If Industry 4.0 is a digital transformation toward a smarter factory, artificial intelligence and machine learning must have a role. But as with any technology adoption, it needs to be done in a pragmatic fashion that’s manageable while also creating measurable competitive advantage.
But while there are many opportunities for AI and machine-learning adoption in a wide range of manufacturing environments, there are still challenges, not the least of which is identifying clear, discrete use cases, especially for smaller players where budget for new technology deployments must be carefully managed.
Begin with the low-hanging fruit
Research released earlier this year by Capgemini Research Institute makes the case that AI is essential for manufacturers if they are to build a real-time future.
Based on interviews with actual manufacturers, “Scaling AI in Manufacturing Operations: A Practitioner’s Perspective” identified many examples of how AI has already been implemented, with a significant focus on real-time monitoring by using AI to help maintain machinery and production assets. The most popular use case for AI in manufacturing, according to the Capgemini research, is predictive maintenance so that it can be conducted at an optimum time before machines or equipment are likely to fail. Complex AI algorithms and machine learning can make reliable predictions as to the health of assets and machinery, and because maintenance becomes more preventative, equipment lifespans can be extended.
Predictive maintenance is just one of many benefits of AI and machine learning for real-time monitoring in a manufacturing environment. They can also help to troubleshoot production delays and track scrap rates, as well as help a manufacturer better understand processes and workflows through the application of machine-learning models informed by contextually relevant data.
Quality control can also be enabled by AI using image analysis with high-resolution cameras in real time to evaluate products so they meet performance benchmarks and compliance obligations in regulated industries such as automotive. By evaluating component images taken from a production line, it’s possible to automatically spot deviations from established quality standards by doing a comparison with approved images stored in a database. If they don’t meet those standards, a human inspection team can be notified.
Just as Industry 4.0 spans the entire supply chain beyond the factory floor, so do the opportunities for AI and machine learning. The latter can be used to improve demand forecast accuracy in alignment with product promotions and marketing efforts so that enough product is available in distribution channels, including store shelves. By using machine learning to inform the collaborative efforts between marketing, sales, account management, and supply chain, a manufacturer can improve the overall bottom line by reducing forecasting errors that can lead to lost sales, production that far exceeds demand, and product obsolescence.
But because there are so many possibilities for AI and machine learning to help transform manufacturing environments into smart factories, it can be overwhelming for manufacturers to know where to start, and adopting AI and machine learning isn’t without challenges.
Data science is a key component of AI-powered manufacturing
If you want to effectively take advantage of AI and machine learning, it’s imperative that you have a clear use case. Using it to improve quality control or predictive maintenance are great places to start, but even if you have that clarity, there are several key roadblocks that must be overcome.
While the industrial internet of things includes the use of sensors and cameras to help gather data from the factory floor, creating digital intelligence using AI and machine learning requires a great deal of knowledge documentation — after all, there’s tremendous insight and understanding of the environment residing in people, including those working on the floor, doing the maintenance, sourcing raw components, and distributing final products. You need to be able to codify all this domain expertise.
Predictive maintenance through AI and machine learning can bring a great deal of savings to manufacturing because it avoids costly downtime and allows you to address small problems before they become bigger ones. Worth noting: It also needs to be done in compliance with safety regulations, too.
Overall, using AI and machine learning requires strong data-management capabilities, which means manufacturers are now competing for data science skills and learning how to integrate that expertise across the breadth of the manufacturing environment.
You need high-quality data to inform AI and machine learning. Without, you won’t be able to fully leverage the automation and analytics made possible by the connected factory. Even before the advent of Industry 4.0, integrating different data in multiple formats was challenging, and it requires that you not only pull it from information technology (IT) systems but also operational technology (OT) systems. If you’ve already begun your digital transformation, then you should have people in place to serve as a liaison between IT and OT as well as production domain experts — you need people who understand production processes and information systems.
Data in a manufacturing environment is quite diverse, so not only do you need to collect anything that’s relevant, you must also “clean” and organize that data into a format that’s useful. For AI and machine learning to make use of it, that data must be consistent, and the various streams must be combined if you’re to enrich it with context that supports smarter decisions in organizations. In some cases, you want AI and machine learning to make decisions for you in real time with small amounts of data; in other cases, though, you might be ingesting a great deal of historical data collected over time.
Successful data management will not only allow you to apply AI and machine learning to improve maintenance and quality control but also transform the business of manufacturing and create new market opportunities.
AI, machine learning create opportunities across the entire supply chain
In addition to improving the production of existing processes, AI and machine learning can also support better product development.
Through generative design, AI can take detailed information such as available time, resources, and budget to suggest new options for producing a product. These approaches can then be further qualified by applying deep-learning models. With enough AI maturity that leverages real-time data, supply chains can be adjusted on the fly based on AI suggestions that account for a wide variety of factors, including economic trends, consumer behaviors, government policy, and disruptions from political upheaval or natural disaster. Considering these factors might allow for more efficient ways to acquire raw materials and reduce delays and downtime, or guide expansion decisions by using predictive models based on historical data.
By incrementally incorporating AI and machine learning into the manufacturing environment, organizations can build a smarter factory that can self-optimize to improve existing processes and products while readying itself to quickly adapt for new opportunities.