It’s impossible to miss the rapid rise of Artificial Intelligence in manufacturing, or Industrial Artificial Intelligence. It seems like every other technology-related article — and practically every single vendor vying for our attention — uses terms like AI, machine learning, digitization, automation, Industry 4.0, Industrial AI, etc… almost as a form of punctuation.

As a result, manufacturers’ views of AI range from unrealistic (“AI can solve all our problems!”) to skepticism any time the words “Artificial Intelligence” are even uttered.

But, as with any technology, the truth is really somewhere in between.

AI can be extremely effective — in the right context.

That’s the key. Understanding those contexts, and the kinds of AI technology that are relevant to them, is the only way to set realistic business goals for AI adoption.

Introducing The Industrial AI Quadrant

Watch: How to select the right Industrial Artificial Intelligence solution:

As a rule of thumb, AI works best when it is applied to solving a specific problem, or a very closely-related set of problems.

Artificial Intelligence is not a silver bullet; no solution will solve all, or most, of your problems. “General AI” is something to be wary of: if a vendor claims to do everything, then they probably do nothing particularly well. When it comes to AI in manufacturing specifically, there are numerous potential applications and use cases, each of which requires a unique type of Artificial Intelligence.

Sounds simple enough, right?

There’s just one problem: most manufacturing executives don’t have a PhD in data science. Even if they do, they certainly don’t have the time to pour over the technicalities of whether each vendor is using the optimum type of algorithm.

Fortunately, you don’t need to do that. Seebo’s Chief Data Scientist has invented a highly effective methodology to select the right Industrial AI solution to address any given manufacturing challenge. We call it “The Industrial AI Quadrant”.

The Industrial AI Quadrant:

The focus: reducing production losses that hurt your bottom line

The Industrial AI Quadrant focuses specifically on perhaps the most significant application of Industrial Artificial Intelligence: reducing the losses incurred during the manufacturing process — things like quality, yield, waste and downtime. These losses hurt manufacturers’ bottom line and are a major source of headaches.

To address this challenge, manufacturers are increasingly turning to Machine Learning — a specific category of Artificial Intelligence — which can provide insights, predictions and recommendations to prevent these losses.

Of course, there are multiple types of losses, occurring to varying degrees and with varying frequency across different industries. “Machine learning” is a broad term (despite being a sub-category of the even broader term “Artificial Intelligence”); there are different types of Machine Learning, which are each suited for a particular type of problem.

Process-driven vs. asset-driven

One of the most common crossroads when choosing Industrial Artificial Intelligence technologies to address production losses is the question of where those losses are rooted.

Asset-driven losses, for example, are a common cause of losses due to downtime. In recent years, a variety of Predictive Maintenance solutions have arisen, which address this specific question using Unsupervised Machine Learning: when is my machinery going to break down or otherwise cause losses?

But the AI technology used for Predictive Maintenance — while highly useful in that context — isn’t suited for solving a different pressing challenge many manufacturers face: process-driven losses; things like quality, waste, yield and throughput losses. These types of losses are caused by inefficiencies within the production process, rather than asset-related problems. Since many manufacturing processes are highly complex (particularly in the world of continuous process manufacturing), process experts and engineers can’t possibly figure out all the root causes of these inefficiencies themselves.

To address these process-driven losses, manufacturers need an altogether different kind of Machine Learning AI. — one that understands the unique complexities of each individual production line, and can deliver the insights production teams need to prevent production losses. That’s what Seebo’s proprietary Process-Based Artificial Intelligence is designed to do (see here for more details).

Choosing wisely

Leading manufacturers are already using the Industrial AI Quadrant as a simple but effective methodology to navigate this complex ecosystem, and zero-in on the technology that can solve their specific business problems.

Download the guide to discover how: