Let’s have a conversation about data readiness in heavy industry. A viral social post has been making the rounds on LinkedIn this week discussing the most important question businesses should be asking a potential AI vendor: “Will this work with the data I already have?”
It’s a fair question - and one that goes straight to the heart of whether AI can deliver real value for your business. So we wanted to respond and to provide our reasons for our response.
While we’d love to say “yes” every time, the reality is more nuanced.
The truth is, for AI to work effectively, your data needs to meet certain standards. And while we’ve achieved incredible success stories with manufacturers who were ready to dive into AI-driven optimization, we’ve more often encountered situations where the data just wasn’t there yet.
But here’s the good news: bridging that gap isn’t as daunting as it may seem.
Let’s start by breaking down what “data readiness” entails. For solutions like Fero Labs’ process optimization software to work, your data needs to be:
- Sufficient in Volume: Enough rows of historical data to train machine learning models.
- Detailed and Accurate: Including key parameters like spec limits, process variables, and quality metrics.
- Accessible and Integrated: Unified across systems rather than siloed in spreadsheets or legacy platforms. Fero’s AI-powered data preparation makes short work of this - ask us how!
For example, a steel mill might collect temperature and pressure data from its furnaces but lack detailed records of alloy compositions or quality outcomes. Without these additional parameters, it’s tough for an AI model to pinpoint optimization opportunities or to have enough examples for Fero to provide statistical confidence in a prediction.
Why Data Gaps Exist in Heavy Industry
If you’re struggling with incomplete or insufficient data, you’re not alone. A recent survey by the Manufacturing Leadership Council revealed that 65% of manufacturers cite data issues as their biggest challenge in adopting AI.
These issues range from incomplete datasets to fragmented systems and manual data collection riddled with errors. Many manufacturers face similar challenges due to a variety of reasons:
- Manual Data Collection
70% of manufacturers today still rely on manual methods like spreadsheets or paper logs. In older plants, data is often recorded manually, sometimes even on paper log, and later entered into spreadsheets. This process introduces errors and inconsistencies that can undermine AI’s effectiveness. Fero’s data preparation can help here. - Fragmented Systems
It’s common for different parts of a plant to use different systems for recording data. For instance, a cement plant might track kiln temperatures in one system while quality metrics are stored in another. Without integration, it’s hard to get a complete picture. Fero’s data preparation system will automatically merge all data sources into one. - Limited Sensor Deployment
While sensors have become more cost effective to run and own, they are often only deployed across certain parts of a process. Traditionally used for mechanical upkeep, Fero Labs now enables an entire process and workflow to be optimized requiring additional sensors or manually entering this into Fero until a new collection process is in place. - Cost Constraints
While sensors are becoming more affordable, deploying them across an entire process can still be cost-prohibitive for some facilities. - Cultural Resistance
Teams accustomed to manual processes may resist adopting new technologies or workflow. However, from our experience, teams like this are a dying breed. Only those willing to adapt to an AI-powered future will continue to survive and thrive in our ever-changing world.
When Fero Labs May Not Be the Right Fit (Yet)
Although we continue to develop AI-powered tools within Fero to overcome many of the data issues that our customers experience, including Flexible Optimization which enables live optimization even when specific details are unknown, our software ideally requires robust datasets to function most effectively.
It’s not that Fero Labs’ solution won’t work -it’s that the plant isn’t ready yet. The good news? Bridging this gap is entirely achievable with the right approach.
Preparing for an AI-Powered Future
The question “Will this work with the data I already have?” is not just about compatibility, it’s about readiness. For manufacturers, addressing gaps in their data isn’t just a technical challenge; it’s a strategic imperative that paves the way for long-term success.
While not every plant is ready today, every plant can be ready tomorrow with the right steps. By investing in better data collection methods and fostering a data-driven culture, manufacturers can unlock the full potential of AI, and we’re here to help them on that journey.
If you’re unsure if your data is ready - let’s chat - we can let you know quickly and we can provide a data sample so you know what you should be aiming to produce in the near future.
