Everything you need to know about Generative AI in Manufacturing

Data Analytics for Manufacturing

Glossary post
If you’re a manufacturer, no matter what industry you’re in, chances are your goal is to achieve greater productivity, minimize waste, and improve product quality. Manufacturing data analytics refers to a variety of technologies that aim to make this possible using advanced insights based on data you collect from your manufacturing facility.
Glossary post

What is manufacturing data analytics?

If you’re a manufacturer, no matter what industry you’re in, chances are your goal is to achieve greater productivity, minimize waste, and improve product quality. Manufacturing data analytics refers to a variety of technologies that aim to make this possible using advanced insights based on data you collect from your manufacturing facility.

What is a Manufacturing Data Analytics

Today, factories and production facilities of all kinds are capable of generating a wide variety of data points, giving you a steady stream of information on the health of machines, efficiency of operations, quality of finished products, and more.

In fact, there’s almost too much information. With so much information being generated in real time, figuring out what to do with it can be truly overwhelming.

Taking full advantage of manufacturing data analytics can be a challenge, demanding that you connect production lines with software that can analyze that stream of non-stop data and produce useful, actionable information.

The truth is that many organizations don’t manage to achieve ROI from manufacturing data analytics projects. Either the task is too complex, requiring extensive research and development, or the system gets bogged down and cannot handle the challenges of manufacturing at scale, such as across multiple facilities.

But there are ways to simplify data analytics to streamline, optimize, and ultimately increase product quality and customer satisfaction.

What you need to know about manufacturing data analytics

Even the most traditional manufacturing businesses today have gone digital in order to remain competitive and reap a wide range of business benefits. Manufacturing data analytics refers to harvesting and leveraging data end to end across your entire process to boost quality, improve production performance, eliminate waste, and reduce costs.

Certainly in fields where margins are narrow or where ensuring quality is of the utmost importance, such as in the automotive or electronics industries, manufacturing data analytics promises to provide a massive competitive advantage.

What kind of data does manufacturing data analytics software use to accomplish this?

Manufacturing equipment. Today, machines and production lines are often capable of connecting and providing a stream of data—machine health, preventative maintenance, bottlenecks, and more.

Product quality. Automated inspection stations generate visual and other types of reports on product quality, not just at the end of the process, but along the way, often letting you catch problems before they affect customers.

Beyond the manufacturing floor. Most businesses can also mine a wealth of manufacturing-relevant data such as supply chain, inventory, demand, and pricing.

However, in order to implement data analytics, it’s not enough to ensure that manufacturing data collection is in place. The information must also be processed by a system capable of seeing the big picture and deriving insights in real time based on this data.

Data analytics systems accomplish this using a combination of automation, artificial intelligence (AI), machine learning (ML), and a smart platform that lets you drill down to understand what’s going on across all of your production lines.

What are some top use cases for manufacturing data analytics?

Data analytics use cases in manufacturing include a wide range of applications with practically infinite potential. Let’s look at three use cases based on the types of data identified above to see how manufacturing data analytics can improve production processes over traditional methods. Each of these use cases can cut costs and achieve far greater customer satisfaction.

Manufacturing equipment
Traditionally, it’s hard to know when a machine is going to fail. Preventative maintenance schedules attempt to make sure that nothing goes wrong during production, but these often have little to do with the actual state of the machine.

Manufacturing data analysis software can collect data from machines in real time and let you know if it’s struggling, slowing down, or causing defects—before it’s too late.

Product quality
Traditionally, defect detection has required visual or other inspection by a human expert at the very end of the production line. This is wasteful and inefficient. Not only is a dedicated expert required, but by the time you find problems, materials have been wasted. Worse, human error could lead to defective products reaching customers.

Manufacturing data analytics can help catch defects sooner, cutting waste and increasing overall quality and customer satisfaction.


How you can use manufacturing data analytics to optimize production
The question remains: If manufacturing data analytics can achieve such dramatic results, why aren’t more manufacturers using it?

The truth is, it hasn’t always been easy to implement.

But that’s changing with the advent of simpler data analytics platforms built with manufacturers’ needs in mind.

To achieve all the benefits of manufacturing operations analytics, you need a platform that is capable of handling big data. The software must ingest a vast amount of information and process it into actionable insights.

Historically, this required data engineering—a long, complicated cycle that demanded highly specialized developer roles:

  • First, a team of developers worked for months (or even years) to create a model that could start making predictions or deriving insights
  • Then, once the model was created, it needed to be trained, which also took time (and money)
  • Finally, once the model was in production, it worked for a while and then—for a number of reasons—inevitably broke as data changed or other “real world” factors got in the way, demanding that you return to Step One and begin all over again


Understandably, this exhausting, expensive cycle has been a barrier to quite a few manufacturers, who rightfully see themselves as manufacturers first and are reluctant to invest in a major software development project.

That means they haven’t been able to reap the benefits of manufacturing data analytics—until now. 

Today, Vanti offers a predictive platform built to provide AI-based solutions for manufacturers with no data engineering required. You won’t need a team of specialists to start reaping the advantages of manufacturing data analytics from day one.


Vanti lets you optimize the production process, tackling your most urgent challenges:

  • Early failure detection
  • Faster machine calibration process 
  • Visual defect detection at multiple points
  • Predictive analytics


Only Vanti has a deep understanding of manufacturers’ needs, focusing on helping you increase throughput while cutting waste to a minimum. And best of all, Vanti starts working right away, hassle-free.

You’ll get up and running with zero downtime and gain an intuitive interface that’s easy to understand and control. 

Let Vanti help you make manufacturing data analytics a reality—so you can optimize and strategize for the future of your manufacturing business.