In the dynamic world of manufacturing, Artificial Intelligence (AI) is no longer a futuristic concept but a transformative reality. AI, with its ability to mimic human intelligence and continuously learn from its environment, is revolutionising the manufacturing sector, ushering in a new era of efficiency, productivity, and innovation.

In this article, we will delve deeper into the growing market for AI in manufacturing, exploring current trends and making predictions for the future.

Understanding AI in the Manufacturing Industry

AI in the manufacturing industry refers to the integration of AI technologies into manufacturing operations and processes. This includes machine learning algorithms that can predict equipment failures, robotics that automate repetitive tasks, AI systems that optimise supply chains, and algorithms that optimise material efficiency supporting sustainability goals to name a few. The goal is to enhance decision-making, improve product quality, and facilitate more efficient and flexible production processes.

The importance of AI in manufacturing cannot be overstated. As we navigate through the 21st century, the manufacturing landscape is becoming increasingly competitive. Companies are under constant pressure to improve efficiency, reduce costs, and deliver high-quality products faster than ever before. In this context, AI serves as a critical tool, enabling manufacturers to meet these challenges head-on and stay ahead of the curve.

As manufacturers transition towards smart factories, AI is at the forefront, driving this transformation and shaping the future of manufacturing.

Federated Learning
Federated learning, a technology that allows for the decentralised training of machine learning models, will become increasingly important in global manufacturing. It will allow manufacturers to apply AI in a custom manner at each site, product, or edge device while still sharing insights across similar products globally​.

Predictive Quality
While predictive maintenance has been a major focus for AI in manufacturing, the future focus is shifting towards predictive quality. This aspect focuses on the health of products and processes rather than the health of assets. It can help manufacturers maximise overall performance in terms of throughput and quality and has the potential to significantly improve manufacturing outcomes​.

Digital Twins
AI is enabling more precise manufacturing process design and problem diagnosis using digital twins, which are virtual replicas of the physical components involved in the manufacturing process. 

Factory Optimisation
The scope of AI applications extends beyond just the manufacturing process. Consider its implications in the realm of factory planning. Several factors, ranging from worker safety to process flow efficiency, dictate the layout of a facility. There could be a need for the facility to be adaptable to facilitate numerous short-term projects or constantly evolving processes.

Such frequent alterations may give rise to unexpected spatial and material clashes, potentially leading to issues concerning efficiency or safety. However, these conflicts can be monitored and quantified with the help of sensors. AI plays a significant role in refining and enhancing the layout of factories, thus optimising their operations.

Recognised as a significant frontier of innovation for the future, with some aspects already in fruition, is the ‘Factory in a Box‘ (FIAB), and more specifically, its advanced version – FIAB 2.0. For the past two decades, this concept has been under continuous exploration and holds a transformative potential for the global supply chain. By offering more efficient and modular production alternatives, FIAB can reshape the way we manufacture and distribute goods. 

In essence, the FIAB system is a compact manufacturing unit capable of swift deployment, remote management, and flexibility to adapt to varying consumer demands. It embodies a revolutionary way to approach production and supply chain management. A FIAB system creates the opportunity to effectively package an end-to-end work process to sell to a manufacturer, including everything from software to the physical machinery in the factory, the digital twin, the ordering system that exchanges data with the factory’s supply-chain systems, and the analytics to monitor manufacturing methods and collect data as inputs move through the system.

The potential of FIAB systems extends to fulfilling two fundamental objectives of modern industrialisation: decentralisation and digitalisation. Decentralisation, achieved through the use of robotics and networked sensors, allows FIAB systems to operate autonomously, making data-driven decisions without the need for human intervention. This not only streamlines the process but also increases efficiency.

Digitalisation, on the other hand, is about integrating cutting-edge technologies like artificial intelligence (AI), robotics, and the Industrial Internet of Things (IIoT) into the FIAB system, creating FIAB 2.0. This combination of technologies amplifies process optimisation, boosts accuracy, and enhances productivity.

An added advantage of digitalised FIAB systems is the ability to control operations remotely. This can facilitate safer handling of hazardous materials and help shorten the supply chain, all while maintaining high product quality.