From Chaos to Clarity: How Asset Performance & Predictive Maintenance is Rewriting the Rules of Industrial Efficiency

The cost of unplanned downtime for industrial manufacturers is estimated at $50 billion a year. Preventative maintenance isn’t enough. Boeing found that 85% of all equipment fails at random, regardless of the amount of preventative maintenance, while the Arc Advisory group reported that 82% of failures cannot be bypassed using traditional equipment monitoring.  It’s a challenging expense, but as artificial intelligence (AI) and smart tools evolve, predictive maintenance is becoming increasingly invaluable. 

Research published in the CIRP Journal of Manufacturing Science and Technology found that predictive maintenance could reduce scheduled repairs by up to 12%, decrease maintenance costs by up to 30%, and predict almost 70% of equipment failures before they occurred3. These numbers, particularly for asset-intensive operations, translate directly to bottom-line performance.  

Looking beyond the reaction 

Traditional maintenance approaches typically fall into two categories: reactive (fix it when it breaks) and preventative (scheduled maintenance regardless of an asset’s condition). Both have drawbacks – reactive maintenance leads to expensive emergency repairs and unplanned downtime while preventative maintenance often results in unnecessary work on functional equipment. 

What predictive maintenance adds to this mix is a fresh new layer of efficiency. It changes the paradigm entirely as it uses real-time data to determine the actual condition of equipment and predict the best time for maintenance to be performed. This approach minimizes downtime while still maximizing the lifespan of assets. It removes the risks and complexities that traditional methods add to the mix by turning maintenance into an active solution as opposed to a passive reaction. 

The data advantage: How information becomes insights 

Modern predictive maintenance platforms can transform operations by transforming real-time operational data to a platform of choice. These predictive tools empower technical teams to understand the data and the contextual information that relates to the data – this is information usually only provided to engineers and operators on the factory floor, but now in a more digestible format it can be used to make rapid and relevant decisions that can transform maintenance efficiency. 

Tools designed to provide companies with these insights need the right levels of data and insights to ensure they meet targets. When data scientists gain access to both machine data and operational context, they can develop more accurate predictive models that address real-world expectations and conditions rather than theoretical scenarios. 

But what does this mean for the organization? 

It means that the models and solutions developed to provide predictive maintenance schedules and data analytics are capable of delivering exceptional visibility into facilities, assets and systems. Effective predictive maintenance programs include: 

1. Comprehensive data collection – sensors are capable of capturing critical parameters that include vibration, temperature, pressure and other essential operational metrics. These metrics can be adapted to suit unique and specialized operating environments. 

2. Advanced analytics – machine learning algorithms can analyze the data to identify the patterns that precede equipment failure. 

3. Domain expertise – engineering knowledge that helps to interpret algorithmic findings in the context of real-world operations. 

4. Actionable insights – maintenance recommendations that fit into existing workflows and scheduling constraints. 

The right approach connects all these elements into a cohesive ecosystem that bridges the gap between operational technology and information technology. And that ensures information flows smoothly throughout the systems to ensure the data collected results in the correct maintenance action.  

It’s a Connected Future 

Predictive technologies are constantly maturing and evolving, particularly as technologies such as AI and ML continue to become more accessible, cost-effective and invaluable. Ongoing integration with other digital transformation initiatives will amplify their impact, especially when aligned with solutions such as digital twins – these virtual replicas of physical assets are increasingly combined with predictive maintenance to provide even deeper visibility into systems and asset performance. 

These evolutions and innovations represent a fundamental shift in how companies manage their assets as they move from isolated maintenance functions towards integrated operational excellence. Predictive maintenance, with its immediacy and relevance, is fast becoming a strategic necessity as it saves on costs, time and downtime while providing significant operational benefits. 

To get started on your operational journey towards predictive and scalable operations, contact Radix today at Connect with us | Radix.