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.
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.
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
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
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.Â
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:
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.
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.
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.
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.
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.
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.
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.
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.
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.