In manufacturing, optimisation is crucial for every aspect of the business: from maximising productivity while enforcing rigorous quality control to minimising costs and compliance risks while ensuring smooth, uninterrupted manufacturing processes. To succeed in these and stay competitive, manufacturers use automation and other innovative manufacturing solutions. Artificial intelligence (AI) can be used to empower both, which is why more and more companies are using AI in manufacturing.

In this comprehensive guide, you’ll learn about practical use cases, challenges, and benefits of AI, as well as find out how to start using AI in manufacturing.

Why do companies use artificial intelligence in manufacturing?

Although artificial intelligence can be used in almost every aspect of life and work, AI and manufacturing are particularly compatible thanks to an important shared element: data. Manufacturers generate and own vast volumes of data, including machine performance, logistics, process, and external data; AI technologies require data to train machine learning algorithms and provide accurate output specific to each business. This means that AI can help manufacturing companies put their structured and unstructured data to good use. So, how is AI used in manufacturing?

AI’s versatility is one of the reasons it’s playing such a huge role in the business world: leaders across industries find countless uses for AI, and manufacturing is no exception. It helps to streamline manufacturing processes, maximise efficiencies, reduce errors, improve the quality of products, empower employees, support operational excellence, and ultimately—gain a competitive edge.

How to use AI in manufacturing: Examples and use cases

There’s a very wide variety of use cases for AI in manufacturing, applicable in different ways across different types of manufacturing: from high-volume or customisable product manufacturing in industrial and automotive industries—to the continuous process manufacturing in the chemistry and energy sectors, or batch processes in pharmaceutical and food production.

So, rather than trying to come up with an exhaustive list of all AI use cases, let’s break down some of the key applications:

Predictive maintenance and AI-assisted quality control

Thanks to computer vision, cameras and trackers monitoring the manufacturing processes, and AI models used for advanced analytics, artificial intelligence can:

• Help predict required asset and equipment maintenance, which allows human workers to avoid issues rather than respond reactively once they arise (that’s why it’s called “predictive maintenance”)
• Identify anomalies and quality control issues more quickly and automatically trigger alerts or take prescribed actions to prevent defects
• Anticipate potential failures of equipment by using digital twins
• Optimise maintenance processes to reduce costs and extend equipment life
• Assistance in visual inspection and quality control automation

What is a digital twin?

In manufacturing, a digital twin is a virtual representation of a physical product, equipment, or machine. Using real-time data from sensors and other monitoring devices that track the state and performance of the physical asset, the digital twin simulates it in a digital environment. This virtual model can help optimise asset productivity and predict potential issues, such as equipment failure, which is why digital twins work well for predictive maintenance.

Supply chain management and machine learning algorithms

Machine learning algorithms can analyse vast volumes of supply chain data and identify patterns, which enables AI to:

• Provide real-time insights to improve demand forecasting and stock management
• Identify potential risks and supply chain disruptions early, which helps manufacturers mitigate risks by making the necessary adjustments quickly
• Help assess supplier quality and reliability
• Identify opportunities to reduce ecological footprint of materials used and deliveries
• Optimise warehouse management and logistics and reduce idle time

Data-driven process optimisation

By analysing performance and real-time data from sensors on the factory floor, AI technologies can identify areas for improvement in the existing manufacturing processes and equipment layout, which allows companies to:

• Identify bottlenecks and inefficiencies and receive recommendations for improvement
• Monitor and analyse resource usage, as well as occupancy and production patterns, for opportunities to reduce carbon footprint and save energy
• Optimise resource allocation to improve output and reduce costs and downtime

Task and process automation

Many innovative manufacturing solutions have been designed to automate repetitive manufacturing tasks, and this is something artificial intelligence can help with too. AI can:

• Save time on administrative processes and increase productivity by automating routine tasks
• Free up employees to focus on more strategic and skill-dependent activities by taking over labour-intensive tasks
• Optimise resource usage by automatically modifying production in response to demand fluctuations

Product development and customisation

AI can analyse both internal and external data, which includes market trends, sales data, and customer preferences. With that and rapid prototyping capabilities, AI can:

• Help develop or customise products to meet customer demands and tastes
• Accelerate development by swiftly generating and evaluating design iterations based on input parameters and constraints
• Conduct virtual testing to ensure optimal product performance by simulating various conditions, which allows manufacturers to address potential design flaws even before physical prototypes are produced

Employee empowerment

The use of artificial intelligence in manufacturing can benefit the manufacturer's employees too:

• AI can monitor and analyse data from sensors to improve workplace safety by detecting potential hazards and alerting employees to take appropriate action
• AI-assisted learning can help employees acquire new skills to adapt to change in job roles and technologies
• AI-enhanced visual inspection helps quality control specialists identify issues and production flaws, alleviating the burden of responsibility and chance of human error
• AI can provide employees with insights and recommendations that help make data-driven decisions—for example, about production planning and forecasting
• Due to developments in generative AI, many AI technologies now support conversational capabilities, which allows employees at various levels of technical proficiency to benefit from the use of AI in manufacturing (AI copilots, such as Joule, are a great example)

Benefits of AI in manufacturing

The three key benefits of using AI in manufacturing are that it serves as a catalyst for productivity, efficiency, and operational excellence. In other words, with artificial intelligence, manufacturers can do more, better, and in less time. For companies that produce goods, especially those in the field of industrial manufacturing, this opportunity alone makes AI worthwhile. But the use cases described above make it clear that there are even more benefits to incorporating AI into any smart factory strategy:

Better product quality

AI-assisted quality control helps manufacturers reduce the number of products with defects and provides real-time feedback for root cause analysis, whilst rapid prototyping makes it easier to spot design flaws early in the product development process.

Improved decision-making

By providing data-derived insights and advanced analytics, AI helps human workers make informed decisions faster and more confidently, which makes their lives easier and, ultimately, leads to better business outcomes.

Smart manufacturing and productivity

Thanks to AI-enabled automation and optimisation, manufacturers can be more efficient in their use of resources and time. This smart manufacturing approach, in turn, raises productivity, allowing companies to produce goods at a faster rate without compromising quality.

Cost reduction

AI can improve cost-effectiveness through more than just automation. The digital twin technology and AI-driven predictive maintenance can extend the life of equipment, which translates into savings in the long term—as does the conservation of energy, time, water, and other resources. The same is true for optimised supply chain management: AI-assisted data analysis helps make demand planning and inventory management more cost-efficient and risk-resilient.

Environmental sustainability

Through AI-optimised management of resources, logistics, and warehouses, manufacturers can reduce energy and material waste, lessening the ecological footprint. This positive environmental impact is important for sustainable manufacturing.

The current state and future of AI in the manufacturing industry

Given the potential benefits of artificial intelligence in manufacturing, it’s not difficult to see why manufacturers are interested in it. But when it comes to the actual adoption of AI in manufacturing, there’s still room for improvement. For example, not all manufacturers’ AI strategies are both linked to business objectives and supported by a measurement approach to evaluate success with ERP.

ERP is essential to innovative manufacturing solutions, so manufacturers need to ensure compatibility and synergy of their existing IT landscape and ERP portfolio—with the AI capabilities they want to incorporate. However, despite the adoption lag, the industry is likely to continue embracing the use of artificial intelligence.

Two factors have converged to make the use of AI in manufacturing more viable than ever before, which gives us reason to think this trend is here to stay:

Smart factory processes generate valuable data

The increasingly widespread use of cameras, sensors, and other technologies that track manufacturing processes 24/7, which started with smart factory and industry 4.0 initiatives, allows manufacturers to feed AI vast amounts of data in real time. This helps maximise the value manufacturers gain from their data and supports certain use cases of AI. In fact, some of the key applications of artificial intelligence in manufacturing, such as predictive maintenance, digital twin technology, and AI-assisted visual inspection, are impossible without this data. Moreover, by connecting this wealth of data with AI used for specific business objectives, manufacturers can drive customer value and empower employees to gain experience and skills faster, mitigating talent shortages.

Conversational AI makes artificial intelligence more accessible

At the same time, thanks to recent advancements in machine learning (such as breakthroughs in generative AI), conversational AI is now a reality. What does it mean? It means that humans can communicate—and work—with artificial intelligence using natural language rather than code. This is important because it makes AI accessible to employees at various levels of technical proficiency: everyone in the company, from operations and supply chain management to the factory floor, can use AI tools to be more effective and productive. This exponentially increases the value of AI as a catalyst for human potential and operational efficiency.

Adoption of AI in manufacturing: Challenges and concerns

Despite the benefits, some companies still have concerns about implementing AI in manufacturing processes, for example:

Shortages of skilled labour

To implement and operate AI-assisted capabilities, companies need talent with the right skills. Thankfully, AI itself can be a part of the solution.

• AI can help hire people with the right skills
• Existing employees can use AI-enabled HR solutions, such as learning and development software, to acquire new skills
• Assistive technologies can help improve worker safety on the factory floor by providing instructions and helping manufacturers enforce necessary compliance and safety procedures
• Generative AI enables AI assistants and copilots to understand natural language prompts, which makes it easier for all employees, not just IT staff, to access AI capabilities that help—for example, to configure complex solutions for customers without needing years of experience
• Many software providers integrate AI into business solutions they provide. For example, at SAP, we embed AI on multiple layers across our solutions, so customers using, say, the SAP cloud ERP portfolio already have access to AI features

Safety, security, and responsible use of AI

As with many innovative manufacturing solutions, the use of artificial intelligence requires regulation and guardrails, especially because AI handles potentially sensitive data. There are two important steps in addressing this issue.

Firstly, manufacturers should prioritise implementing ethical and responsible AI practices and opt for selecting third-party software providers that do the same. Secondly, to ensure the protection of business and customer data, it’s best to work with AI solution providers who are committed to ethical, transparent, compliant, and secure handling of your data. This is especially important, given the cyber security risks, sabotage, and IP theft that threaten manufacturing companies.

Here are some green flags to look for when selecting a security-minded provider:

• AI provider doesn’t share your data with third parties for the purpose of training their AI models
• AI solutions are developed responsibly and with rigorous standards
• AI provider employs advanced data security measures to protect your data at all times
• AI provider is committed to transparency and explicability

Large-scale business transformation for complex enterprise architecture

Smart manufacturing often involves vast IT infrastructures. And after going through multiple mergers and acquisitions, many companies end up with a patchwork of legacy systems. A large-scale AI adoption across such a complex enterprise architecture can seem challenging. The good news is that manufacturers don’t have to tackle this challenge alone: they can work with a software provider on developing a clean core strategy and AI-ready enterprise architecture.

Getting started with AI in manufacturing

The same sensible steps that apply to most innovative manufacturing solutions are applicable to introducing AI in manufacturing:

• Stay informed. Explore the state and capabilities of artificial intelligence, familiarise yourself with use cases, and look at results others have already achieved.
• Evaluate the advantages. Consider the specific nature of your manufacturing business: what challenges is your company facing, and can they be addressed by AI? Do you have a large volume of data that’s underutilised? How would AI benefit your manufacturing processes?
• Formulate the objectives. Like many tools, AI is most impactful when used purposefully and strategically. Working from your business goals, create an AI adoption strategy that clearly delineates what benefits you are expecting to gain and how.
• Research suppliers. Security, compliance, and data protection must be at the core of AI solutions you’re using. To protect yourself and your customers, thoroughly evaluate the prospective AI providers: ensure their data security practices are transparent and up to standard.
• Seek professional advice. Many software providers, especially in the ERP and business optimisation space, are already up to speed on all things AI—they can help strategise and even carry out the introduction of AI in manufacturing companies. If you’re already using an ERP portfolio that supports AI capabilities, introducing artificial intelligence in your company might be even easier than it seems. Embedded AI allows manufacturers to take advantage of artificial intelligence without the need to build, maintain, and iterate their own models.