Show simple item record

dc.contributor.authorVerstraete, Guylian
dc.contributor.authorAghezzaf, El-Houssaine
dc.contributor.authorDesmet, Bram
dc.date.accessioned2019-04-29T13:07:35Z
dc.date.available2019-04-29T13:07:35Z
dc.date.issued2019en_US
dc.identifier.issn0969-6989
dc.identifier.doi10.1016/j.jretconser.2019.02.019
dc.identifier.urihttp://hdl.handle.net/20.500.12127/6302
dc.description.abstractAccurate demand forecasting is of critical importance to retail companies operating in high-volume low-margin industries. Inaccuracies in the forecasts lead either to stock-outs or to excess inventories, resulting in either lost sales or higher working capital, and for both cases in extra unnecessary costs. Prediction accuracy is essential to retail companies having a part of their product portfolio manufactured in low-cost countries and requiring long delivery times. It is rather vital when the demand for these goods is strongly weather dependent. The combination of long delivery times and weather dependence creates a business challenge, as the availability period of accurate weather information is much shorter than the lead time. In this paper we propose a methodology that handles the impact of both the short-term (with available weather data) and the long-term weather uncertainty on the forecast. For the former, the proposed framework is capable of automatically selecting the best prediction model. For latter, the framework fits a distribution on simulated and aggregated sales using the short-term regression model with historical weather data. This framework has been tested on a company's sales data and is proven to satisfactorily address the challenges that the company is facing.en_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.subjectSales Forecastingen_US
dc.subjectMachine Learningen_US
dc.subjectWeather Forecastingen_US
dc.titleA data-driven framework for predicting weather impact on high-volume low-margin retail productsen_US
dc.identifier.journalJournal of Retailing and Consumer Servicesen_US
dc.source.volume48en_US
dc.source.issueMarchen_US
dc.source.beginpage169en_US
dc.source.endpage177en_US
dc.contributor.departmentGhent Universityen_US
vlerick.knowledgedomainOperations & Supply Chain Managementen_US
vlerick.typearticleJournal article with impact factor
vlerick.vlerickdepartmentTOMen_US
dc.identifier.vperid64585en_US


Files in this item

Thumbnail
Name:
Publisher version

This item appears in the following Collection(s)

Show simple item record