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Practice paper

How analytics is used in forecasting

Barry Keating
Applied Marketing Analytics: The Peer-Reviewed Journal, 7 (1), 47-57 (2021)
https://doi.org/10.69554/NTXA4233

Abstract

Over the last decade, the science of forecasting has adopted the tools of the data scientist. Prediction today combines traditional demand planning models with the standard tools of machine learning. The result is much improved accuracy over the short term and an enhanced ability to account for the effects of major changes in the economic environment. On the flipside, researchers must now sort through much greater volumes of data in order to identify what might be useful to produce accurate forecasts. The application of machine learning solves what could be a major stumbling block here. So-called ‘data consolidators’ are now emerging to support forecasters by providing access to previously unknown data as well as the tools for using such data creatively. This paper will demonstrate how data from data consolidators may be used by analytics algorithms to improve the accuracy of forecasts.

Keywords: analytics; forecasting; classification; prediction; supply chain; demand planning

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Author's Biography

Barry Keating is Professor Emeritus of Finance at the University of Notre Dame. He has done extensive work in the areas of business forecasting, analytics and regulation. His forecasting and predictive analytics textbook is widely used in colleges and universities. He works as a consultant for both forecasting and analytics software producers and multinational firms with forecasting problems.

Citation

Keating, Barry (2021, June 1). How analytics is used in forecasting. In the Applied Marketing Analytics: The Peer-Reviewed Journal, Volume 7, Issue 1. https://doi.org/10.69554/NTXA4233.

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cover image, Applied Marketing Analytics: The Peer-Reviewed Journal
Applied Marketing Analytics: The Peer-Reviewed Journal
Volume 7 / Issue 1
© Henry Stewart
Publications LLP

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