Machine learning in manufacturing: key applications, examples & adoption guidelines

Machine learning in manufacturing: key applications, examples & adoption guidelines

March 21, 2024

In the manufacturing context, machine learning algorithms are applied to process large volumes of data about the production, equipment, and products to help optimize time-consuming aspects of the manufacturing process, including quality control, equipment maintenance, and product design.

Machine learning is now one of the main drivers of manufacturing digital transformation, poised to transform the majority of labor- and data-intensive manufacturing processes and improve companies’ operational efficiency. Here, we discuss the ten most prominent machine learning use cases and the technology’s impact on the industry, while our machine learning consultants outline a step-by-step ML implementation roadmap and explore the common adoption pitfalls.

Table of contents

Machine learning adoption in manufacturing: market statistics

Take a look at the most telling statistics of machine learning in the manufacturing industry:

growth rate of AI in manufacturing from 2019 to 2027

Fortune Business Insights

KPIs for companies which actively adopt machine learning in manufacturing

McKinsey & Company

of companies expect to increase their efficiency with digital technologies

PwC

The importance of ML adoption in manufacturing

Chart 1: Average improvement through machine intelligence, by KPI

Data source: mckinsey.com-Toward smart production: Machine intelligence in business operations, 2022

Chart 2: Nine out of ten companies are investing in digital factories

Data source: pwc.de-Digital Factories 2020: Shaping the future of manufacturing

Machine learning-based technologies used in manufacturing

Predictive analytics

Predictive analytics uses machine learning, statistical modeling, and data mining techniques to determine future outcomes. In the manufacturing context, predictive analytics are used extensively for equipment diagnostics and failure prediction, quality control automation, and streamlining of material procurement decision-making.

Intelligence process automation (IPA)

IPA combines robotic process automation, optical character recognition, data analytics, intelligent document processing, and machine learning to enable hyper-automation. In the majority of cases, manufacturing organizations use IPA to automate repetitive and manual tasks including accounting, staffing, inventory management, vendor communication, etc.

Computer vision

Computer vision is a field of artificial intelligence that allows computers to draw insights from visual information. Computer vision in manufacturing is used to automatically conduct a quality inspection of products at every stage of the production process, guide robots during product assembly, and ensure staff safety by proactively monitoring production facilities. 

Neural networks

Neural network algorithms are powerful tools geared toward recognizing hidden patterns and complex relationships in disparate input data. In smart manufacturing, neural networks are applied for complex, data-heavy tasks like predictive maintenance, quality control, energy efficiency improvement, and product design and development.

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10 use cases of machine learning in manufacturing

Predictive maintenance

In manufacturing, equipment maintenance is a crucial activity, performed by dedicated personnel at regular intervals. While somewhat effective, the issue with the manual approach is that equipment can break down unexpectedly, resulting in operation disruption, and considerable revenue loss. Implementing machine learning- and Internet of Thing-enabled systems, manufacturing organizations can monitor equipment conditions and take proactive repair measures. Installed sensors retrieve data from machinery components and send it to an ML-based analytics system, which automatically detects performance deviations and predicts equipment’s remaining useful life and when it’s going to fail.

Quality control

By augmenting quality control systems with computer vision and machine learning, the whole process can be almost fully automated and made much more effective. Given that manufacturing companies know exactly what a final product has to look like and often have more than enough data to train ML algorithms, quality control is one of the most potent areas for machine learning applications.

Demand forecasting

When it comes to manufacturing, demand forecasting is not an easy task. With thousands of products and product parts at their warehouses, understocking and overstocking are common yet serious issues that plague many manufacturing companies. Regardless of how experienced inventory managers are, relying on a gut feeling can take them only so far. With an abundance of past inventory data, predictive models can accurately forecast inventory demand and minimize the risks of overstocking or understocking. What’s more, advanced machine learning solutions can also take into account customer behavior, season, or the current economic situation.

Contract management

Any manufacturing company heavily relies on a range of suppliers. While predicting how many supplies a company will need for a given period, procurement teams also need to manage legal contracts between the manufacturing company and multiple suppliers. These documents contain valuable information including payment deadlines, termination dates, and renegotiation rights. Instead of manually sifting through hundreds of pages written in legal language, natural language processing systems can automatically detect the most crucial information in these documents and deliver it straight to the decision-makers. This can significantly decrease the time it takes to make sense of legal documents, and cut operational costs as a result.

Product development

Forward-looking manufacturing companies extensively use neural networks and deep learning to streamline product development. ML-based product development is often referred to as generative design, which is a method that allows users to input all the necessary information (for example, the number of available resources and desired production time) about the final product for a system to develop. This allows manufacturing companies to decrease production time and, most importantly, create better products.

Production optimization

Manufacturers can use machine learning to uncover hidden flaws in the production process. In the majority of modern manufacturing facilities, each production stage is assigned to a particular production module, which has many settings that can be adjusted. By quickly analyzing vast amounts of production data with machine learning, companies can identify what production stages are the most ineffective and tweak them accordingly.

Cybersecurity

Modern manufacturing facilities heavily rely on the Internet of Things, digital twins, cloud, and other digital systems to operate. While inherently important, these digital systems are common targets for cyberattacks. With increasingly sophisticated attacks being carried out, conventional cybersecurity methods are becoming obsolete. By continuously monitoring production systems, machine learning-based anomaly detection platforms can quickly identify signs of a cyberattack and immediately shut down operations or alert respective personnel about the potential attack.

Robotics

Nowadays, modern manufacturing is unimaginable without robotics. However, conventional manufacturing robots can only follow predetermined paths and perform predefined actions, which makes it impossible for them to adapt to changes in the production environment. This can not only result in faulty products, but also cause life-threatening situations in the factory. By augmenting robots with machine learning and computer vision, robots can differentiate between objects and people and make intelligent decisions about their next move. For example, if a person stands in the way of a robot’s conventional route, it will be able to change its path and avoid that person.

Order management

In essence, manufacturers rely on other manufacturers to put together their end-products. Given that every factory wants to maintain its bottom line, order managers need to determine which suppliers can provide raw materials of the best quality for the best price. With hundreds of suppliers and thousands of raw materials, this task becomes increasingly complicated, with compromises inevitably being made along the way. Machine learning models can process supplier data in real time and help manufacturers determine the most adequate price for a given material while considering the type of material, as well as quality, finishing, size, etc.

Digital twin

A digital twin is a digital replica of a factory's equipment that provides elaborate information about its condition and performance in real time. Digital twins are extremely useful as is, but when augmented with machine learning, manufacturing companies can achieve unprecedented efficiencies. Thousands of sensors installed across a factory's machinery send data to a unified data platform, where neural networks and other advanced machine learning models make sense of this data and generate useful insights. Thus, complemented with a machine learning system, digital twins can aid manufacturers in refining products and workflows and make equipment maintenance more efficient.

Machine learning across manufacturing sectors

While many machine learning use cases are similar from one manufacturing sector to another (predictive maintenance, product quality control, inventory management), some of them have sector-specific use cases, which we outline below:

Machine learning
Food
Automotive
Furniture
Semi- conductors & computers
Plastic products
  • Sorting food with the help of computer vision
  • Quickly analyzing the ripeness of fruits and vegetables
  • Cleaning equipment that doesn’t need disassembling
  • Self-driving features
  • Voice assistants and emotion recognition features
  • Classifying and detecting defects
  • Verifying proper assembly of the model
  • Cost-effective generative design
  • Visual inspection of finished products
  • Visual inspection of wafers
  • Chip development and design
  • Autonomous sorting of recyclable materials
  • Optimizing injection molding

Manufacturing companies successfully using machine learning

ZF Friedrichshafen AG, commonly referred to as ZF Group, is a German manufacturer of mechanical components for the automotive industry. ZF Group's machinery is equipped with sensors that continuously collect data, which is then analyzed with proprietary machine learning algorithms to predict equipment failures. ZF Group also uses machine learning to predict the plant’s energy consumption and reduce its carbon emissions.

Siemens, a world-renowned company, was one of the earliest advocates of implementing machine learning, AI, and other emerging technologies. While the company extensively uses machine learning to enhance its processes, it also provides an industrial machine learning- and IoT-based solution MindSphere for other manufacturers. MindSphere is a flexible solution that allows manufacturers to tackle factory-specific issues. For example, one of the solution’s use cases provided by Capgemini on the MindSphere platform allows organizations to significantly improve visual inspection accuracy at production assembly lines.

Carlsberg, the Danish beer maker, uses machine learning to come up with new beer flavors. In collaboration with Microsoft, Aarhus University, and the Technical University of Denmark, the company developed sensors that can differentiate between different beer flavors. A machine learning-based predictive engine can describe the taste of a new beer based on a set of specified ingredients, which accelerates the development of a new beer flavor by three times on average.

General Motors, one of the biggest vehicle manufacturers in the US, uses machine learning-based generative design to create a rather inconspicuous, but crucially important vehicle component – the seat bracket that is used to fasten seat belts. Kevin Quinn, Director of Additive Design and Manufacturing at General Motors, states that using conventional design methods, the team can come up with two to three design options, but generative design provides over 100 design options for a single component. Moreover, the resulting part turned out to be 40% lighter and 20% stronger than the original part.

Founded in 2016, the US-based Veo Robotics is on a mission to make factory floor robots safer and more efficient with the help of computer vision, machine learning, and 3D sensing. Veo Robotics' so-called FreeMove sensors are positioned in the working environment and capture the image data of the workspace. If a human is closer to the robot than the specified distance or an environmental hazard is detected, the robot immediately shuts down. When a human is at a safe distance, the robot automatically restarts.

Signs you need to adopt machine learning

Unsafe working environments

Robots embedded with advanced computer vision systems can differentiate between objects and people and make intelligent decisions about pathfinding and movement in general, which significantly increases on-site employee safety.

Inefficient equipment maintenance

Assisted with IoT sensors and machine learning, technicians can save valuable time and resources by conducting maintenance checks only when equipment has a high probability of failure in the near future instead of routinely checking machinery conditions.

Inefficient visual inspection

Conventional visual inspection systems rely on high-resolution optical cameras that capture visual data, which is then compared to a template to identify defects. Slight variabilities in the surrounding environment or product itself cause a high number of false positives. Machine learning-enabled visual inspection tools, on the other hand, aren't thrown off by environmental changes and can continuously learn, which results in improved visual inspection precision.

Limited design options

While creativity is an exclusively human capability, machines can boost and scale it up. When a machine learning-based generative design system is given a very narrowly-defined set of parameters and limitations to adhere to, the number of design options usually far exceeds human outputs.

Inaccurate demand forecasting

Instead of relying on a gut feeling and industry experience, organizations can introduce machine learning-enabled systems to streamline demand forecasting. Thanks to its ability to process enormous amounts of data in real time, demand predictions can become much more accurate.

A step-by-step implementation guide

1

Use case definition

As with any technology implementation, it all starts with the definition of use cases, and feasible and value-adding use cases stem from real business needs. For example, if there are too many customer complaints about scrapped products, investing in a machine learning-augmented quality control system might be a good idea.

2

Data gathering

After you've defined a use case, it's time to determine what data you need and where you can source it from. For example, for predictive maintenance, it's data generated by machinery sensors, and for visual inspection systems, it's thousands of images of faulty products.

3

Data cleaning and formatting

Machine learning models are as good as the data you provide them with. For machine learning tools to operate effectively, you need to clean, format, contextualize, and organize the initially gathered unstructured, raw data. Ideally, this process should be performed for every data set you gather, as it can have a significant impact on your future ML applications.

4

Data visualization

While it may seem not mandatory to visualize data for your specific use case, data visualization is a sure way to realize the full potential of machine learning and advanced analytics in general. Easily understandable dashboards can be extremely useful in unlocking valuable insights about manufacturing processes.

5

Model training

After the data is in the desired state, you are all set to train your machine learning model. By running historical and live data through the algorithm, you can identify patterns that lead to inefficiencies in a given process. While choosing the algorithm goes way beyond the scope of this guide, the importance of opting for the right algorithm should never be underestimated.

6

Model validation and deployment

To ensure that your machine learning model can be useful in a real-world scenario, it’s crucial to test it against previously unseen real data. In the majority of cases, these tests reveal the need for sufficient model adjustments and improvement. If after multiple tests, the model works as intended, you can safely deploy it into the production environment.

7

Continuous model retraining

When it comes to machine learning, implementation is never a one-time effort. As the production environment, products, processes, data, and people change and evolve, machine learning models become obsolete quite fast. To maintain model effectiveness, it's critical to constantly retrain, update, and deploy them.

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Machine learning adoption challenges and how to resolve them

Given an avalanche of potential use cases and benefits that AI and machine learning can bring to manufacturing organizations, one may wonder why this technology is not adopted by more companies. In their 2021 report AI Adoption in the Enterprise, O’Reilly Media found out what aspects of the technology’s implementation proved particularly challenging.

Talent shortage

Regardless of the industry, experienced data scientists and machine learning engineers are scarce. What's more, if your company has no previous experience with artificial intelligence, you will likely hire all the wrong people even with infinite funds. The best solution here is to partner with a full team of specialists comprising data engineers, data scientists, ML engineers, data analytics leads, etc. This way, you can dedicate yourself to business outcomes while leaving all the technicalities to experts.

Lack of data

While your manufacturing facility constantly produces great amounts of data, properly gathering and structuring it is a whole other story. Nowadays, many consider synthetic data as an immediate solution to this problem, but in the grander scheme of things, it’s useful only as a first aid kit. Investing in establishing proper data governance standards is probably one of the most, if not the most important thing a modern manufacturing organization can do to secure its future in an industry that has grown to be so driven by data.

Finding a starting point

In the manufacturing context, seemingly every use case is the right one for ML implementation. Every manufacturer would love to know the exact reasons for machinery failure and offload routine tasks like inventory management to the algorithms. Successful use case definition boils down to addressing real business needs. In other words, machine learning use cases have to bring tangible financial impact. Then, when you think that you've selected the right use case, it's always a good idea to start small. Deploy that machine learning tool on a single factory floor in a single production line before expanding to other facilities to see if the juice is worth the squeeze.

Boost your production performance with ML

Machine learning is an essential technology for realizing the Industry 4.0 transformation of the manufacturing field, and it's not surprising that companies are actively investing into it. In the meantime, ML solutions are becoming more commonplace among factories as a result of rapid technological advancements. The benefits of machine learning are numerous, including the production of high-quality products, downtime reduction, productivity and efficiency increase, resources optimization, and safety improvement. If are looking to improve your operational efficiency with machine learning, don’t hesitate to get in touch with Itransition’s experts and discuss your project.

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