If you want to use AI in supply chain management and want to use AI to automate tasks and provide actionable insights into risks and possible exceptions, the most effective way to achieve this, is to look at the conjuction of Artificial Intelligence (AI) and Machine Learning (ML).
However, what is AI and what is ML?
AI and ML are often used together and in some cases, the terms are used interchangeably. However, what is actually the difference between AI and ML?
Artificial Intelligence (AI) has as many definitions as it has implementations.
The most inclusive definition is the one of leading AI textbooks, where AI considered a synonym of Intelligent Agents. On the side of the spectrum you’d have the so called “The AI Effect”, where AI is anything that has not been done yet.
We consider AI as any application that achieves its goals by exhibiting intelligence that was built upon general-purpose tools, which the application combined in an order that it saw fit.
In other words; AI achieves its goals in a way that was not spelled out on forehand, like you’d have with traditional computer programs.
The definition of Machine Learning is a lot simpler: The study of computer algorithms that improve automatically through experience.
This means that ML does not always also have to be AI. There is a huge field of “traditional” ML, containing many tools that have been used for years by statisticians and data scientists. Many of these tools are still extensively used today, like regression, and K-Nearest Neighbours.
Where AI and ML meet
The most interesting recent progress in AI and ML can be found in their conjunction. This combination of AI and ML profits from the flexibility of AI, combined with the power of ML to learn from past results, based on the vast amounts of data that are available today.
Algorithms like Neural Networks / Deep Learning and Evolutionary Algorithms show us that, if applied correctly, a human can easily be outperformed by a generically applicable algorithm when solving complex problems.