Title: 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

Authors: Sheraz Alam Malik; Andrew Fearne; Jesse O'Hanley

Addresses: College of Business, Alfaisal University, P.O. Box 50927, Riyadh 11533, Kingdom of Saudi Arabia ' Norwich Business School, University of East Anglia, Norwich, Norfolk, NR4 7TJ, UK ' Kent Business School, University of Kent, Canterbury, CT2 7FS, UK

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.

Keywords: sales promotions; demand forecasting; Monte Carlo simulation; stock allocation; optimisation; supermarket loyalty card data.

DOI: 10.1504/IJVCM.2019.103271

International Journal of Value Chain Management, 2019 Vol.10 No.4, pp.339 - 357

Received: 26 Mar 2019
Accepted: 16 Apr 2019
Published online: 23 Oct 2019 *

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