Machine learning seems to be everywhere you look, from television commercials and supply chain conferences to university degrees. But despite its buzzword status and adolescent-age maturity, this technology is still shrouded in mystery for many supply chain practitioners. In this hyped-up stage in technology maturity, it’s crucial to be well informed so you can see beyond the headlines and apply the technology correctly to solve business problems and deliver real value.
If you have no background in machine learning, this ebook will teach you the basics. Do you already have some machine learning experience under your belt? We’ve got you covered with some specifics on machine learning in supply chain planning, a few of the top business use cases, and tips for how to get started.
Does all the machine learning and AI speak have you confused? You’re not alone. Here’s a simple primer on the most commonly used terms.
The theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, language translation, and decision support systems.
A branch of AI that is concerned with the question of how to construct computer programs that automatically improve with experience. So instead of being explicitly programmed or told what to do, you feed the program the data and it learns what to do.
Family of machine learning methods based on neural networks architecture, that, due to recent advances in the ability to successfully train multiple layers of networks (“deep” networks), have become the top performing algorithms in specific fields (computer vision, speech recognition, etc.)
IDC predicts spending on AI systems will reach $97.9 billion in 2023, more than two and a half times the $37.5 billion spent in 2019.1
*Worldwide Spending on Artificial Intelligence Systems Will Be Nearly $98 Billion in 2023, According to New IDC Spending Guide