Revenue Assurance Model for Project Management Organizations Using Outlier Mining View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2018-10-02

AUTHORS

Gilberto F. Castro , Anié Bermudez-Peña , Francisco G. Palacios , Mitchell Vásquez-Bermúdez , Diana J. Espinoza , Fausto R. Orozco , Inelda A. Martillo

ABSTRACT

The increase of competitiveness in global markets has led to the need for improvements in project management organizations aimed at stimulating financial health and revenue. Revenue assurance combines statistical techniques, scope, time and risk management with the goal of reducing costs and maximizing revenue in organizations that apply it. The objective of this paper is to present a revenue assurance model for project management organizations that allows the detection of planning errors and revenues maximization during the projects development. As part of the research novelty, the proposed model combines risk management, outlier mining and soft computing techniques. Risk management is developed with a proactive approach, based on the application of computing with words for qualitative risk assessment. In the research, cross validation tests are carried out comparing different techniques for the detection of anomalous situations. In the comparison, project management databases are used for the development of computer solutions. The model is introduced in a platform for integrated project management and the results of its application are also presented. More... »

PAGES

124-139

References to SciGraph publications

  • 2002. Outlier Detection Using Replicator Neural Networks in DATA WAREHOUSING AND KNOWLEDGE DISCOVERY
  • 2013. Norm Aggregations and OWA Operators in AGGREGATION FUNCTIONS IN THEORY AND IN PRACTISE
  • 2016. Platform for Project Evaluation Based on Soft-Computing Techniques in TECHNOLOGIES AND INNOVATION
  • Book

    TITLE

    Technologies and Innovation

    ISBN

    978-3-030-00939-7
    978-3-030-00940-3

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-030-00940-3_10

    DOI

    http://dx.doi.org/10.1007/978-3-030-00940-3_10

    DIMENSIONS

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