What is AI/ML in Manufacturing?

Artificial Intelligence (AI) plays many roles in manufacturing. It’s intrinsically connected with industrial IoT (IIoT), and drives industry 4.0. There are dozens of use cases for AI in manufacturing and many ways that it helps drive value in the industry. One of the most common subsets of AI is machine learning (ML).

Process manufacturing is a highly competitive sector, with swiftly-changing markets and complex systems that have a lot of moving parts. In order to drive innovation and improve profitability, process plants need all the advantages that AI and ML can give them.  Machine learning in manufacturing commonly powers predictive analytics, robotics, predictive maintenance, and automated processes, which help make plants more efficient, profitable, and safe.

Why does AI/ML matter to process manufacturing plants?

Process plants rely on AI to integrate data, analyze it, and produce the deep insights and predictions that help drive better decision-making across the board.  ML is the type of AI that crunches huge datasets to spot patterns and trends, then uses them to build models that predict what will come in the future. ML allows plants to forecast fluctuations in demand and supply, estimate the best intervals for maintenance scheduling, and spot early signs of anomalies.  With the help of AI and ML, manufacturing companies can:

  • Find new efficiencies and cut waste to save money
  • Understand market trends and changes
  • Meet regulations and industry standards, improve safety, and reduce their environmental impact
  • Increase product quality
  • Find and remove bottlenecks in production process
  • Improve visibility into supply chain and distribution networks
  • Detect the earliest signs of failure or anomalies o cut downtime and carry out repairs more quickly
  • Conduct more accurate root cause analysis to improve processes
  • Optimize equipment lifecycle
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How to use AI/ML in manufacturing to the fullest extent?


Improve your data management

No matter what type of AI or ML tool you want to implement, you need large amounts of data to make it happen. Before beginning your AI project or building an ML model, you need to ensure that you’re gathering all the relevant data, storing it in a single location that’s accessible to your ML tools, and that you’re using the right data handling platforms to extract and process data into usable datasets.

Define your goals

There are multiple use cases for ML and AI in manufacturing, and they all have potential to deliver value and improve your bottom line. Start by defining the areas that can deliver value fastest and/or already have the necessary data, and prioritize which goals to aim for first, so that you can implement AI/ML in an orderly manner.

Apply AI and/or ML to the entire organization

Although you might begin by using AI for specific, limited tasks in certain departments or applying ML predictions to particular use cases, you won’t see its true impact this way. You need to connect up isolated use cases and apply AI automation and ML prediction capabilities both vertically and horizontally throughout the organization.

Assess your available skills 

Before you can apply ML or AI to your plant, you’ll need to check that you have the right personnel with the necessary skill sets. This can include analysts, data scientists, IT specialists, and more. In the case of SAM GUARD, no specialized personnel are needed, neither data scientists nor others; the only people who need to be available are those who know the plant well, typically process engineers.

Build a data-driven culture

Successfully implementing AI/ML in manufacturing requires you to first carry out a culture shift to become data-driven. You need to build trust  by collecting data to produce meaningful insights that assist employees in completing their jobs, demonstrating the value of data before you launch  ML models, and AI algorithms, otherwise your employees will simply ignore them.

How does AI/ML in manufacturing make process plants more productive?

By embracing the many use cases for AI and machine learning in manufacturing, process plants can improve production quality, predict fluctuations in market demand, reduce the number of serious incidents, raise their reputation for safety and environmental impact, and increase efficiency and productivity across the board. Implementing ML and AI in manufacturing is an ongoing process that continuously delivers value and boosts revenue across the long term.