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Expertise, credibility of system forecasts and integration methods in judgmental demand forecasting
Alvarado-Valencia, J. Barrero, L.H. Onkal, Dilek Dennerlein, J.T.
Alvarado-Valencia, J.
Barrero, L.H.
Onkal, Dilek
Dennerlein, J.T.
Publication Date
2017-01
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© 2016 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved. Reproduced in accordance with the publisher's self-archiving policy. This manuscript version is made available under the CC-BY-NC-ND 4.0 license.
Peer-Reviewed
Yes
Open Access status
openAccess
Accepted for publication
22/12/2015
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Abstract
Expert knowledge elicitation lies at the core of judgmental forecasting—a domain that relies fully on the power of such knowledge and its integration into forecasting. Using experts in a demand forecasting framework, this work aims to compare the accuracy improvements and forecasting performances of three judgmental integration methods. To do this, a field study was conducted with 31 experts from four companies. The methods compared were the judgmental adjustment, the 50–50 combination, and the divide-and-conquer. Forecaster expertise, the credibility of system forecasts and the need to rectify system forecasts were also assessed, and mechanisms for performing this assessment were considered. When (a) a forecaster’s relative expertise was high, (b) the relative credibility of the system forecasts was low, and (c) the system forecasts had a strong need of correction, judgmental adjustment improved the accuracy relative to both the other integration methods and the system forecasts. Experts with higher levels of expertise showed higher adjustment frequencies. Our results suggest that judgmental adjustment promises to be valuable in the long term if adequate conditions of forecaster expertise and the credibility of system forecasts are met.
Version
Accepted manuscript
Citation
Alvarado-Valencia J, Barrero LH, Onkal D et al (2017) Expertise, credibility of system forecasts and integration methods in judgmental demand forecasting. International Journal of Forecasting. 33(1): 298-313.
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Article