Intelligent CIO Europe Issue 47 - Page 75

AI ENABLEMENT IS MOST COMMONLY USED BY MANUFACTURING INDUSTRIES TO INCREASE OVERALL EQUIPMENT EFFICIENCY AND YIELD .
INDUSTRY WATCH

AI ENABLEMENT IS MOST COMMONLY USED BY MANUFACTURING INDUSTRIES TO INCREASE OVERALL EQUIPMENT EFFICIENCY AND YIELD .

wWhat benefits does AI bring to manufacturing ?

As a collective and sometimes rather omniscient term , Artificial Intelligence ( AI ) includes the capabilities of learning systems that are perceived as intelligent by humans . AI and Machine Learning ( ML ) technologies have become top priorities in manufacturing since they allow firms to alter business models , invent operational paradigms to support those models , and monetise information to achieve higher levels of productivity .
Beyond hypes and fads , AI works because it amasses significant benefits for the manufacturing sector , such as enabling smart production , developing predictive and preventative maintenance , offering supply chain optimisation , improved safety , product development , and optimisation , facilitating AR / VR ( Augmented and Virtual Reality ), cost reduction , quality assurance and enabling green operations ( energy management ), to name a few . AI enablement is most commonly used by manufacturing industries to increase overall equipment efficiency and yield . AI is also being utilised generally as a tool to improve productivity , quality and consistency , which helps manufacturers forecast more accurately .
It ’ s safe to say that the manufacturing industry continues to be driven by AI and ML technologies . UST has observed the critical use of AI in transforming operations , improving product quality and reducing costs through various methods including smart operation , design prediction , quality assessment of products and more .
Why is the adoption of AI in manufacturing accelerating ? pads for manufacturers to embark on their cognitive computing journey – intelligent maintenance , intelligent demand planning and forecasting , and product quality control .
The deployment of AI is a complex process , as with many facets of digitisation , but it has not stopped companies from moving forward . The ability to grow and sustain the AI initiative over time , in a manner that generates increasing value for the enterprise , is likely to be crucial to achieving early success milestones on an AI adoption journey .
Manufacturing companies are adopting AI and ML with such speed because by using these cognitive computing technologies , organisations can optimise their analytics capabilities , make better forecasts and decrease inventory costs . Improved analytics capabilities enable companies to switch to predictive maintenance , reducing maintenance costs and reducing downtime .
Why is predictive maintenance important in manufacturing ?
The use of AI allows manufacturers to predict when or if functional equipment will fail so that maintenance and repairs can be scheduled in advance . It ’ s important because machines can operate more efficiently – and cost-efficiently – when AI-powered predictive maintenance is used . The ability to predict breakdowns and optimise scheduling before the equipment fails makes AI excellent for maintaining reliable equipment and maintaining smooth production .
What are the challenges of incorporating AI into manufacturing processes ?
Adnan Masood , PhD ., Chief Architect – AI / ML at UST
There are few key advantages which make the adoption of AI particularly suitable as launching
We see that scaling AI implementations beyond a Proof-of-Concept ( POC ) level remains one of the
www . intelligentcio . com INTELLIGENTCIO EUROPE 75