Demand Forecasting of Individual Probability Density Functions with Machine Learning

Felix Wick (), Ulrich Kerzel (), Martin Hahn (), Moritz Wolf (), Trapti Singhal (), Daniel Stemmer (), Jakob Ernst () and Michael Feindt ()
Additional contact information
Felix Wick: Blue Yonder GmbH
Ulrich Kerzel: IU Internationale Hochschule
Martin Hahn: Blue Yonder GmbH
Moritz Wolf: Blue Yonder GmbH
Trapti Singhal: Blue Yonder India Private Limited
Daniel Stemmer: Blue Yonder GmbH
Jakob Ernst: Blue Yonder GmbH
Michael Feindt: Blue Yonder GmbH

SN Operations Research Forum, 2021, vol. 2, issue 3, 1-39

Abstract: Abstract Introduction Demand forecasting is a central component of the replenishment process for retailers, as it provides crucial input for subsequent decision making like ordering processes. In contrast to point estimates, such as the conditional mean of the underlying probability distribution, or confidence intervals, forecasting complete probability density functions allows to investigate the impact on operational metrics, which are important to define the business strategy, over the full range of the expected demand. Evaluation of predicted probability distributions Whereas metrics evaluating point estimates are widely used, methods for assessing the accuracy of predicted distributions are rare, and this work proposes new techniques for both qualitative and quantitative evaluation methods. Explainable predictions Using the supervised machine learning method “Cyclic Boosting”, complete individual probability density functions can be predicted such that each prediction is fully explainable. This is of particular importance for practitioners, as it allows to avoid “black-box” models and understand the contributing factors for each individual prediction. Temporal confounding Another crucial aspect in terms of both explainability and generalizability of demand forecasting methods is the limitation of the influence of temporal confounding, which is prevalent in most state-of-the-art approaches.

Keywords: Explainable machine learning; Retail demand forecasting; Probability distribution; Temporal confounding (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s43069-021-00079-8 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:snopef:v:2:y:2021:i:3:d:10.1007_s43069-021-00079-8

Ordering information: This journal article can be ordered from
https://www.springer.com/journal/43069

DOI: 10.1007/s43069-021-00079-8

Access Statistics for this article

SN Operations Research Forum is currently edited by Marco Lübbecke

More articles in SN Operations Research Forum from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().