Additional technologies and applications of AI include:
- Machine Learning. Machine learning is a form of AI that builds applications that learn from data, allowing equipment to continuously self-optimize and improve over time.
- Predictive Maintenance. Machine learning can be used to accumulate data in order to predict when maintenance activity should be scheduled, including which parts are likely to fail in order to prevent stoppages and disruption in the supply chain.
- Robotic Process Automation. Robotic process automation uses software that recognizes and learns patterns to perform tasks that may be difficult, dangerous, delicate, time-consuming or monotonous. This could be mechanical tasks or data tasks, and can be used to boost productivity and quality by:
◦ being available 24/7
◦ allowing personnel to concentrate on more value-added activities
◦ processing data faster
◦ reducing impact of human errors
- Intelligent Process Automation. Intelligent process automation tools combine robotic process automation with machine learning to imitate human interaction and make advanced decisions.
- Cognitive Computing. Cognitive computing refers to pattern-recognition software and the use of algorithms for machine learning to categorize and analyze spend, cost, and other supplier data.
- Expert systems. Expert systems are a type of AI that use an extensive database of knowledge, usually expressed as a set of rules, in order to solve problems and produce solutions. Expert systems can often provide alternative solutions that a human decision-maker cannot, either due to time or expertise.
Watch the short video below to explore more about how AI and automation are transforming supply chain.
Download video transcript here.