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In cooperation with Sensorfy we created an ebook that offers a complete framework for implementing predictive maintenance. But why is it important for manufacturers to embrace industry 4.0 in their assets and start implementing predictive maintenance? The developers of the ebook, Maurice Jilderda, Specialist Industry 4.0 at Perfact and Frank den Ridder, Business Development at Sensorfy, talk about it.

Rapid technological progress has boosted interest in predictive maintenance. “What we mean with predictive maintenance, is that maintenance is performed on time, based on predictions of imminent failure or quality degradation before these actually occur”, says Frank. “In this way, quality problems in operation are prevented and inconvenient and often expensive corrective maintenance, such as repair or replacement, is avoided. As a result overall equipment effectiveness is increased and downtime is minimized”, Frank continues.

Manufacturers acknowledge the potential of adopting industry 4.0 in their assets for making maintenance more efficient and effective. “What is needed to achieve this, is usually a combination of digitalizing business and operational processes with installing Internet of Things (IoT) networks and applying artificial intelligence (AI) technologies, such as machine learning”, complements Maurice.

But implementing predictive maintenance starts, as any other business activity, with a strong business case. That is why Sensorfy and Perfact have joined forces to better serve customers who are considering adopting predictive maintenance. Where Sensorfy develops technological solutions, Perfact, as consultancy organisation, helps organisations and people to improve demonstrably by combing knowledge, people and technology.

“The ebook we developed together, is a first step in adopting industry 4.0 into your assets. It addresses a complete process, from quick-scan to scale up, and divides it in three stages; Connect, Predict and Accelerate”, says Maurice. In the ebook we elaborate this framework we developed and illustrate its application within 3 concrete cases. “All cases succeeded in reducing downtime and improving efficiency. So we hope that this ebook offers the readers a start of introducing predictive maintenance”, closes Frank.

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