How and where to Use Machine Learning for Production Optimization

I Machine Learning: Transforming Data into Knowledge

Inefficiencies in processes impair production. Industrial data, an analytical and business mindset, and the application of Artificial Intelligence (Machine Learning) tools and techniques make it possible to understand why they occur, when they will happen, and how to avoid them.

But, What is Machine Learning?

Machine Learning is the type of artificial intelligence that processes enormous data sets to detect patterns and trends, and then uses them to build models that predict what may happen in the future, becoming more intelligent over time in a continuous process.

Machine Learning is not a system that can be connected to a production line and make the line work better than before. It is a continuous process that needs data inputs from one or more devices and that enables the data to be collected, prepared, trained, evaluated, and improved to develop knowledge about how the production line works.

This knowledge can be used to determine how the production line can have a higher performance, operate at a lower cost, function more reliably, etc.

In this way, machine learning transforms an industrial operation allowing companies to achieve:

• Find new efficiencies and reduce waste to save resources.
• Understand the trends and changes in your own market.
• Comply with industry regulations and standards, improve safety, and reduce your environmental impact.
• Increase the quality of products.
• Find and eliminate bottlenecks in the production process.
• Improve the visibility of the supply chain and distribution networks.
• Detect the first signs of failures or anomalies or reduce downtime and perform repairs more quickly.
• Carry out a root cause analysis to improve processes.
• Optimize the life cycle of assets.

In which Industrial Areas is Machine Learning Applied?

Some of the current applications and transformations attributed to Machine Learning that allow predicting and improving inefficiencies in production processes are:

1.- Predictive Maintenance

• Predict interruptions in a production line in advance to schedule downtime at the most advantageous time and eliminate unscheduled downtime.
• Identify anomalous behaviors by comparing current operation with the normal behavior of the machine to identify and address situations of risk to the integrity of the machines.
• Anticipate machine failures by identifying the complex behavior patterns that indicate the first signs of a problem, and take advantage of them to plan maintenance and avoid unexpected stops.
• Estimate the remaining useful life of the components by modeling the wear patterns of the components.

2.- Quality Control

• Understand the variability by identifying the process inputs that most affect the quality metrics and the complex interactions between these and the quality of your product.
• Anticipate quality by continuously predicting the quality of the products based on the configuration of the process and its behavior.
• Simulate different configurations by modeling the quality based on the control levers and simulating the results of different configurations to search for the optimal one in each situation.
• Reduce laboratory analysis by identifying the batches with the highest risk of quality non-compliance to focus the tests on these, thus reducing costs and the lead time of the product.

3.- Resource Optimization

• Increase the energy efficiency of the process by detecting anomalies and optimizing it from an energy cost point of view.
• Organize the tasks of the employees in an optimal way, so that these are adjusted to their workload, skills, experience, authorizations and, even, location.
• Discover the optimal behavior by modeling the normal operation of a process to identify anomalous behaviors and discover superior yields.

4.- Logistics

• Demand forecasting with the identification of the most influential factors, as well as the most relevant KPIs to monitor.
• Planning of manufactures allowing the optimization of inventories of raw materials and finished products.

I Conclusions

Machine Learning is a truly revolutionary technology that uses industrial data to improve business and production practices and help companies make better decisions.

Knowing all the how’s and why’s before something harmful happens can help streamline production, increase product quality, and improve customer satisfaction.

The important thing is to identify the different points within the factory where Machine Learning can provide greater value. Applied correctly and meaningfully, it can bring great benefits that are reflected in the income statement.