The days of high margin manufacturing are dwindling. As the manufacturing sector faces a more complex consumer landscape and reduced access to resources, the need to innovate is ever growing. Companies from a range of industries, from automotive to consumer products, are focusing on achieving cost reduction and better operational performance. Digital technologies are the key enablers for this.

We call it “Industry 4.0”, where highly intelligent connected systems create a fully digital value chain, driving a step-change in production quality and output. It stems from the combination of technologies like Internet of Things (IoT), cloud computing and artificial intelligence (AI), which have the power to transform conventional plants into smart factories. Machines can ‘talk’ to products and other machines, continuously capturing and processing data in real time, so decisions can be made automatically across a distributed industrial ecosystem.

One of the more interesting, yet possibly under-rated technologies in the manufacturing industry is AI. AI, or ‘cognitive computing’, is helping us solve problems and create opportunities that previous generations could not even imagine. That’s largely because AI technologies like IBM’s Watson have enabled us to tap into ‘unstructured’ data for the first time.

Traditional IT systems could only analyse structured or ‘spreadsheet’-type data. Yet unstructured data, such as books, images, audio, video, social media posts and much more, makes up 80% of all data, and has been virtually inaccessible until now. Watson on the other hand, can understand natural language, it can listen to music, analyse images and even detect emotions.

What does that mean for manufacturers? Traditionally, technology systems focused on asset maintenance. That is certainly important, but cognitive manufacturing can also improve asset efficiency, achieve higher workforce productivity and increase the quality of products produced.

For example, cognitive technologies can detect product defects in real time – ensuring that imperfect goods are removed quickly before they are shipped. An AI engine is trained on what each defect looks like by giving it examples of typical defects. On the production line, products are photographed as they move through the assembly process, and the AI system analyses those images in real time. It can immediately detect product defects – even something as small as a pinhole-size puncture – and alert the assembly line workers to rectify it or remove the product from the line.

This type of Cognitive Visual Recognition technology is in its early stages, and yet, it has already been shown to reduce manufacturing defects by up to 10%. The real power however, stems from the fact that anyone can train Watson, and you do not need to be an expert in Machine Learning or even Image Analysis to leverage it.

Maximising the potential of today’s technological capabilities in the manufacturing industry will offer us incredible possibilities to not just enable new levels of productivity, but also ensure that quality is flawless and waste is minimised. This is why we can say we have entered a new Industrial era and IT is playing a major part in that.

Elinor Swery is an alumna of the Centre for Innovation and Entrepreneurship’s Velocity programme. Elinor is Senior Consultant, Digital Strategy at IBM.