Sir Edmund Shackleton and his 27-man crew sailed for Antarctica in December of 1914 in an attempt to achieve the first overland crossing of the entire Antarctic continent. But he never made it - shortly after arriving in Antarctica his ship got trapped and then crushed by the ice. But despite this tragedy, he and his entire crew survived, and amazingly, all made it back to safety two years later. He's a legend in exploration history. Here are the links if you want to hear the events on Audible or read it in a book.
And speaking of making history, in a sense it seems like we are also doing the same, like the explorers did in different ages, similar to Shackleton. Every year or sometimes even every month, with technology moving so quickly forward, it seems we are exploring new ground. New territory. New advancements.
One particularly impactful advancement gaining traction in pulp and paper mills now is predictive maintenance powered by AI and machine learning. And this firmly established technology is currently poised for further widespread adoption.
How widespread is AI adoption in the pulp and paper sector, and what does the future hold? According to recent market analysis, the global AI market in the pulp and paper industry - including predictive maintenance, process optimization, and quality control - is projected to grow from $7.1 billion in 2024 to $14.7 billion by 2034. Check out this chart, showing the predicted expansion of AI in all its forms at mills in the near future.
With the pressures that mills face in today's current economic (and social) climate, AI predictive maintenance is becoming a game changer for pulp and paper mills. It shifts operations from reactive repairs to proactive prevention by addressing:
- ROI, which in some instances are a minimum of 6 - 9 months (of course, your mileage will vary)
- Reduced downtime and maintenance costs (20-25% typical savings)
- Improved asset longevity
- Enhanced safety
- Sustainability contributions (energy optimization, waste reduction, lower emissions)
- Avoidance of emergency repairs from unexpected failures
At its core, AI predictive maintenance directly protects and strengthens operating margins by minimizing unplanned downtime - one of the largest drains on profitability, of course.
Beyond all these functions, an increasingly powerful application that integrates the AI and machine learning systems is Overall Equipment Effectiveness (OEE) monitoring and decision making. The difference between predictive maintenance and OEE is that predictive maintenance anticipates the equipment performance and assists in its improvement. OEE is a metric that reflects on how well the equipment performs.
Monitoring your OEE provides real-time anomaly detection and root-cause analysis, and proactive interventions can boost OEE by 10-20% in many cases (with some manufacturers reporting up to 15% gains directly tied to reduced breakdowns).
OEEs are designed with a very user-centric design, so operators can ask questions like, "Why is pump P439 unavailable?" or, "How is the plant running today?" and receive accurate, up-to-date responses drawn from a combination of current and historical data points. Not a bad way to start your day.
So is it all a bed of roses? Certainly not.
Realize that when integrating any AI system, you can have issues such as data quality. (AI is well known for making up information, called "AI hallucinations". You also have to check for gaps in data. Etc.) There can also be integration issues with your current system. Or cybersecurity concerns - the headache of the century. What about skill gaps with the staff?
You have to factor all this in too.
Because the field of AI is undergoing rapid transformation, significant advancements in capabilities and deeper integration into mills are anticipated over the next 10 years. As a direct result AI-powered predictive maintenance - using machine learning to anticipate equipment failures and optimize schedules - is emerging as a cornerstone application.
From explorers discovering new land and new passageways, to technology finding new applications to save money, improve efficiency, and increase safety, the common themes are discovery and change. Discovery and change will continue to happen, but thankfully with AI, we get to stay within the comfort of our own environment. And we all stay significantly warmer, too.
Have a safe and happy New Year. |
