Fildes and Goodwin (F&G) observed the subject (the regional subsidiary of a pharmaceutical company) was using a statistical forecasting system, but not fully trusting its output. Forecasters were making overrides to the system generated forecast to make it look like what they believed it should (e.g., following a life-cycle curve
Two weeks ago we looked at the first two steps in effecting forecasting process change: Justify your suspicions with data Communicate your findings That was the easy part. So why is it that so many organization realize they have a forecasting problem, yet are unable to do anything about it?
What if you suspect something is wrong with your forecasting process? What if the process is consuming too much time and too many resources, while still delivering unsatisfactory results (lousy forecasts). What can you do about it? This post looks at the first two steps to effecting meaningful forecasting process
With 2018's M4 Forecasting Competition behind us (although analysis, interpretation, and debate continue), the new M5 Competition starts March 2. Running through June 30, M5 is utilizing actual data provided by Walmart. It will be implemented using Kaggle's Platform, with $100,000 in prize money. Forecasting practitioners are encouraged to participate,
The International Journal of Forecasting has published its 2020-Q1 issue, guest edited by Spyros Makridakis and Fotios Petropoulos, and dedicated entirely to results and commentary on the M4 Forecasting Competition. This issue should be of great interest and value to business forecasting practitioners, and you get online access to it
Following is editor-in-chief Len Tashman's preview of the Winter 2020 issue of Foresight: The International Journal of Applied Forecasting. Preview of Foresight (Winter 2020) This Winter 2020 issue of Foresight—number 56 since the journal began in 2005—formally introduces a new section: Integrated Business Planning (IBP), the meaning of which is evolving
Foresight Editor-in-Chief Len Tashman's Preview of the Fall 2019 Issue 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
The SAS Forecasting R&D team has an open position for a Forecasting and Machine Learning Specialist (apply here). What you’ll do As a Forecasting and Machine Learning Specialist on the SAS Forecasting R&D team, you will help create innovative software to apply cutting-edge statistical methods to automated enterprise-scale business forecasting processes. You will:
Artificial Intelligence for Forecasting Can artificial intelligence augment and amplify our forecasting efforts? Will AI impact our forecasting roles and processes? Does AI deliver the automation and forecast accuracy we've been pursuing? These are the sorts of questions to be addressed by a stellar panel of world-class experts at the
The International Institute of Forecasters and SAS® announce two $10,000 grants to support research on forecasting. Per the announcement: Forecasting research has seen major changes in the theoretical ideas underpinning forecasting effectiveness over the last 30 years. However, there has been less impact on forecasting practice. We aim to put
