Artificial intelligence (AI) is well on its way to radically changing many industries. Manufacturing, however, is particularly well-suited for the advances of AI. The modern development of AI and machine learning (ML) technologies is seen by many as the next major step in improving the efficiency and reliability of manufacturing processes. To better understand where the tech is headed, here’s a breakdown of three things you need to know about AI in manufacturing.
#1: The Outlook is Strong
The market for AI in manufacturing is projected to reach $16 billion by the year 2025. Interestingly, the use of advanced tech in manufacturing is a main driver of the AI market as a whole, which isn’t too surprising given the high pressure within the industry to increase productivity and streamline costs. It also speaks to the increasing pressure for manufacturers to not only meet regulations and drive out waste, but also adapt to evolving consumer demands for an improved commerce experience, and the increasing role manufacturers play in providing that experience.
#2: There are Five Major Cases for Using AI in Manufacturing
Referring to AI in manufacturing as the Industry 4.0 revolution, CIO notes that, “from the design process and production floor, to the supply chain and administration, AI is destined to change the way we manufacture products and process materials forever.”
Ultimately, each of the uses of AI in manufacturing are aimed primarily at improving productivity, efficiency, and product effectiveness. While there are many use cases across various angles, there are five in particular that stand out:
1) Product Development
One way manufacturers use artificial intelligence is in product development. The goal is to produce items that consumers want, and to do this, manufacturers employ machine learning to analyze market trends and customer demand. The end result is more targeted, personalized product design that appeals to individual needs.
One area AI helps with product development is with “generative design.” By inputting data into an AI algorithm describing various product and production parameters, solutions can be tested using ML, which helps designers gather objective insights on the product’s design and make adjustments until the optimal result is reached.
2) Quality Control
Quality control is increasingly difficult for manufacturers in a world where consumers demand perfection, products are more complex, regulations are stricter, and time-to-market is faster than ever. There’s also a lot at stake. Recalls can be crippling, both from a bottom-line perspective and the harm caused to brand trust and loyalty. As such, AI is being used to perform quality checks more efficiently and precisely than humans can. It can be implemented at every stage in the process, from raw materials to final post-production quality checks.
AI technology can not only detect problems after the fact, but it can also be used in a predictive manner with a process called “predictive quality,” or Quality 4.0. The use of AI algorithms to notify manufacturing teams of emerging production faults and potential quality issues helps detect trouble early on so a high level of quality can be maintained. It also enables manufacturers to collect data about the use and performance of their products in the field, helping product development teams make better engineering decisions.
3) Streamlining Manufacturing Processes
Of course, what many manufacturers focus on most is their actual production process. AI can be used in many ways to make production more efficient with faster output and less waste. For instance, a manufacturing plant that uses CNC (computer numerical control) machines to mill and assemble parts typically uses manually-keyed programs to get the job done. With the introduction of AI, the process is executed through machine learning algorithms that not only perform tasks with precision, but also analyze the process as a whole to find areas where it can be streamlined. That means less material waste, faster production, and more reliable results.
4) Predictive Maintenance
Studies show that unplanned downtime costs manufacturers $50 billion annually, and that asset failure is the cause of 42 percent of this unplanned downtime. The massive cost of unplanned downtime has elevated predictive maintenance as a must-have solution for manufacturers.
Predictive maintenance uses various sensors to monitor equipment for potential faults to cut down on maintenance costs. That capability is enhanced through the introduction of machine learning. As AI monitors equipment using various sensors, it’s able to pick up on trends and use those to anticipate potential issues before they develop. It can also get closer to root causes, meaning maintenance teams are better able to target underlying issues instead of spending time treating symptoms. So precise is this predictive monitoring that it’s often called prescriptive maintenance, or RxM. The end result is less time spent on superfluous preventive tasks and better maintenance resource utilization. It allows for drastic reductions in costly unplanned downtime, as well as extending the Remaining Useful Life (RUL) of production assets.
5) Inventory Management
Finally, AI in manufacturing is often used within inventory and supply chain management. Whether it’s with regards to MRO (maintenance, repair, and operations) or consumer inventories, artificial intelligence can be used to monitor trends to anticipate demands, track costs and usage, and generally optimize inventory management. That means less spending on unnecessary items, fewer stockouts, and shorter lead times.
#3: Artificial Intelligence for Manufacturing Requires a Strong Data Foundation
A look at the use cases shows that AI in manufacturing is a game-changer. For those in the industrial industry who aren’t ready to deploy AI, the reverse is also true. Forbes quotes innovation expert Stephen Ezell as saying, “If you’re stuck to the old way and don’t have the capacity to digitalize manufacturing processes, your costs are probably going to rise, your products are going to be late to market, and your ability to provide distinctive value-add to customers will decline.”
The purpose of AI and machine learning is ultimately to analyze data, which means it’s only going to be as effective as the information you feed it. As such, you need to be able to have that data readily available.
MDM Acts as Contextual Glue
Master Data Management (MDM) is an essential tool for bringing meaning and insight to an abundance of structured and unstructured data. As sensor devices stream an unprecedented amount and variety of data, the ability to intelligently master this data – and give context to the complex relationships associated with it – grows even more important. MDM acts as contextual glue to help enterprises master the complexities of their data and apply actionable insights to the business. Combining the science of AI and ML with the capabilities of an MDM platform helps organizations compile a complete data picture to transform operations, improve product offerings, and elevate the customer experience.
(Read more about MDM for Manufacturing here)
Furthermore, MDM with a Multi-domain approach enables and links multiple data domains simultaneously – like Product, Customer, and Supplier, as well as Reference Data, Asset Data, and Location Data which are crucial for manufacturing and distribution industries. These systems interoperate with various legacy systems, inheriting and learning from relationships between data, entities, and events across domains to drive data analytics and predictive insights.
For example, utilizing Multi-domain MDM, a manufacturer can link machine analytics to asset data and location data to configure a data model that allows them to predict future machine performance in each plant. These insights can help prevent unplanned outages, optimize maintenance, and make strategic decisions around cost and production in each location.
To dive deeper into this topic, listen to our webcast on Leveraging NextGen MDM to Drive Bottom Line Growth, featuring our guest speaker, Forrester VP and Research Director Gene Leganza.