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FORESIGHT, Issue 54
$45.00
Description
Summer 2019
Special Feature
Judgmental Model Selection by Fotios Petropoulos
Although judgment plays a significant role in the production and acceptance of forecasts, its performance in model selection has not been tested. In this article, Fotios demonstrates that the application of judgment to the selection of a forecasting model can improve forecast accuracy, and he presents the conditions where this is most likely to be the case.
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Commentaries by Paul Goodwin, Nigel Harvey, Spyros Makridakis and Eric Stellwagen
Articles
- State Space Modeling for Practitioners by Diego Pedregal
State Space methods have been around among economists for some time, though often concealed under a veil of formality that has prevented their use for practitioners. This article offers a more gentle, nontechnical introduction to State Space while going deeply enough to enable use of the SS framework in practice. - Benefits and Challenges of Corporate Prediction Markets by Thomas Wolfram
While prediction markets have become common platforms for political forecasts, they have received limited interest in the business world. Here, Thomas Wolfram discusses recent research on the benefits and challenges of implementing corporate PMs. - Why Is It So Hard to Hold Anyone Accountable for the Sales Forecast? by Chris Gray
Chris Gray probes the critical questions of responsibility and accountability for the forecasts and demand plans. The all-too-common problem of “everyone is responsible, no one is accountable” tends to appear in many multistep processes like demand planning. His article presents an indispensable checklist of issues that every organization must address to clarify individual responsibilities as well as overall accountability for results. - Communicating the Forecast: Providing Decision Makers with Insights by Alec Finney
Forecasts are necessary, but not in themselves sufficient for effective decision making. Here, Alec Finney describes his takeaways from asking decision makers to reveal what’s missing from the numerical outputs they receive from forecasters. Key themes that emerge are the need to agree on assumptions, manage risk, and sell “a story—not a spreadsheet.” - Book Review by Shaun Snapp
Data Science for Supply Chain Forecast by Nicolas Vandeput
For several years now, machine learning (ML) has been one of the most discussed topics in forecasting. It is a foundation of what we now call data science, which is generally taken to mean the tools by which information is analyzed to identify patterns that can improve forecasting and planning. So this new book from Nicolas Vandeput is timely and should generate a good amount of interest.
