Improving organizational decision support: Detection of outliers and sales prediction for a pharmaceutical distribution company

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Détails sur la publication

Liste des auteurs: Ribeiro A, Seruca I, Durao N
Editeur: Elsevier: Creative Commons Attribution Non-Commercial No-Derivatives License
Année de publication: 2017
Numéro du volume: 121
Page d'accueil: 282
Dernière page: 290
Nombre de pages: 9
ISSN: 1877-0509
Languages: Anglais-Royaume-Uni (EN-GB)


Résumé

Stock unavailability in the supply of medicines to pharmacies can be caused by several factors including manufacturing problems, lack of raw materials, end of product selling, disease and epidemics outbreaks. Furthermore, the sale of medicines by some pharmacies to foreign markets has increased in recent years, and is considered one of the main causes of medicine supply failures in Portugal. This paper depicts the case study of a pharmaceutical distribution company in Portugal and aims to address two main research issues. The first one consisted in detecting customers (pharmacies) and products (medicines) which may be considered outliers and perform stock proration when these outliers are detected, in order to avoid abnormal sales and out-of stocks in pharmacies. The second one targeted the sales prediction for the pharmaceutical distribution company, in order to better control and manage the levels of stock of medicines, so as to avoid excessive inventory costs while guaranteeing customer demand satisfaction, and thus decreasing the possibility of loss of customers due to stock outages. In outliers detection (customers and products) we used the Box-plot method as well as the SPSS statistical software. For sales prediction, the time series data mining method smoothed Pegels was used, while the implementation was done in SQL and the analyzed data was stored in an Oracle database. (C) 2017 The Authors. Published by Elsevier B.V.


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Dernière mise à jour le 2019-23-08 à 11:15