Manufacturers face many challenges of ensuring global supply chains and delivering global orders on time. Customer demands can change rapidly, bad weather and strikes can result in delays or cancellations of component parts, and machines do break down.
In order to address these issues, manufacturers are turning to AI technologies to streamline processes, reduce costs and downtime while maintaining quantity and quality of products. One key aspect of AI, is the ability to find insights in real time, in large data sources that humans would not be able to assess and analyse quickly enough.
For example, in machine learning based AI systems, the algorithms at the heart of AI can be used to predict when maintenance is needed, monitor and provide recommendations to improve quality, provide guidance on root cause analysis, improve yields and much more. AI not only enables these analytics, but by looking and learning from the data, the insights delivered can be tailored to specific applications.
Ethics and safety
There are however a number of points which must be dealt with, such as ethical and societal concerns such as the safety of machines working side by side with people in manufacturing plants, and the overall safety of AI enabled systems from a data perspective.
Other important questions have also been raised around governance of such systems used in organizations, such as who is responsible for making decisions and managing how such systems are deployed? Do managers have enough of an understanding of the wider implications?
How standards can help
Standards are essential to removing barriers to deployment, addressing concerns and ultimately accelerating adoption.
Wael Diab, who currently heads standards development for AI technologies and discusses the work being carried out with IEC and ISO families, which considers the entire AI ecosystem. He elaborates on the standards being developed for Big Data, trustworthiness, bias and robustness of neural networks, terminology and foundational frameworks, risk management and more, which can be used across diverse industries using AI technologies.