AI for Manufacturing

AI for manufacturing is the intelligence of machines to perform humanlike tasks, responding to events internally and externally, even anticipating events autonomously. The machines can detect a tool wearing out or something unexpected, and they can react and work around the problem.

What Is AI for Manufacturing?

AI for Manufacturing is ushering in the “Industry 4.0” era for manufacturing. Artificial intelligence is currently used in manufacturing plants all over the world to reduce critical errors, drastically improve production times and boost safety measures.  Artificial intelligence is a game-changing technology for any industry. As the technology matures and costs drop, AI is becoming more accessible for companies. In manufacturing, it can be effective at making things, as well as making them better and cheaper.

Leveraging AI technologies can enhance organisations’ analytics capability so that they can use their resources more efficiently, make better forecasts, reduce inventory costs. Thanks to better analytics capabilities, companies can also switch to predictive maintenance leading to eliminating downtime costs and reducing maintenance costs.

AI for Manufacturing

AI for manufacturing is the use of machine learning (ML) solutions and deep learning neural networks to optimise manufacturing processes with improved data analysis and decision-making.

A commonly cited AI or Manufacturing use case, is predictive maintenance. By applying AI for manufacturing to data, companies can better predict and prevent machine failure. This in turn reduces expensive downtime in manufacturing processes. AI for manufacturing has many other potential uses and benefits, such as improved demand forecasting and reduced waste of raw materials. AI and manufacturing have a natural relationship since industrial manufacturing settings already require people and machines to work closely together.


Why Does AI for Manufacturing Matter?

AI is crucial to the concept of “Industry 4.0,” the trend toward greater automation in manufacturing settings, and the massive generation and transmission of data in manufacturing settings. AI and ML are essential ways to ensure that organizations can unlock the value in the enormous amounts of data created by manufacturing machines. Using AI to apply this data to manufacturing process optimisation can lead to cost savings, safety improvements, supply-chain efficiencies, and a host of other benefits.


Use cases of AI for manufacturing

Quality checks

Some flaws in products are too small to be noticed with the naked eye, even if the inspector is very experienced. However, machines can be equipped with cameras many times more sensitive than our eyes, and thanks to that, detect even the smallest defects. Machine vision allows machines to “see” the products on the production line and spot any imperfections. The logical next step might be sending the pictures of said flaws to a human expert, but it’s not a must anymore, the process can be fully automated. We can offer an automated visual inspection tool to find even microscopic flaws in products. The system recognises defects, marks them, and sends alerts.

Prediction of failure modes

We select some data to take into consideration and overlook other, often due to lack of its visibility. This can lead to false conclusions.  We can make false conclusions considering products and processes. Products can fail in a variety of ways, irrespective of the visual inspection. A product that looks perfect may still break down soon after its first use. Similarly, a product that looks flawed may still do its job perfectly well. The way we observe objects and flaws is biased and many things may be different than they seem. With vast amounts of data on how products are tested and how they perform, artificial intelligence can identify the areas that need to be given more attention in tests.

Predictive maintenance

Predictive maintenance allows companies to predict when machines need maintenance with high accuracy, instead of guessing or performing preventive maintenance. Predictive maintenance prevents unplanned downtime by using machine learning. Technologies such as sensors and advanced analytics embedded in manufacturing equipment enable predictive maintenance by responding to alerts and resolving machine issues.  By analysing data, our artificial intelligence systems can draw conclusions regarding a machine’s condition and detect irregularities in order to make predictive maintenance possible.

Generative design

Generative design is a process that involves a program generating a number of outputs to meet specified criteria. Designers or engineers input design goals and parameters such as materials, manufacturing methods, and cost constraints into generative design software to explore design alternatives. The solution utilises machine learning techniques to learn from each iteration what works and what doesn’t.  Computational design doesn’t replace human creativity, a program aids and accelerates the process, expanding the limits of design and imagination.

Digital twins

A digital twin is a virtual representation of a factory, product, or service. The representation matches the physical attributes of its real-world counterpart through the use of sensors, cameras, and other data collection methods.  This pairing of the virtual and physical worlds allows analysis of data and monitoring of systems to head off problems before they even occur, prevent downtime, develop new opportunities and even plan for the future by using simulations.

To make digital twins work, the first thing you have to do is integrating smart components that gather data about the real-time condition, status or position with physical items. The components are connected to a cloud-based system that received all the data and processes it.

Environmental impact

The manufacture of a variety of products, including electronics, continues to damage the environment. How? Extraction of nickel, cobalt, and graphite for lithium-ion batteries, increased production of plastic, huge energy consumption, e-waste, just to name a few.   AI could help to transform manufacturing by reducing, or even reversing, its environmental impact. AI for manufacturing can support developing new eco-friendly materials and help optimise energy efficiency.

Making use of data

There’s a whole variety of ways to use big data in manufacturing. Manufacturers collect vast amounts of data related to operations, processes, and other matters, and this data combined with advanced analytics can provide valuable insights to improve the business. Supply chain management, risk management, predictions on sales volume, product quality maintenance, prediction of recall issues these are just some of the examples of how big data can be used to the benefit of manufacturers. This type of AI application can unlock insights that were previously unreachable.

Price forecasts

To manufacture products, you first need to purchase the necessary resources, and sometimes the prices can get a little crazy. For example, if you buy stainless steel, its price is affected by a variety of factors, including the listings of Metal Exchange or the prices of other elements, some of them not listed on the metal exchange. With the rapid changes in prices, sometimes it may be hard to assess when it’s the best time to buy resources.

Knowing the prices of resources is also necessary for companies to estimate the price of their product when it’s ready to leave the factory. Let’s stick to the example of stainless steel: the prices can vary, depending on the current listings of e.g. nickel or the price of ferrochrome. The system is able to provide accurate price recommendations just like in the case of dynamic pricing that’s used by e-commerce businesses like Amazon where machine learning algorithms analyse historical and competitive data to always offer competitive prices and make even more profit.


It’s not surprising that a large share of the manufacturing jobs is performed by robots. However, conventional industrial robots require being specifically programmed to carry out the tasks they were created for. The conventional robots now need to be provided with a fixed procedure of assembling parts but AI-powered robots can interpret CAD models, which eliminates the need to program their movements and processes. In 2017, Siemens developed a two-armed robot that can manufacture products without being programmed.

Customer service

In manufacturing, the importance of customer service is often overlooked, which is a mistake as lost customers can mean millions of pounds in lost sales. AI solutions can analyse the behaviours of customers to identify patterns and predict future outcomes. Observing actual customers behaviours allows companies to better answer their needs. 

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