In the mid-1700, the first industrial revolution changed the face of our society by upgrading the way goods were produced. At the core of this were coal-fueled machines. Fast-forward three hundred years, the manufacturing sector represents a huge chunk of human-caused carbon emissions. Within the United States, it is estimated that one-third of carbon emissions are originating from the industrial sector. While we can't change the past, our focus is now shifting to ensure a cleaner future.

Lean manufacturing tends to reduce waste and make the production process more cost-effective and efficient as well. The primary focus is to streamline production operations while enhancing customer experience. To make this possible, lean manufacturing optimises production operations by emphasizing customer needs. Overall, lean manufacturing is about terminating things that do not add value, therefore adhering to delivering the product based on the needs of the customers and fulfilling the expectations, says Ritesh Sutaria, co-founder of Prompt Softech. Using a low-code platform with manufacturing can support manufacturers in solving several issues.

Closed-loop manufacturing is central to Manufacturing 4.0 automation, but it's also been in place on production floors for years. But can it be automated to work with little or no human intervention? A closed-loop system on a production floor is a set of machines utilized in manufacturing that communicate and coordinate with each other to get certain processes done. The only catch is when something goes wrong and an alert is issued. At that point, a human has to step in to resolve the issue.

Industrial manufacturing as a sector has been an early adopter of robotics and other forms of technological improvements for decades. Robotics have been one of the best options to increase production efficiency for large and often highly repetitive manufacturing processes. But the era of producing large quantities of just a few products with low mix is coming to an end, giving way to increased product personalization requiring a more flexible production process with less waste than ever before. Fortunately, the future of manufacturing is brimming with opportunity. It is full of new technologies designed to reduce waste and maximize process efficiency and flexibility through software and hardware capabilities.

There's no doubt that the manufacturing sector is leading the way in the application of artificial intelligence technology. From significant cuts in unplanned downtime to better designed products, manufacturers are applying AI-powered analytics to data to improve efficiency, product quality and the safety of employees. In manufacturing, ongoing maintenance of production line machinery and equipment represents a major expense, having a crucial impact on the bottom line of any asset-reliant production operation. Moreover, studies show that unplanned downtime costs manufacturers an estimated $50 billion annually, and that asset failure is the cause of 42 percent of this unplanned downtime. What Is Consumption-Based IT and Is It Right For Your Business?

For all the focus manufacturers have been placing on digitisation, and especially on intelligent automation technologies, AI has yet to have a significant impact on the factory floor. This is about to change, believes Harald Bauer of McKinsey. "Until now, AI has been applied in a few niche areas by some, though by no means all, manufacturers," he says. "The enablers are in place, however, to allow more manufacturers to apply AI in a wide range of uses, and at scale." These enablers include high existing levels of digitisation and automation, the availability of voluminous data and access to the enormous computing power existing in the cloud.

There's no doubt that the manufacturing sector is leading the way in the application of artificial intelligence technology. From significant cuts in unplanned downtime to better designed products, manufacturers are applying AI-powered analytics to data to improve efficiency, product quality and the safety of employees. In manufacturing, ongoing maintenance of production line machinery and equipment represents a major expense, having a crucial impact on the bottom line of any asset-reliant production operation. Moreover, studies show that unplanned downtime costs manufacturers an estimated $50 billion annually, and that asset failure is the cause of 42 percent of this unplanned downtime. For this reason, predictive maintenance has become a must-have solution for manufacturers who have much to gain from being able to predict the next failure of a part, machine or system.