The manufacturing sector is constantly evolving. Smart manufacturing, the use of connected technologies and automation to improve production processes, is increasingly becoming the focus of many companies. Its potential applications are vast, with improvements in accuracy, efficiency, cost savings and better customer service being just some of the benefits. As we approach 2023, a number of key smart manufacturing trends will become even more important for companies to consider if they wish to keep up with the competition.
How to define smart manufacturing
Before we continue, let us recall what is meant by smart manufacturing, through Gartner‘s definition:
“Smart manufacturing refers to the orchestration of physical and digital processes within factories and other supply chain elements to optimize current and future supply and demand requirements. This is done by transforming and improving how people, processes and technologies operate to provide the critical information needed to impact quality, efficiency, cost and decision agility.”
The smart factory, in short, is one in which data and information contribute to process optimization on the one hand and agility of decision-making and transformation on the other.
Key 2023 trends for smart manufacturing
As we know, the manufacturing sector is facing a period that is not easy, due to various contingencies, including the ever-increasing cost of energy and the poor availability of raw materials. Despite this, some of next year’s trends will be about innovation and not just the quest to contain costs and waste, although these will play a key role. With this in mind, let’s look at some of the most interesting trends that are likely to involve smart manufacturing in the coming months.
Industrial Internet of Things will remain a rising trend
The Industrial Internet of Things (IIoT) has been a fast-growing trend in the past few years in the manufacturing sector, which has seen an influx of technological advances over the past decade. According to McKinsey, by 2023 there will be 43 billion connected devices . This system of interconnecting machines, devices and sensors to create a network of smart products is revolutionizing traditional manufacturing processes by enabling more efficient production and advanced automation capabilities. In 2023, IIoT will remain an essential component in smart manufacturing as it facilitates greater communication between various systems and increases overall productivity.
Companies will increasingly adopt IIoT solutions to improve existing workflows and customer experience, reduce downtime costs and increase overall profit margins. In addition, the technology enables real-time information on performance data that can be used to monitor progress in specific areas such as quality control or energy efficiency. It is precisely the latter that will predictably be the area of greatest development. Some analysts say that companies that already have industrial IoT in place will try to use it as much as possible to reduce energy consumption.
Predictive maintenance will further improve and be widely adopted
The use of predictive maintenance is estimated to grow rapidly in smart manufacturing in 2023. Predictive maintenance involves the use of real-time data from machines and systems to anticipate potential problems before they occur. This proactive approach helps manufacturers avoid costly downtime, increase production efficiency and reduce the risk of accidents in their facilities.
Leveraging technologies such as machine learning algorithms, sensors, analytics, artificial intelligence (AI) devices and the Internet of Things (IoT), predictive maintenance can be used in the connected factory to detect any signs of anomalies in advance. Through this process, problems can be identified in a timely manner, enabling faster resolution before it leads to accidents or production downtime. In addition, predictive maintenance provides detailed insights into asset performance so that corrective actions can be taken as needed.
Finally, a trend that is gaining momentum, starting with predictive maintenance itself, is to extend its paradigm. What is called predictive resolution or Predictive Resolution, uses similar principles to offer engineers guidance on how to solve predicted problems with a good margin of certainty. In other words, thanks to this new technique, it is possible not only to identify problems well in advance, but also to take more efficient action to solve them, often on the first try.
The Digital Twin will take center stage and begin to explore the metaverse
In 2023, digital twins will increasingly be an integral part of smart manufacturing. A digital twin is a virtual representation of a physical asset, such as a production line or machine, that can be used to monitor and optimize its performance in the real world. By combining data from multiple sources, including sensors, IoT devices and virtual reality, digital twins enable detailed information about their operations in near real-time.
Furthermore, it cannot be ignored how the numerous investments on the Metaverse have given a new boost to areas such as virtual reality and augmented reality. And while imagining companies beginning to interact with their assets through this platform is definitely a long shot, it is undeniable that the use of augmented reality and virtual reality have already led to some success stories in the recent past, especially in the maintenance of realities with numerous decentralized plants. With this in mind, the use of the Digital Twin to create highly accurate simulation models that help predict how changes in conditions may affect plant processes is undoubtedly one of the concrete possibilities for 2023.
Preparing for innovation
As we have seen, 2023 looks above all like a year of confirmation and even smarter use of available technologies. Given the international contingency, it is more than likely that it will be an “interim” year in which to consolidate the innovations of recent years. This undoubtedly presents an opportunity especially for companies intent on closing technology gaps.
