticated assets, and the cycle reinforces itself. Ultimately, these investments can drive significant ROI both through cost reductions and increased productivity.


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