The Struggle of Artificial Intelligence in Manufacturing and Why AI Projects Fail

Artificial intelligence in manufacturing is an incredibly promising prospect many industry leaders have been chomping at the bit to implement- and the reasons why are plain to see! With just some IoT connected devices and industrial grade computers designed to gather that data, an AI in manufacturing workplaces can be equipped to perform all manner of tasks ranging from predictive maintenance and quality control to troubleshooting and much more. 

However, like most smart tech-centered innovations, artificial intelligence requires more than a “set it and forget it” approach. Creating an AI program takes meticulous planning, testing, failing, and refining. Unfortunately, many aren’t privy to this and often think of artificial intelligence in manufacturing as a plug-and-play product. 

In fact, because of these misunderstandings and unrealistic expectations, IDC explains that about half of all AI end up in failure. Considering that same study showed only 25% of companies actually use AI, and the number of success stories out there are somewhat limited.  And yet, it’s not like the prospective benefits of a successful AI programs can just be ignored. 

So then, what are some impressive artificial intelligence in manufacturing industry examples? Why do so many of these AI projects fail? And, most importantly, how can you avoid the common pitfalls of AI implementation and reap the benefits?  

Artificial Intelligence in Manufacturing Industry Examples and Use Cases

The beautiful thing about artificial intelligence in manufacturing is that use cases for the innovative tech are constantly growing as the industry ebbs and flows. Below are only a few of the more readily applicable manufacturing-based AI use cases. 

1.) Quality Control

Using machine vision apparatuses, many manufacturers, especially those in the food and beverage industry, have been able to drastically improve their quality control efforts on the factory line.

Armed with cameras and algorithms capable of scanning products and picking up breaks or drops in quality on particular products, artificial intelligence in manufacturing can notify staff, allowing them the team to stop production and pinpoint where the drop in quality is occurring. From there, they can address and fix the problem instead of having the faulty product cause more disruption or dissatisfied customers further down the supply chain. 

Quality control AI application has proven very helpful for companies like Heineken and other food/beverage manufacturers that output gargantuan amounts of product and require more automated QC solutions.  

2.) Predictive Maintenance

Tying into the idea of spotting drops in product quality, observing repeated drops in quality coming from the same machine or team can tip off a well-trained AI program that a certain component within the supply chain is broken down or will break down in the future. 

After catching these repeated breaks, an artificial intelligence system that’s been optimized to do so can once again notify the proper staff of this soon-to-be expensive disruption before it can occur and bring the supply chain to a halt. This was a use case that was actually implemented beautifully into auto manufacturer FANUC’s Zero Down Time system. After an 18 month pilot run of the system, the manufacturer saw savings on component failures that ran in the hundreds of millions of dollars.  

3.) Employee Safety

Now more than ever, manufacturers are having to pay extra close attention to the safety of their employees. Not only does the volatile industrial workspace provide heightened risk of injury, pandemic concerns and infection now double worries over improper manufacturing employee safety programs. And while the circumstances are rather unfortunate, they give us an opportunity to observe a moment where artificial intelligence in manufacturing has transformed and developed new use cases to meet current needs. 

Utilizing the same high definition cameras and complex algorithms, AI programs are beginning to automatically notify employees when they forget to maintain proper social distancing efforts or wear their PPE. Not only that, using thermal imaging, these programs are even notifying managers of employees who exhibit signs of fever, allowing them to be sent home as quickly as possible to protect the health and well-being of healthy workers on the floor.  

This kind of early detection can prove to be essential in preventing cluster outbreaks of sickness for businesses like Ebm-papst Group in Germany who was able to maintain production throughout the breaking of the COVID-19 pandemic.   

AI programs such as these, coupled with industrial fanless computers that curb the spreading of contaminants, can create a rather fortified barrier against spreading illness on the floor. 

Why AI Projects Fail in Manufacturing

Taking a moment to observe all of the benefits and interesting artificial intelligence in manufacturing industry examples, it’s no wonder the tech has received the buzz it has. So then, what’s behind the repeated failures mentioned by IDC’s study? Unfortunately, a few things.

1.) Over-Excitement

Excitement is natural when dealing with something as potentially groundbreaking as AI on your factory floor. Unfortunately, over-excitement and an unclear expectation of the process behind deploying an effective AI implementation can kill the program before it’s even had a chance to shine. 

Many AI programs require meticulous training and testing before they can be deployed on the factory floor. This also means a single program is likely to go through multiple iterations and failures before the “just right” solution is found. Many times, efficiency-focused managers get too excited and quickly deploy their AI programs before they’re truly ready and, when they inevitably fail, they become disillusioned with the process as a whole and revert back to their old ways for fear of lost productivity.  

2.) Not Enough Data

Artificial intelligence in manufacturing is only as good as the data used to train it. If an AI program doesn’t know what a faulty product looks like, it’s not going to be able to catch one on the line and notify staff. 

There are a thousand disruptions that can occur on the factory floor and for an AI program to be able to catch and fix these issues, it needs to know what to expect. Thus, if you don’t have a lot of data on a particular quality control issue, say, a broken screen on a device you manufacture, your AI program won’t be able to detect a broken screen on the line. 

3.) Poorly Defined Data

Even if you DO have enough data to train your AI on all sorts of disruptions, making sure that data is consistently defined and labeled is essential as well. 

Say two employees are in charge of managing the data being used to train your AI program. If these two employees have different opinions on what constitutes an issue worthy of being picked up by the AI, you could be training your program on a set of contradictory data. One employee, for example, can say a scratch on a device constitutes an issue to be picked up by the AI while the other believes that same issue can be ignored and only larger cracks need to be detected. Thus, some scratches are labeled as issues while some aren’t, dooming your AI to be inconsistent before it even hits the ground. 

4.) Training isn’t Continued

AI programs are like a muscle. When you stop training it, it atrophies and becomes less effective at its job. Like we mentioned, there are thousands of disruptions your factory can experience on any given day and some of those disruptions have yet to ever occur. 

As new breakdowns, quality issues, and concerns arise, your AI program needs to be retrained to include these new variables so they can be predicted, detected, and prevented. The longer you go not training your AI on new data, the more new issues your program will be forced to overlook until it stops being even remotely effective. 

How Do You Use Artificial Intelligence in Manufacturing Efficiently?

By now it should be apparent that AI in manufacturing isn’t a one-time investment. It takes multiple iterations to reap the incredible benefits it offers. So, if you continue to put time and effort towards creating a program for your own factory or supply chain, how do you avoid the pitfalls we mentioned above? Fortunately, the answer to this question is a little simpler than the answer to why these programs fail. 

Train Your Machines

Your goal should be constantly drawing in more data about what makes your supply chain tick and, more importantly, fail. The more data you have to train your AI on, the more prepared and resilient it will be to all manner of disruptions. 

Digital Twin technology can be incredibly versatile and helpful in training your AI programs without having to actually experience repeated breakdowns and quality control issues. If you know the types of breakdowns you fear occurring, you can use these programs to create digital replicas of your factory floor and run simulations of these disruptions. Using the data drawn from these virtual breakdowns, you can then train your AI to detect them in real life.

Regardless of whether you employ digital twin tech or not, you’ll want to make sure your IoT sensors and the workstations benign used to gather that data are on the up and up. Din rail computers  designed to be industrial grade can seamlessly gather this data and store it securely with customizable hardware and peripherals optimized to collect said data.

Train Your Humans

Of course, even something as technological and automated as AI requires a fair share of human input.

To avoid poorly labeled and classified data, be sure to train your employees on what constitutes an error or quality issue, or really any kind of red flag sign that needs to be picked up and detected by your AI program. The more consistent your IT team is with their classification of these red flags, the more accurate and consistent your AI program will be. 

Artificial Intelligence in Manufacturing Requires Time, But it’s Well Worth it

Manufacturing-optimized AI is an investment in more ways than one, however, its benefits and use cases for both the present and the industrial 4.0 factories of the future have been well documented. If you feel like your plant can benefit from the futuristic tech, you owe it to yourself to set up the proper infrastructure needed to create a truly versatile AI program. For more information on how you can set that infrastructure up, contact an expert from Cybernet today.