A Proposed Hybrid Data Modelling Framework to Enhance Decision Making in Pharmaceutical Sector

EasyChair Preprint 2414

Abstract

Pharmaceutical companies suffered from loss because of wrong decisions. At this point, data warehouses have been introduced as a solution for enhancing decision for those companies. Most recent trends of Business Intelligence (BI) techniques have taken attention of both communities and research in pharmaceutical sector. This paper aimed to propose a framework to enhance decisions of pharmaceutical distribution department by focusing on the sales department in Egyptian Company for Medicine Trade. High level managers often give critical decisions affects the future of the company. Large amounts of data need to be reached and modified to enhance these decisions. Creating a hybrid data modelling to help managers in decision support by analyzing data to make queriers, reports using online analyzing processing (OLAP) based on multidimensional data model and predicting using  artificial neural network (ANN) modelling . This leads to overcoming many problems facing them such as increasing the cost of inventory and expired medicines. 

Keyphrases: ANN, Business Intelligence, Data Mart, Data Mining, Data Warehouse, OLAP, Pharmaceutical Companies

@booklet{EasyChair:2414,
  author    = {Noura Mahmoud Abd Elazeem and Nevine Makram Labib and Aliaa Kamal Abdella},
  title     = {A Proposed Hybrid Data Modelling Framework to Enhance Decision Making in Pharmaceutical Sector},
  howpublished = {EasyChair Preprint 2414},
  year      = {EasyChair, 2020}}