This 55th issue of Foresight opens with an article from Phillip Yelland, Zeynep Erkin Baz, and David Serafini of the Data Science/AI team at Target: Forecasting at Scale: The Architecture of a Modern Retail Forecasting System. The challenge of scale at Target is the requirement for nearly one billion forecasts per week, demanding a careful balancing act between statistical modeling, software engineering, and business practice. The team also reports the need to weigh considerations of forecast accuracy against model explainability.
Foresight began reporting on open-source forecasting software back in 2010, featuring an article by Stephan Kolassa and Rob Hyndman: Free Open-Source Forecasting Using R. Since then, the functionality of R has expanded significantly and, as described by Tim Januschowski, Jan Gasthaus, and Yuyang Wang of Amazon Web Services, “the Python programming language has gained immense popularity in recent years,” particularly for machine-learning applications. The authors proceed to evaluate major Open-Source Forecasting Tools in Python while comparing the forecasting sophistication of these tools to those available to users of R.
One of Foresight’s most visionary authors, Niels van Hove, looks ahead to the emergence of Autonomous or “Lights Out” Supply-Chain Planning and identifies the essential requirements for this new technology to take hold.
Niels’ article is followed by a Commentary from Stefan de Kok, who argues that the autonomous supply chain is not going to happen if the separate horizons of operational, tactical, and strategic planning continue to operate in isolation from each other. We not only need to close the loops in planning but be able to rapidly respond to changes through autonomous replanning.
Our book review is of the hot-off-the-press Forecasting: An Essential Introduction, by Jennifer Castle, Michael Clements, and David F. Hendry. Reviewer Mike Gilliland, Foresight’s Editor for Forecasting Practice, observes that while business forecasting and economic forecasting “overlap in many ways, this book exposes some significant, fundamental differences that make economic forecasting an even more vexing challenge.”
Autonomous transportation and autonomous planning are two of the applications of futuristic technology and artificial intelligence. This edition of Foresight concludes with Dan Philps’s article Continual Learning: The Next Generation of Artificial Intelligence, which describes the emergence of autonomous machine learning and highlights how Dan’s team applied CL to investment decisions. The article addresses how automated machine learning might mitigate forecasters’ concerns with the complexity of AI.