What’s the role of artificial intelligence (AI) in Industry 4.0?

It’s not easy to answer. AI isn’t a single technology; it’s better described as a broad field of inquiry into the development of machines that solve problems normally solved by human intelligence (along with problems we can’t solve!).

For Industry 4.0, the AI innovation with the most impact (so far) is machine learning (ML).

What is ML?

ML algorithms create models based on historic data that return accurate predictions when given new data.

“Machine learning is not intelligence in the true sense,” says Scott Genzer, data scientist at RapidMiner, speaking with the Association for Advancing Automation. “It’s really nothing more than the good, old-fashioned mathematical modeling we’ve been doing for decades. The difference is that we have the computing power to [manipulate] massive amounts of data to find patterns, to find signal in a lot of noise that we used to do by hand.”1

Applications of ML in Manufacturing

When fitted with Industry 4.0 capabilities (e.g., smart sensors), machines on the plant floor provide constant streams of data about their performance. CRM and other systems can track every interaction with suppliers and customers. The goal for manufacturers is to parse this data — and learn. Questions this data may answer include:

  • When will my equipment break down?
  • What are the factors that determine the quality of my product?
  • How likely are my suppliers to perform as expected?
  • How much product do I need to meet — but not exceed — demand?

ML-driven data analytics offer opportunities for predictive maintenance, predictive quality, and production forecasting to answer these questions — and more.

Example: Predictive Maintenance

Let’s use the example of estimated time-to-failure for a machine on the plant floor.

An ML algorithm can be designed to create a model for predicting estimated time-to-failure for specific pieces of equipment. Using a large dataset on past failures and variables associated with those failures (e.g., asset condition, environment, temperature)2, the algorithm updates a mathematical model for predicting the time-to-failure. Given enough data points, the model will return a reasonably accurate prediction when fed new data.

The Ghost in the Machine

ML can feel mysterious. How is the algorithm doing anything? How does it know what adjustments to make to the model?

Yet there’s really nothing mysterious about machine learning.

An algorithm is a set of step-by-step instructions a programmer gives the computer. The programmer specifies the type of model or models for the algorithm to test, along with parameters for adjustment. With each new data point, the algorithm makes small changes, creating a more sophisticated model with each iteration.3

ML and Industry 4.0

If data is the fuel of Industry 4.0, ML is the engine that makes the whole system of technologies work.

Is ML working for you?

 

Credits

Image by kjpargeter on Freepik.

1. https://www.automate.org/industry-insights/getting-started-with-ai-based-predictive-maintenance

2. Ibid.

3. https://writings.stephenwolfram.com/2023/02/what-is-chatgpt-doing-and-why-does-it-work/