Algorithms for Analyzing and Forecasting in a Pharmaceutical Company

Lucelia Dedu ()

Informatica Economica, 2014, vol. 18, issue 3, 103-112

Abstract: This paper presents some of the utilities of using SAS code for analyze and forecast, in a pharmaceutical company. First, data must be cleaned, in order to obtain representative results. Once this stage done, the input can be evaluated and models can be created in SAS, to extract the most valuable information from the initial data. To sustain management decisions, SAS code allows creating different types of reports and has the capability to recode the initial variables into new ones, but keeping the most part of the information contained, through the technique called “principal components analysis†. Another advantage of SAS code is its capability of classifying the initial variables into compact class. Based on scorecards and WOE (Weight of Evidence), notions used in banking analyses, it has been created a model which evaluates the suppliers of the company and ranks them, in order to find out what contracts must be continued and which contracts must be closed.

Keywords: Reports; Principal Components Analysis; Clusters; Scorecard; Weight of Evidence (search for similar items in EconPapers)
Date: 2014
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://revistaie.ase.ro/content/71/09%20-%20Dedu.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:aes:infoec:v:18:y:2014:i:3:p:103-112

Access Statistics for this article

Informatica Economica is currently edited by Ion Ivan

More articles in Informatica Economica from Academy of Economic Studies - Bucharest, Romania Contact information at EDIRC.
Bibliographic data for series maintained by Paul Pocatilu ().