Home > Research > Publications & Outputs > Modelling consumer directed substitution
View graph of relations

Modelling consumer directed substitution

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Published

Standard

Modelling consumer directed substitution. / Vaagen, Hajnalka; Wallace, Stein W; Kaut, Michal.
In: International Journal of Production Economics, Vol. 134, No. 2, 12.2011, p. 388-397.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Vaagen, H, Wallace, SW & Kaut, M 2011, 'Modelling consumer directed substitution', International Journal of Production Economics, vol. 134, no. 2, pp. 388-397. https://doi.org/10.1016/j.ijpe.2009.11.012

APA

Vaagen, H., Wallace, S. W., & Kaut, M. (2011). Modelling consumer directed substitution. International Journal of Production Economics, 134(2), 388-397. https://doi.org/10.1016/j.ijpe.2009.11.012

Vancouver

Vaagen H, Wallace SW, Kaut M. Modelling consumer directed substitution. International Journal of Production Economics. 2011 Dec;134(2):388-397. doi: 10.1016/j.ijpe.2009.11.012

Author

Vaagen, Hajnalka ; Wallace, Stein W ; Kaut, Michal. / Modelling consumer directed substitution. In: International Journal of Production Economics. 2011 ; Vol. 134, No. 2. pp. 388-397.

Bibtex

@article{093275dd26e0409d8acf1983eeb248d9,
title = "Modelling consumer directed substitution",
abstract = "We discuss the challenges and difficulties arising when approaching and modelling the consumer-directed substitution problem in quick response supply chains. Further, we propose heuristic solutions suited for large problems with complex uncertainty and dependency patterns. Despite the single-period newsvendor model we use, our substitution process is an approximation of the dynamic product choice. To ensure consistency with regard to the information used to establish substitution fractions and information available at the time of optimisation, substitution fraction estimation and inventory/assortment optimisation are discussed simultaneously. The decision-independent substitution preferences applied here do not require inventory or sales transaction data, but reflect understanding on the demand driver attributes. This approach, in turn, leads to increased robustness in assortment planning. Factual substitution is an outcome of the optimisation process, constrained by the available substitutes and unfulfilled demand.Despite being unable to fully describe the dependencies among the substitute choice possibilities, our substitution approach, together with the modelling process, allows handling the most important dependencies, such as negatively correlated substitute choice possibilities and positively/negatively correlated first and second choice possibilities.",
keywords = "Assortment planning , Substitution estimation , Multi-item newsvendor , Stochastic programming , Simulation , Correlations",
author = "Hajnalka Vaagen and Wallace, {Stein W} and Michal Kaut",
year = "2011",
month = dec,
doi = "10.1016/j.ijpe.2009.11.012",
language = "English",
volume = "134",
pages = "388--397",
journal = "International Journal of Production Economics",
issn = "0925-5273",
publisher = "Elsevier Science B.V.",
number = "2",

}

RIS

TY - JOUR

T1 - Modelling consumer directed substitution

AU - Vaagen, Hajnalka

AU - Wallace, Stein W

AU - Kaut, Michal

PY - 2011/12

Y1 - 2011/12

N2 - We discuss the challenges and difficulties arising when approaching and modelling the consumer-directed substitution problem in quick response supply chains. Further, we propose heuristic solutions suited for large problems with complex uncertainty and dependency patterns. Despite the single-period newsvendor model we use, our substitution process is an approximation of the dynamic product choice. To ensure consistency with regard to the information used to establish substitution fractions and information available at the time of optimisation, substitution fraction estimation and inventory/assortment optimisation are discussed simultaneously. The decision-independent substitution preferences applied here do not require inventory or sales transaction data, but reflect understanding on the demand driver attributes. This approach, in turn, leads to increased robustness in assortment planning. Factual substitution is an outcome of the optimisation process, constrained by the available substitutes and unfulfilled demand.Despite being unable to fully describe the dependencies among the substitute choice possibilities, our substitution approach, together with the modelling process, allows handling the most important dependencies, such as negatively correlated substitute choice possibilities and positively/negatively correlated first and second choice possibilities.

AB - We discuss the challenges and difficulties arising when approaching and modelling the consumer-directed substitution problem in quick response supply chains. Further, we propose heuristic solutions suited for large problems with complex uncertainty and dependency patterns. Despite the single-period newsvendor model we use, our substitution process is an approximation of the dynamic product choice. To ensure consistency with regard to the information used to establish substitution fractions and information available at the time of optimisation, substitution fraction estimation and inventory/assortment optimisation are discussed simultaneously. The decision-independent substitution preferences applied here do not require inventory or sales transaction data, but reflect understanding on the demand driver attributes. This approach, in turn, leads to increased robustness in assortment planning. Factual substitution is an outcome of the optimisation process, constrained by the available substitutes and unfulfilled demand.Despite being unable to fully describe the dependencies among the substitute choice possibilities, our substitution approach, together with the modelling process, allows handling the most important dependencies, such as negatively correlated substitute choice possibilities and positively/negatively correlated first and second choice possibilities.

KW - Assortment planning

KW - Substitution estimation

KW - Multi-item newsvendor

KW - Stochastic programming

KW - Simulation

KW - Correlations

U2 - 10.1016/j.ijpe.2009.11.012

DO - 10.1016/j.ijpe.2009.11.012

M3 - Journal article

VL - 134

SP - 388

EP - 397

JO - International Journal of Production Economics

JF - International Journal of Production Economics

SN - 0925-5273

IS - 2

ER -