Recently-issued research by Framingham, Mass.-based market research and consulting firm IDC highlighted the firm's top 10 predictions and underlying drivers that the firm expects to have the biggest impact of manufacturers' IT investments in 2022 and future years to come as well. A top-level look at the predictions sees that they address remote operations, supply chain management, product and service innovation, security, data, and application sharing, B2B commerce, low code/no code, and sustainability. Perhaps the most germane prediction, relative to our industry, was prediction number two, which was the following: "By 2023, 50% of All Supply Chain Forecasts Will Be Automated Using Artificial Intelligence, Improving Accuracy by 5 Percentage Points." That one really caught my eye, given everything that the supply chain has been through going back to the onset of the pandemic in March 2020. And while things have been uneven, to be fair, the pandemic really highlighted the need for better supply chain forecasting on myriad fronts, for things like supply chain resiliency, demand planning, inventory management, equipment and labor availability, among many others.

I had a chance to review the manuscript. It is a very good book. For the supply chain managers out there, you should read at least the first few chapters, and then have others on your team read the rest of it and act on it ... you can have close to state-of-the-art forecasts with a minimum of effort.... This book closes the coffin on vendors who are selling only a handful of forecasting models. The objective of Data Science for Supply Chain Forecasting is to show practitioners how to apply the statistical and ML models described in the book in simple and actionable "do-it-yourself" ways by showing, first, how powerful the ML methods are, and second, how to implement them with minimal outside help, beyond the "do-it-yourself" descriptions provided in the book. In an age where analytics and machine learning are taking on larger roles in business forecasting, Nicolas' book is perfect for professionals who want to understand how they can use technology to predict the future more reliably.