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
by Sheraz Alam Malik; Andrew Fearne; Jesse O'Hanley
International Journal of Value Chain Management (IJVCM), Vol. 10, No. 4, 2019

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.

Online publication date: Wed, 23-Oct-2019

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Value Chain Management (IJVCM):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com