Complementing approaches in ERP effort estimation practice: an industrial study

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    4 Citations (Scopus)

    Abstract

    Projects implementing enterprise resource planning (ERP) solutions are characterized by specific context factors such as high level of reuse, scope of the ERP modules, interdependent functionality, and use of vendor-specific standard implementation method, all of which impose risks known to cause various degrees of project failure. We suggest a remedy to this issue by tackling it from a portfolio management perspective. Our solution rests on earlier work by other authors and is a combination of a classic cost estimation method (COCOMO II), a Monte Carlo simulation process, and a portfolio management model. We report on the results of a case study done in a company site in the telecommunication sector.
    Original languageEnglish
    Title of host publicationProceedings of the 4th ICSE International Workshop on Predictor Models in Software Engineering (PROMISE'08)
    EditorsG. Boetticher, T. Ostrand
    Place of PublicationNew York USA
    PublisherAssociation for Computing Machinery (ACM)
    Pages87-92
    Number of pages6
    ISBN (Print)978-1-60558-036-4
    DOIs
    Publication statusPublished - 10 May 2008
    Event4th ICSE International Workshop on Predictor Models in Software Engineering, PROMISE 2008 - Leipzig, Germany
    Duration: 12 May 200813 May 2008
    Conference number: 4

    Publication series

    Name
    PublisherACM
    NumberWoTUG-31

    Conference

    Conference4th ICSE International Workshop on Predictor Models in Software Engineering, PROMISE 2008
    Abbreviated titleConference on Software Engineering, ICSE
    CountryGermany
    CityLeipzig
    Period12/05/0813/05/08

    Keywords

    • SCS-Services
    • METIS-255010
    • IR-62617
    • EWI-14621

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  • Cite this

    Daneva, M. (2008). Complementing approaches in ERP effort estimation practice: an industrial study. In G. Boetticher, & T. Ostrand (Eds.), Proceedings of the 4th ICSE International Workshop on Predictor Models in Software Engineering (PROMISE'08) (pp. 87-92). New York USA: Association for Computing Machinery (ACM). https://doi.org/10.1145/1370788.1370808