Machine learning in manufacturing: an overview

Machine learning in manufacturing: an overview

November 9, 2022

Machine learning and artificial intelligence are now the main drivers of manufacturing digital transformation. These technologies are poised to transform the majority of manufacturing processes including equipment maintenance, inventory management, supply chain, product design, and quality control.

In this article, we discuss ten use cases, the technology’s impact on the industry, and how it can help organizations become more efficient. Our machine learning consultants outline a step-by-step ML implementation roadmap and identify the common adoption pitfalls.

Machine learning in manufacturing: a summary
In the manufacturing context, machine learning is applied to collect data about equipment vibration, temperature, electrical activity, and other factors to make informed and partially automated decisions about equipment maintenance and product quality, helping improve manufacturing processes’ overall efficiency.

While many modern manufacturing firms employ various data analytics methods, the majority of them are rule-based, meaning that these tools make decisions based on a predefined set of rules and thresholds. When key indicator values differ from the expected ones, such systems automatically report deviations to operators and engineers, who then use their expertise to figure out the causes and come up with solutions. In the meantime, machine learning systems can detect issues, find their causes, and suggest solutions automatically without human involvement.

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


The importance of ML adoption in manufacturing

Chart 1: Average improvement through machine intelligence, by KPI

Data source: smart production: Machine intelligence in business operations, 2022

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

Data source: 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 to predict equipment failures, streamline quality control, and improve material procurement.

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. 

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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 IoT-enabled systems, manufacturing organizations can proactively monitor equipment conditions, predict when equipment is about to fail, and take measures to prevent it. Installed sensors retrieve data from machinery components and send this data to an ML-based system, which automatically detects performance deviations.

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
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

Semiconductors and computers

  • Visual inspection of wafers
  • Chip development and design

Plastic products

  • 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 how much energy the plant will consume in a given time, allowing the company to reduce carbon emissions.

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


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.


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.


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 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.


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.


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.


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.


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 machine learning and artificial intelligence 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 important technology for digital transformation in manufacturing, and it's not surprising that, like with other emerging technologies of the past, manufacturers 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 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.