Radwell International’s Blog

In the fast-paced industrial landscape, the emphasis on efficiency and productivity has never been greater. However, alongside these critical drivers, quality control remains a cornerstone of manufacturing and industrial processes. With the advent of automation, new techniques and tools are reshaping quality control paradigms, offering new ways to maintain high standards while boosting efficiency and reducing costs. Let's dive into the world of quality control in the era of automation.

What is AI?

AI, or artificial intelligence, is a science of programming computers to have the ability to make human decisions. AI basically simulates human intelligence. It can be used in a wide range of scenarios. The manufacturing industry uses AI to help with productivity and it can be used on tasks as small as talking to Siri on an iPhone.

Artificial intelligence can have a great impact on the modern warehouse in terms of safety, productivity, and accuracy. What is artificial intelligence and where does it fit in a warehouse environment? The definition of artificial intelligence, according to IBM, is “a field, which combines computer science and robust datasets, to enable problem-solving. It also encompasses sub-fields of machine learning and deep learning, which are frequently mentioned in conjunction with artificial intelligence. These disciplines are comprised of AI algorithms which seek to create expert systems which make predictions or classifications based on input data.” Ideally, AI is comprised of systems that think and act rationally.

Here are a few of the many AI functions that can have a positive effect on warehouse operations:

The factory of the future represents a transformation from traditional automation to fully connected and flexible systems using streams of data from connected operations. Production environments learn and adjust to new demands. Here is a framework of six key steps to guide you along that journey, regardless of a plant’s current maturity level.

As technology continues to grow and expand, so does the Industrial Revolution. There are four established stages as of now, with a fifth beginning to take shape. It started with the first stage of mechanized production. During this stage, water wheels and steam engines were created, and manufacturing moved from manpower to machine power. The second stage was mass production. A major technological advancement was achieved in the form of electricity. This technological advancement allowed for the creation of assembly lines. The third stage was the Digital Revolution. Analogue electronics and mechanical devices were expanded into digital technology such as personal computers, the Internet, and information and communications technology.

Engineers play a critical role in integrating legacy systems into the digital landscape of today’s businesses. The new-age enterprises thrive on technologies such as artificial intelligence (AI) and machine learning (ML), Big Data and analytics, and robotic process automation (RPA). To optimize these technologies, organizations need to either overhaul their operations completely or make use of the existing setup and intelligently transform them as per the business needs. This makes strategizing a very important gamut of the digital transformation exercise.

A complete overhaul is not only cost-intensive but also risks compromising business continuity. As a result, organizations generally opt for the latter option and gradually transition legacy systems while keeping a close look at the lifecycle.

The work of MIT computer scientist Aleksander Madry is fueled by one core mission: “doing machine learning the right way.”

Madry’s research centers largely on making machine learning — a type of artificial intelligence — more accurate, efficient, and robust against errors. In his classroom and beyond, he also worries about questions of ethical computing, as we approach an age where artificial intelligence will have great impact on many sectors of society.

“I want society to truly embrace machine learning,” said Madry, a recently tenured professor in the Department of Electrical Engineering and Computer Science. “To do that, we need to figure out how to train models that people can use safely, reliably, and in a way that they understand.”

Few industries have seen more transformation over the past decade than that of the pulp and paper industry. Gone forever is the ability of the business to rely heavily on staples such as newsprint and glossy magazine paper. Use of electronic devices and media have reduced considerably even the need for paper in the office environment.

Add to this the constant external pressures from government and other organizations pushing for stricter environmental standards. To compete, the paper and pulp industry must embrace efficiency producing new technologies that can save time and money.

While some see machine learning and related technologies as a threat to companies and jobs, these developments will help ensure the long-term survival of both.

If you’re a manufacturing plant manager, what don’t you want to see out to the production floor? Probably a significant number of things, but near the top of that list would probably be a large group of workers congregating around a critical piece of machinery that should be running—yet isn’t. Rarely does such a sight have positive implications.