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The key stages to deliver Artificial Intelligence into manufacturing production

Where tasks are either too repetitive, or too hard, for humans – then these should obviously be assigned to , freeing up operatives to focus on where they retain a comparative functional advantage.

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SwissCognitiveAs part of his keynote for The Manufacturer’s Industrial Data Summit, Bala Amavasai, Head of and Lead Architect at Stanley Black & Decker, explored the benefits of for manufacturers and some of the ways it can be implemented into businesses.

Stanley Black and Decker are at the forefront of delivering digital solutions to industry and operate across manufacturing verticals, with a vision for digital transformation to bring in cutting-edge technology to solve the hardest problems.

The ambition is to build connected factories across the globe using technologies including: and ; ; digital apps and digital twins. Bala Amavasai Head of
and Lead Architect,
Stanley Black & Decker Bala explained, “Among these technologies, is at the centre of building connected factories; but when we speak about , we are not only referring to , in fact it is much more than . It is , statistics and all those technologies that fall within the domain”. The benefits of in manufacturing

According to Bala, the main reasons why has taken off falls into four categories: Data quantity; computing power; convolutional neural networks and marketing and media.

“The amount of data has grown exponentially. And the collection of data continues to proliferate in all aspects of business and society. In manufacturing, for example, we find vast amounts of data and we have got single instruments producing 40-dimensional data sets, so we’ve got a huge amount of data.

“There is also a huge amount of computing power with the element of the GPU, so we can either process in the cloud, or the edge, and computing power is very cheap. Combined with the development of neural networks, which has been a game changer for , there are new solutions opening up for manufacturers across the board.”

Machines “are already much better at doing certain tasks than humans” Bala noted and therefore it is simply a question of using the right technology tools to do the right jobs.

This is particularly evident in the field of pattern recognition and image classification – where algorithms have been outperforming humans for more than 15 years.

Where tasks are either too repetitive, or too hard, for humans – then these should obviously be assigned to , freeing up operatives to focus on where they retain a comparative functional advantage.

Bala noted that, “The most important use cases for industrial are to improve the accuracy, consistency and rate of throughput for manufacture.”

Delivering into manufacturing production

Software development is critical to the delivery of . You need to build the right infrastructure in order to carry out a successful implementation including, your own full specifications; development test production server stages; QA teams, and your own production teams that can deliver a 24/7 operation. […]

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