AI has been a buzzword for the last couple of years.
In fact, today you’d be hard pressed to find an industry where artificial intelligence is not already being used to up production efficiency and quality.
One of the industries where there is heavy, and beneficial, use of AI is in the manufacturing sector.
Despite the initial worries that automation in the manufacturing sector would cut off the human and render many jobless in the millions, this has not actually been the case.
On the contrary, AI has actually aided in making pharma manufacturing processes more efficient, more streamlined, safer and more profitable with Electronic batch record software. It is even projected that the global artificial intelligence in manufacturing market size will reach $USD 4,798 Million by 2026.
In this article, we are going to look at the top applications of artificial intelligence in manufacturing that has contributed to the fast adoption rates of AI in manufacturing.
However, manufacturing is just one of the sectors that AI has taken by storm. If you want to know what is projected to happen in other sectors, check out a recent survey I conducted that saw 80 AI experts predict what AI will bring in 2021.
So, without much further ado, let’s get started.
1. Defect Detection
In a traditional production line, you’ll find that many assembly lines completely lack in systems and technologies that could be put in place to identify defects.
And where this actually does exist, it is usually basic at its very best… which requires software engineers to hard code algorithms that will help differentiate between functional components and defective components.
The main challenge with this approach is that these systems cannot learn from past experiences or integrate new information in the process.
It has led to a ton of false positives that require an onsite worker to manually check and verify before a conclusive decision is made.
Well by integrating machine learning and artificial intelligence into the process, these systems are now powered with self learning capabilities. So manufacturers can now save tons of hours required for quality control by simply reducing false positives.
2. Quality Assurance
The manufacturing sector, contrary to other service based industries, requires an extremely high degree of precision and attention to detail.
Think about car manufacturing or even simple electronics like a smartphone… a malfunction is often characterized by an explosion that could put lives at risk.
If we take a look at just a few years back, quality assurance in manufacturing plants was mainly manual.
And by this I mean, a highly trained engineer with sufficient experience was tasked with ensuring that all electronics devices and parts, like microprocessors, are being manufactured correctly and that all their components are properly functioning.
Now you can imagine how much time and mental bandwidth that took.
However, today thanks to advancements in artificial intelligence, image processing algorithms have been developed that can automatically evaluate and establish whether an item has been perfectly produced.
This is achieved by automatically sorting and processing images collected by cameras installed at key points along the factory floor in real time.
3. Predictive Maintenance
One of the main expenses in manufacturing is the ongoing maintenance of production line machinery and equipment.
Since the continued and flawless operation of these equipment directly impacts the bottom line of any asset-reliant production operation, this spending is to a certain degree justified.
In fact, a recent study showed that unplanned downtime caused by machinery costs manufacturers an estimated $USD 50 billion annually, and that asset failure is the cause of 42 percent of this unplanned downtime.
What if a manufacturer could predict such downtimes?
Well, thanks to artificial intelligence and machine learning, engineers can now build predictive maintenance systems that use advanced algorithms, with neural networks, to make predictions regarding asset performance and possible malfunction.
A predictive maintenance system is now a requirement for any manufacturer who would benefit from knowing when the next failure of a machine would occur.
By implementing this alone, a manufacturer can drastically cut down the relatively high costs associated with unplanned downtime while also extending the Remaining Useful Life (RUL) of production machinery and equipment.
4. Human Robot Collaboration
In most manufacturing settings today, it is not uncommon to find collaborative robots working alongside human workers, as an extra pair of hands.
Now, the biggest fear of many when they hear mention of robots replacing human workers in the manufacturing sector is that of job loss and unemployment. But here is the thing, while more jobs are being taken over by robots, workers are being trained for more advanced positions like in product design and equipment maintenance.
Now to clear the air on this…
There are the autonomous robots that have existed for quite some time now. These are the ones that have been programmed to perform one specific task repeatedly.
Cobots, which is the short form for collaborative robots, are the opposite in the sense that these can learn various tasks. In addition to learning various tasks, they are capable of detecting and avoiding obstacles.
This spatial awareness and cognitive ability enables these robots to seamlessly work alongside human workers.
One interesting application of cobots is where a cobot is put to work in an automotive factory where it lifts heavy car parts and then holds them in place while human workers secure them.
5. Supply Chain Management
How is AI currently used in supply chain management?
Supply chain management is a crucial component of any manufacturing company, regardless of their size. Raw materials have to be properly sourced and logistics handled, or the company can be grounded at a moment’s notice.
A typical manufacturer would have supply chains with millions of orders, purchases, materials and ingredients to process on a regular basis.
If you were to handle these processes manually, like has been the case for a pretty long time, it would pose a significant drain on workers time and limited company resources. It is for this reason that more companies have begun augmenting their supply chain processes with AI.
By implementing AI technologies like machine learning and natural language processing, supply chain management systems are now smarter.
Take an example of logistics and warehouse management.
By developing artificial intelligence tools and apps, a manufacturer can optimize warehouse management and logistic operations, including production, delivery and fleet operations.
Another interesting example, while being a tad bit more complex, is a car manufacturer.
Let’s say a manufacturer receives nuts and bolts from two separate component suppliers. In the event that one supplier accidentally delivers a faulty batch of nuts and bolts, the manufacturer should be able to know which vehicles in particular were made with those specific nuts and bolts.
An effectively built AI system can track the vehicles that were made with the defective nuts and bolts, so that the manufacturer can recall them from the dealerships.
6. Generative Design
In addition to what we have just talked about, generative design is another one of the popular AI applications in manufacturing today.
In generative design, a program which is built using advanced AI technologies like machine learning and neural networks, generates a number of outputs to meet specified criteria.
So engineers can input their design goals and parameters like materials, manufacturing methods as well as cost constraints into a generative design software. It then explores every possible configuration and then provides the best design alternatives.
These proposed solutions can further be tested using machine learning in order to offer additional insight on the designs that work best. Now all you need to do is repeat this process until an optimal design solution is reached.
How is this revolutionary?
Well, it’s because generative design enables a manufacturer to explore ideas that could not be explored in any different way.
Imagine a real person was supposed to come up with one hundred different ways of designing a chair, how much time would that take?
Throw deep learning into the mix and viola, you can do all this in no time. The skilled manpower can then be directed on choosing from a wide range of options. So the company gets to deliver more value to their customers while improving efficiency of their processes.
7. Digital Twins
What is a digital twin?
A digital twin is a virtual representation of a physical product, process or system that a business uses to make model driven decisions.
The final representation, the digital twin, matches the physical attributes of its real-world equivalent through the use of digital sensors, cameras, and other data collection methods.
In fact, according to the IoT implementation survey by Gartner, 13% of organizations that are already implementing IoT use digital twins and 62% of them plan to use it within a year.
Why is the digital twins concept gaining group fast among manufacturers?
To begin with, product simulations are indispensable for any manufacturing company, but real world simulations are super expensive. So companies that need to learn fast, an example being in the manufacture of self driving cars, will rely heavily on simulations.
A good number of businesses use digital twins in a variety of ways, including product development and operational performance improvement.
In order to make digital twins work, you first need to integrate smart components that gather actual data about the real-time condition, status or position of physical items. Each one of these components are then connected to a cloud-based system that receives all the data and processes it using AI technologies like deep learning.
For example, you can use the sensors attached to an airplane engine to transmit data to that engine’s digital twin with every take off and landing. The airline and manufacturer now have critical information about the engine’s performance. An airline can then use this information to further conduct simulations and anticipate issues.
8. Inventory Management
Inventory management is another one of the trending applications of AI in the manufacturing sector in 2021. And rightfully so because it helps manufacturing companies to better manage their inventory needs.
Let’s look at how this is achieved.
Machine learning can be used to design solutions that promote inventory planning activities because they are better at dealing with demand forecasting and supply planning.
By using AI-powered tools for demand forecasting, a manufacturer will get more accurate results than traditional demand forecasting methods that engineers have been using in manufacturing facilities for ages.
With these tools a company can better manage their inventory levels to prevent cash-in-stock and out-of-stock scenarios from happening.
For example, let’s say a pharmaceutical company is using an ingredient that has a short shelf-life. With an AI system they can predict whether this particular ingredient will arrive on time or if it’s running late as well as determine how this delay will affect production.
9. Demand Prediction
Can you predict future demands for a product?
The ability to predict which kind of product or service will be a hit before launching is a valuable skill that any manufacturer will capitalize on for profit.
Luckily, the use of artificial intelligence in manufacturing is not limited to applications from the production floor, but transcends everything that includes helping companies to anticipate market changes.
With this superpower, the management now has a huge advantage over their competitors because they can move from a reactionary/response mindset and to a strategic approach.
How is this achieved through AI?
AI algorithms that run predictive analysis tools are used to estimate market demands by looking for behavioral patterns, linking key factors like location, socioeconomic and macroeconomic factors and weather patterns etc.
For example, predictive analytics algorithms can detect buying patterns that trigger manufacturers to ramp up or lower production on a particular item.
So the manufacturer only produces high-demand inventory before the stores need it.
10. Customer Service
Last but not least is the use of AI in customer service.
Even though the use of AI tools like chatbots and customer insights are more common in industries that deal directly with customers, like retail and hospitality, the manufacturing industry is also stepping in to harness this technology.
So manufacturing companies should not overlook customer service because AI-powered solutions can analyze the behaviors of customers, identify patterns and then predict future outcomes.
And by observing the customers’ behaviors, a manufacturing company is better able to answer their needs.
Some of the quick benefits of implementing artificial intelligence solutions in customer service include quick response times and personalized experiences which lead to improved relations.
As we have seen through these artificial intelligence applications in manufacturing, there remains no doubt that the manufacturing sector is leading the way in the application of AI technology.
And if you are not convinced yet, the benefits have been massive.
Think about the significant cuts in unplanned downtime and products that are much better designed.
Simply put, manufacturers are now putting to use artificial intelligence powered data analytics to improve production efficiency, product quality as well as safety for industrial workers.
It also means that there has never been a better time to learn AI, thanks to the immense amount of resources available that can very easily get you started… like today.
I hope this article has opened your eyes to the wide use and applications of artificial intelligence technologies in manufacturing. So if you were still in doubt as to whether AI has had a great impact on manufacturing, now there is not a single double.
What are some other applications of artificial intelligence in manufacturing that I didn’t mention in this list?
Please share your thoughts in the comments below.