Artificial intelligence (AI) is the name commonly given to the ability of machines to mimic the human aptitude to reason, solve problems, and learn from experience. Research in this field over the past few decades has spanned many disciplines, including the mathematical, social, and biological sciences.

In his four-part article for Foresight beginning in this issue, Spyros Makridakis

• looks at the challenges of forecasting AI progress and presents what he sees as the forthcoming advances in the field (Part 1);
• examines four major scenarios for the impacts of AI and the actions needed to avoid the potentially negative consequences of these technologies (Part 2);
• explores how AI will affect the competitive landscape to which our business models will have to adapt (Part 3); and
• goes beyond AI to cover intelligence augmentation (IA) and the forthcoming revolution in blockchain technologies (BT), whose implications he believes may be greater than those from the Internet (Part 4).

Part 1, featured in this issue, is entitled Forecasting the Impact of Artificial Intelligence and includes an introduction from Owen Davies of TechCast Global, which employs the collective intelligence of global experts to forecast technology breakthroughs.

Paul Goodwin’s latest Hot New Research column is Forecasting After a Fashion and addresses the challenges of forecasting products with very short life cycles plus the whimsy of rapidly changing tastes and priorities.

Pharmaceutical companies face most of the challenges of new-product forecasting and have traditionally relied on judgmental approaches such as historical analogues. One advantage to forecasters that this industry has over many others is the availability of good data: on patients, prescriptions, and medication use over time. Christian Schäfer and Stephan Brebeck present a promising data-driven approach to Predicting the Uptake Curves of New Drugs—an approach that, among other features, can improve a pharmaceutical firm’s decisions on product promotion.

Forecasting in industry is highly collaborative, both across companies and within the firm. John Mello discusses the major form of external collaboration in his article Principles, Benefits, and Pitfalls of Vendor-Managed Inventory.

On the internal side, Chris Gray and John Dougherty present Part 2 of their examination of Misconceptions, Missteps, and Bad Practices in S&OP. Here they take on mispractices related to metrics, time fences, freezing of forecasts, documentation of assumptions, and the linkage of planning and operations to company strategy.

In response to Gray and Dougherty’s argument over the role of software in S&OP in Part 1 of their article, Bill Tonetti attempts to reconcile different viewpoints in his commentary Do Companies Really Need Software for S&OP?

For every expert that says you don’t need S&OP software, others will say that you do. There is little consensus on this subject, and I have come to believe that there are two primary reasons why experts are polarized on the subject.

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