Digital modernization of manufacturing driven by AI/ML and data, is the next frontier for AI SaaS/PaaS solutions

In the past, the practice of machine control was a complex task that required people to be present at all stages of the production process. But with new digital technologies, this approach is changing. An exciting trend in software engineering and industrial automation is making it possible: Digital modernization in manufacturing and industrial environments.

The concept of digital modernization in manufacturing has been around for quite some time. In fact, the trend is well under way, though it may not be apparent to many. So, what is Digital Modernization? It's the trend of moving to software-defined machines. Digital modernization is taking off in manufacturing because it's a way to increase efficiency, lower costs, and improve collaboration in the factory.

Digital manufacturing refers to a set of technologies that help manufacturers monitor, collect, analyze, and act on data from connected devices and systems in real time. These technologies include sensors, wireless communications, cloud computing, big data analytics and the Industrial Internet of Things (IIoT). The IIoT is a network of physical objects—devices, vehicles, buildings, and other items—embedded with electronics, software and sensors that enables these objects to collect and exchange data.

You may have heard the maxim “data is the new oil” but what does this really mean? It’s a reference that shows the most precious resource for industrial customers isn’t coal or electricity, it’s data. And most modern industrial companies are somewhere along their digital transformation journey to “Industry 4.0” manufacturing, the latest industrial revolution driven by software, artificial intelligence, and massive connectivity.

The main drivers of digital transformation in manufacturing (or any industry) are increased competition, supply chain disruptions, and the expectations of digitally native customers. By leveraging digital technologies, manufacturers can improve speed and efficiency, increase production, decrease costs, and provide better customer experiences

Digital modernization of manufacturing driven by AI/ML and data, is the next frontier for AI SaaS/PaaS solutions. In this new age, with data generated by sensors and digital systems, solutions that allow businesses to monitor and react to real-world processes, will allow for more flexible modes of production—these solutions will be the seeds for the factories of the future.

Challenges that an industrial AI system will face

Now more than ever, we have seen success among consumer software AI solutions. Their architects have figured out how to bring timely solutions to the market with efficiency. These solutions have seen success because they are often serving similar use cases and end users. They also gather data from millions of users allowing them to create smarter AI models.

However, with all the successes in consumer AI, when you move over to the industrial environment, you are facing a completely different beast. Why is this so? Well, let us try to explore some of the challenges that an industrial AI system will face:

Bigger Data Volumes

Industrial applications are likely to be much bigger in data volume than consumer applications. For example, an industrial application might have tens of thousands or even millions of data points stored on multiple machines. This is a significant challenge for any real-time system given that having access to all data points at any given time is critical for making decisions in real time.

Less Data Openness

In the consumer space you can get access to most of the information that you want from various sources. Industrial clients often lack the “big data” needed to create robust and smart AI models. For example, Google Images offers access to a wide variety of images that can be used in training neural networks for image recognition.

In contrast, if you want to use the same image recognition technology for an industrial application like inspection of cargo containers at a port, then it would be very difficult to get access to high quality images of cargo containers and their contents taken at different angles and lighting conditions as well as videos of these containers being loaded and unloaded, for example.

Summarily, the source of data for industrial AI systems is usually proprietary and rarely shared with others, while consumer AI is more open source and almost readily available.

Lack of Suitable Infrastructure 

Many industrial environments lack the sensors or smart equipment needed to gather process related information. A wide range of companies are working on AI solutions to their business needs, but the technology isn't quite there yet. That's because the environments in which these companies operate don't yet have sufficient sensors or smart equipment that can gather data that artificial intelligence will require to get the job done.

There are many reasons for this gap, including a lack of computing power and advanced software and hardware. As a result, AI experts must manually take measurements and enter data into existing systems by hand.

This is a big obstacle to progress, since manual data entry is time-consuming and expensive, especially as companies begin to roll out AI at scale. In addition, AI requires massive amounts of data to function, making it even more challenging for organizations to develop effective solutions.

The difference between industrial and consumer AI

In a nutshell, the enormous differences can be defined by the following:

• ·One of the biggest challenges facing companies implementing AI in the workplace is the difficulty in training AI to achieve goals. Unlike consumer AI, which is designed to perform tasks that are easy for humans to explain, industrial AI is designed to perform tasks that are much more difficult and nuanced. Instead of teaching a robot to pick up an object, industrial AI must teach robots how to make extremely high-level decisions.

• ·AI in the workplace faces an additional challenge due to the fact that it requires heavy collaboration with human workers. While consumers might not mind if their autonomous service robot gets confused, industrial AI mistakes can result in serious consequences. The stakes are much higher in the workplace than they are in the home.

• On one hand, an industrial AI agent should achieve maximum performance as quickly and efficiently as possible. On the other hand, this agent needs to work reliably under any operating conditions. It must meet high standards of quality and be extremely reliable.

Conclusion

Industrial AI is a huge opportunity for manufacturing companies, but it all starts with the ability to ingest and process big data for AI applications. As we move toward a software defined factory of the future, the data acquisition and analysis for AI applications will be the first big hurdle for industrial AI solutions.

As more and more industrial giants acknowledge that digital modernization is necessary in creating industry 4.0, more software enters the manufacturing environment. While SaaS/PaaS and AI/ML solutions focuses on digital modernization as the next frontier, it all starts with the ability to gather and process data.

Winning solutions on this continuum will need to devise repeatable and consistent ways for acquiring data. It all starts with the ability to curate and label data to allow companies to develop reliable AI based systems using AI, resulting into faster growth.

Industrial AI requires a different approach than that used by consumer AI and internet companies. It's time to make cutting-edge AI accessible to everyone!