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

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Malik, Sheraz Alam, Fearne, Andrew and O Hanley, Jesse (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

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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
Uncontrolled Keywords: demand forecasting,monte carlo simulation,optimisation,sales promotions,stock allocation,supermarket loyalty card data,information systems,computer science applications,strategy and management ,/dk/atira/pure/subjectarea/asjc/1700/1710
Faculty \ School: Faculty of Social Sciences > Norwich Business School
UEA Research Groups: Faculty of Social Sciences > Research Groups > Innovation, Technology and Operations Management
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Depositing User: LivePure Connector
Date Deposited: 10 Jan 2020 04:24
Last Modified: 07 Oct 2023 01:02
URI: https://ueaeprints.uea.ac.uk/id/eprint/73578
DOI: 10.1504/IJVCM.2019.103271

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