How better maintenance practices lead to cleaner, greener and more efficient manufacturing

In our last blog, we revealed the stark difference between companies prioritizing predictive maintenance practices and those which aren't.

As well as reduced downtime and extended machine life, firms that have embraced PdM enjoy, on average, double the overall equipment effectiveness (60) of those that have not(29).

But the dividing line runs deeper than operational efficiency. Improving maintenance practices in manufacturing leads to savings for people, production and the plane.

Fewer parts mean fewer pollutants

 

In Europe, 21% of emissions come from industrial processes and energy use, and this rises to 23% of direct carbon output in the US. And these are just the headlines. Shrinking one's carbon footprint is a massive task for any manufacturer.

Consider embedded carbon. Companies must work to extend the useful life of their equipment. What about the whole-life carbon cycle, which includes the production, transportation and storage of replacement spare parts?

Machinery malfunction from wear or improper inspection can also result in pollutants or other waste finding their way into drains and waterways.

Predictive maintenance is no silver bullet for sustainability, but it does promote better practices at each stage of a machine's lifecycle:

  • Senseye PdM predicts issues with machinery in advance, so engineers can make the fix before failure, reducing waste and spare part costs by 40%.
  • It also extends machine lifetime by using AI to calculate and predict 'Remaining Useful Life' automatically. With it, manufacturers can improve asset lifetime by 50%.
  • Automated predictive maintenance even has a role in optimizing energy use. Ongoing condition monitoring is vital for running machines at the most efficient rate in the most optimal state.

Doing more with less

Experienced engineers are leaving the workforce, putting maintenance teams under increasing pressure to keep production lines running.

PdM is critical for reducing the number of timely unplanned downtime incidents. But it's just as powerful at organizing maintenance for maximum efficiency.

Senseye PdM reduces the need for manual inspections and overloaded spare inventory. It means interventions are timed optimally and informed by data, not gut feeling, and it cuts the burden of over-maintenance.

More remote data collection and analysis also improves worker safety. While catastrophic failures are dangerous, each time an engineer opens a machine they expose themselves to risk. A move to PdM has a real impact here, reducing the number of times an engineer has to interact with a machine and the total amount of time they spend around it.

Savings, savings, savings

 

A shift to PdM saves money. Senseye estimates that using AI-driven machine-health monitoring across the Fortune Global 500 could:  

  1. Save 1.6 million hours of downtime annually
  2. Realize a 6% productivity boost worth $734 billion.

By bringing in PdM, Senseye's clients have shown the following:

  • An 85% improvement in downtime forecasting accuracy
  • A 50% reduction in unplanned machine downtime
  • A 55% increase in maintenance staff productivity
  • A 40% reduction in maintenance costs

Check out Senseye's 'True Cost of Downtime 2022' report here, to learn how the world's largest manufacturers are turning to PdM to tackle the challenge of unplanned downtime.