We know the web and most of the materials available about AI and machine learning are created for techies to learn the secrets of good coding or for marketing purposes to hype the story. We are targeting our papers and articles towards supply chainers who need to learn more about AI and where to apply it.
In the future, AI technologies will be within the supply-chain applications and analytics we use every day, such as planning, demand management, logistics, supplier management, and so on, which will just keep getting better. Today, however, we have a lot to learn and many decisions to make.
The field of AI/machine learning comes with a bizarre amount of intimidating terminology and, of course, emphasis on data — big data, structured data, web data, unstructured data, social, news, events and weather. As supply chain professionals, do we have to learn and master all of that before we use a solution? Are there solutions that actually fit our problems or do we have to hire a data scientist or two to develop them?
Absorbing and investing in all the new technology, new staff, data subscriptions, training, and so on seem like major impediments to implementing AI/machine learning — expensive and resource-draining. And in dealing with new technology, we want to avoid making costly mistakes along the way.
In fact, there are a lot of myths about what AI/machine learning can do. Getting a handle on that is key to learning about what results can be expected from AI at this juncture. Yes, AI/machine learning is somewhat new for supply chain, so we need to get the facts.
AI for Supply Chain — Debunking the Myths, a new report, is targeted directly at these issues. What are the myths and realities of AI/machine learning today? We talked to developers about the hype in the marketplace. And we talked to users about the figments and folklore, and the challenges they had in understanding AI, avoiding mistakes in application, and in explaining what AI is — and is not — to their peers.
In this report, therefore, we address these major topics of confusion such as: leveraging algorithms/custom development vs. purpose-built solutions; hiring or not hiring data scientists; the impact of AI on the organization and the role of the supply chain planner; and other topics. We also touch on why we might need AI/machine learning, so we can begin to solve some of the intractable supply chain issues we haven’t really solved with traditional systems.
For other topics in AI/machine learning:
AI in Supply Chain — Some Definitions — an industry collaboration on definitions of AI/machine learning terminology
It’s All About the Data — understanding all the types of data and how to manage it
The Data Scientist, Software Engineers, and Data Managers — what are the roles and responsibilities required in a modern supply chain and IT organization in the world of AI and vast data?
To view other articles from this issue of the brief, click here.