Modelling hybrid demand (e-commerce “+” traditional) evolution: A scenario planning approach
Luca Canetta,
Naoufel Cheikhrouhou and
Rémy Glardon
International Journal of Production Economics, 2013, vol. 143, issue 1, 95-108
Abstract:
This work presents a “3 steps–2 aggregation levels” methodological approach for modelling the evolution of the hybrid demand order flow pattern over long time horizons. The hybrid demand is the result of the mutual interaction between the e-commerce and the traditional demand. Thus, its modelling requires a complex analysis at various aggregation levels. More specifically, the quantitative modelling and prediction of e-commerce demand is achieved, both at the market (e-sales turnover) and at the operational (order flow) levels. Then, the demand stemming from the traditional sales channels, modified by the e-commerce introduction is specified and formalised. The concurrent presence of dynamic processes and several uncertainty sources as well as the necessity to assess the impact of e-commerce induced demand modifications over a long time horizon justify the development of a demand scenario planning approach.
Keywords: Scenario planning; E-commerce; Cannibalisation; Diffusion of innovation; Delphi study (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0925527310001982
Full text for ScienceDirect subscribers only
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:eee:proeco:v:143:y:2013:i:1:p:95-108
DOI: 10.1016/j.ijpe.2010.06.003
Access Statistics for this article
International Journal of Production Economics is currently edited by Stefan Minner
More articles in International Journal of Production Economics from Elsevier
Bibliographic data for series maintained by Catherine Liu (repec@elsevier.com).