Artificial Intelligence is no longer just a futuristic notion, it’s here right now – software that senses what we need, supply chains that ‘think’ in real time, and robots that respond to changes in their environment. The essence of the AI paradigm shift is the transformation of all business processes within an organisation. It is changing all the rules of how companies operate.

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.

Artificial intelligence also holds the key to future growth and success in manufacturing. In a recent Forbes Insights survey on artificial intelligence, 44 per cent of respondents from the automotive and manufacturing sectors classified AI as “highly important” to the manufacturing function in the next five years, while almost half – 49 per cent – said it was “absolutely critical to success”.

Today, humans and robots collaborate to produce breakthroughs, thanks to the ‘marriage’ of advanced manufacturing techniques with information technology and data and analytics.

Let’s have a look at key revolutions AI brings to the manufacturing industry.

Computer vision is being used to optimise production lines, and digitise pro­cesses and workers. In manufacturing, the application of ‘machine vision’, which automates image analysis and directs the robot workforce on the shop floor, is a growth area. On the production line, the most prominent use cases are for inspecting parts and products, controlling processes and equipment, and flagging ‘events’ and inconsistencies. The trick is to take what would seem like the next logical step – sending those images to a person to make judgements and corrections – and hand that over to the machine as well. 

Artificial intelligence also holds the key to future growth and success in manufacturing

Artificial intelligence is also changing the way we design products. One method is to enter a detailed brief defined by designers and engineers as input into an AI algorithm or ‘generative design software’. The brief can include data describing restrictions and various parameters such as material types, available production methods, budget limitations and time constraints.

The algorithm explores every possible configuration, before homing in on a set of the best solutions. The proposed solutions can then be tested using machine learning, offering additional insight as to which designs work best. The process can be repeated until an optimal design solution is reached.

Although digital twins have been around for several decades, it’s only been since the rapid rise of internet of things (IoT) that they’ve become more widely considered as a tool of the future. At its simplest, a digital twin is a virtual replica of a physical pro­duct, process or system. Digital twins act as a bridge between physical and digital worlds by using sensors to collect real-time data about a physical item.

This data is then used to create a digital duplicate of the item, allowing it to be understood, analysed, manipulated or optimised. Digital twins are getting attention because they also integrate things like artificial intelligence and machine learning to bring data, algorithms and context together, enabling organisations to test new ideas, uncover problems before they happen, get new answers to new questions, and monitor items remotely.

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 per cent of this unplanned downtime.

Predictive maintenance – as opposed to preventive maintenance – eliminates guesswork as the machines report their conditions on an up-to-the-minute basis. It also saves businesses valuable time and resources, including labour costs, while guaranteeing optimal manufacturing performance. As with digital twins, sensors and advanced analytics embedded in manu­facturing equipment make it possible. They enable predictive maintenance by responding to alerts and resolving machine issues.

On the one hand, missing the AI wave in manufacturing could mean getting stranded. And for some, that will be a reality. If you’re stuck to the old way and don’t have the capacity to digitalise manu­facturing processes, your costs are probably going to rise, your products are going to be late to market, and your abi­lity to provide distinctive value-add to customers will decline.

Yet it’s not too late to adapt the manufacturing process, and the sector at large, to the changes already taking place. We’re clearly in the early stages of the sea-change, and there remains a long way to go before this vision is fully realised.

Joseph Micallef is chief operations officer and partner at BEAT Ltd.