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The use of disaggregated demand information to improve forecasts and stock allocation during sales promotions: a simulation and optimisation study using supermarket loyalty card data

Malik, S.A., Fearne, A., O'Hanley, J.R. (2019) The use of disaggregated demand information to improve forecasts and stock allocation during sales promotions: a simulation and optimisation study using supermarket loyalty card data. International Journal of Value Chain Management, 10 (4). pp. 339-357. ISSN 1741-5357. (doi:10.1504/IJVCM.2019.103271) (KAR id:74612)

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Official URL:
http://dx.doi.org/10.1504/IJVCM.2019.103271

Abstract

Our work highlights the importance of using disaggregated demand information at store level to improve sales forecasts and stock allocation during sales promotions. Monte Carlo simulation and optimisation modelling were used to estimate short-term promotional impacts. Supermarket loyalty card data was used from a major UK retailer to identify the benefits of using disaggregated demand data for improved forecasting and stock allocation. The results suggest that there is a high degree of heterogeneity in demand at individual store level due to number of factors including the weather, the characteristics of shoppers, the characteristics of products and store format, all of which conspire to generate significant variation in promotional uplifts. The paper is the first to use supermarket loyalty card data to generate store level promotional forecasts and quantify the benefits of disaggregating the allocation of promotional stock to the level of individual stores rather than regional distribution centres.

Item Type: Article
DOI/Identification number: 10.1504/IJVCM.2019.103271
Uncontrolled keywords: sales promotions; demand forecasting; Monte Carlo simulation; stock allocation; optimisation; supermarket loyalty card data
Divisions: Divisions > Kent Business School - Division > Department of Analytics, Operations and Systems
Depositing User: Jesse O'Hanley
Date Deposited: 27 Jun 2019 09:44 UTC
Last Modified: 07 Oct 2021 14:29 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/74612 (The current URI for this page, for reference purposes)
O'Hanley, J.R.: https://orcid.org/0000-0003-3522-8585
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