AI in Manufacturing: Practical Applications

A lot of us think that artificial intelligence (AI) is something futuristic and ‘out there’ rather than being something we can make practical use of right now. But let’s get it straight: lots of manufacturing industries are already taking advantage of AI and machine learning (ML) to control and optimise their processes.

In this article, we’ll look at the 3 main areas where AI applications are used in manufacturing, as well as providing some examples of how this technology works for businesses today.

Process control

AI is used to control manufacturing systems that run production machinery.

Traditional control systems are great for setups that have very few variables. But they’re pretty terrible when there are lots of variables. Machine learning, on the other hand, isn’t restricted like this.

This makes ML ideal for controlling processes with variable input materials. The most obvious example is food.

Natural foodstuffs contain variable ingredients, such as different moisture and protein contents due to their ripeness and growing environments.

An ML-powered control system can gather such inputs and learn what cooking temperatures and cooking times work best when processing those variable ingredients to produce a consistent, predictable output.

Process optimisation

It’s one thing to control processes but another to make manufacturing production itself more efficient.

Putting all the data points gathered during the manufacturing process into the cloud means that manufacturers can rely on remote computing power to perform ML-based real-time analysis. This provides insights that lead to streamlined processes – and the time and costs savings that come with them.

Examples of process optimisation in manufacturing include:

  • Maintenance: planning when machinery needs to be serviced or swapped, to minimise impact on production.
  • Resource usage: optimising use of energy, materials and water, to take advantage of the current economic or environmental conditions, e.g. balancing inputs to make use of free rainwater during the wet season.
  • Data insights: analysing the production process against other data sets such as pollen, pollution and weather, to determine the impact of external factors on production, e.g. using weather predictions to forecast likely demand from supermarkets for barbecue food. AI and ML can also be used to analyse insights related to current news, holidays and advertising campaigns.
  • Demand & logistics: assessing ways to reduce excess inventory, optimise delivery batch sizes and speed up delivery.


Optimisations like this are particularly valuable for manufacturing businesses that turn out a high volume of products with a low profit margin per unit. They’re also ideal for producers of perishable goods, where the optimisation of timing is critical to avoid wastage.

Quality control

Manufacturers increase their profits by increasing their yields and by reducing their defect and returns rates. AI and ML can be used to manage the quality control process to achieve those aims – by ensuring that only the right materials are accepted into the system at the start of production and that only the right finished products are packaged and shipped at the end of production.

Here are some real examples of how AI and ML are used to aid quality control in manufacturing:

  • Ingredient checking: high-speed video and photographic checks ensure that no green potatoes make it through to be turned into crisps and that pizzas don’t leave the conveyor belt without the right toppings.
  • Tinned food: dents that are almost invisible to the human eye can be scanned and detected in split seconds by ML-based systems, ensuring that product standards are retained and that complaints and health risks are reduced.
  • Metal castings & forging: minuscule cracks in metal can develop into larger cracks that lead to structural failure. AI and ML can be used analyse photographs of components during dye penetration testing so that inspection of small cracks is automated rather than being left to humans.
  • Circuit boards: normally reviewed by video alone, circuit-board soldering can be assessed via a combination of video and audio, with microphones recording the sound of soldering, to provide a more accurate indication of whether the process is being performed effectively.

OK, but how can AI help me?

We’re running EXPLAINER sessions to show manufacturing and other businesses how AI and ML could work for them, potentially controlling and optimising their operational processes.

We do these sessions in small in-person groups but also online through Teams/Zoom.

AI and Machine Learning EXPLAINER

Find out more and book your AI/ML EXPLAINER here

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