Firing on all cylinders: The role operational reliability must play in industry’s sustainable future

Posted: July 15, 2024

Three weeks ago, I experienced an unplanned maintenance event firsthand. It was an expensive lesson in the benefits of predictive maintenance, but it also gave me a way to explain the relationship between industrial asset reliability and sustainability in industry. So, it wasn’t all bad. What happened was this:

The warning light on my car’s dashboard clicked on. When I saw it, my stomach sank. I knew what the next few days would look like. My local mechanic would need to run diagnostics. They would need to order the correct part to complete the repair. I would need to wait for the part to arrive.

I’d burn liters of fuel and expel emissions traveling to and from the repair shop in a rental car. It would be a hassle, and I’d waste time that I might have otherwise spent doing something productive. Because my car doesn’t have predictive maintenance capabilities, I would have to use whatever part the repair shop ordered, whether it was sustainably sourced or not. In short, I would feel powerless throughout the entire process.

Of course, industrial assets and operational processes are far more complex than my car’s four-cylinder engine. But, after my 10+ years working in asset performance management, it was hard to ignore the similarities: All the wasted time and resources, the unnecessary costs (both monetary and environmental), the inefficient processes and supply chain difficulties (I’m still waiting for the part to come in).   

Often, at industrial software conferences and webinars on optimizing industrial operations, I hear industry experts and insiders tackle asset reliability and sustainability as separate topics. To be blunt, they’re wrong. Industrial reliability and decarbonization are two sides of the same coin.

When organizations minimize unplanned maintenance, they mitigate a set of compounding environmental impacts, and, just like my ordeal at the car mechanic, a single unplanned maintenance event might require a replacement part that ends up coming from a less environmentally friendly supplier.

Unplanned disruptions also often require resource-intensive production reboots, which translate to increased emissions, water consumption, and transportation needs, adding to an organization’s ecological footprint. Rush shipments of parts and equipment contribute to further environmental costs, worsening the toll on our planet.

For specific industries, the stakes are even higher. In sectors like oil and gas, for example, an asset failure can have catastrophic environmental consequences. Even limited failures lead to inefficiencies caused by underperforming assets or the ripple effects of production interruptions on waste generation.

Or, in industries like mining where operations often take place in remote and environmentally sensitive areas, recovery and clean-up not only become an ecological burden but also negatively impact company profitability, reputation, and the company’s ability to invest for the future.

As you can see, when industrial organizations don’t use AI and machine learning for proactive maintenance, problems (and costs) reverberate through the operational ecosystem. But the inverse is also true. When assets and operations function reliably, the benefits compound.

Increased asset efficiency translates to reduced emissions, fuel consumption, and water usage. Fewer unplanned maintenance incidents result in less material consumption and waste, fostering a more sustainable operational model.

The beating heart of the sustainability-reliability model is visibility. You need a dashboard with warning lights, but alarms that (unlike my car’s) can predict the future—literally. When you apply AI-enhanced analytics to multiple data sources, your operations engineers can visualize anomalies and process deviations to predict potential asset failures and their consequences. You can forecast estimated time-to-failure and predict energy optimization, production deviations, and quality enhancements.

Once you have these deep insights, you can optimize your asset maintenance plans, prevent outages, and reduce environmental impacts. And, by operating at the highest possible production rate while minimizing waste, you can significantly advance your sustainability KPIs.

Stockpiling spare parts and resources, a customary practice when operations are unreliable, also adds pressure. Excessive inventory ties up capital and perpetuates a cycle of waste and inefficiency. But, by optimizing inventory management through reliability-centered maintenance approaches, organizations can streamline operations and increase sustainability even further.

I’ll have to leave you there. I’m off to retrieve my car from the mechanic, which has finally been fixed, eons (or so it felt) after my dashboard alarm first went off. If only my car had AI-enhanced analytics for reliability, I might have avoided this mess. As for how much the repairs cost, we’ll see. Wish me luck.

Learn more about the link between asset reliability and sustainability here