A standardised model to quantify the financial impact of poor engineering information quality in the oil and gas industry

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dc.contributor.authorCoetzer, Emile Ottoen_ZA
dc.contributor.authorVlok, P. J.en_ZA
dc.date.accessioned2020-01-28T07:44:14Z
dc.date.available2020-01-28T07:44:14Z
dc.date.issued2019
dc.descriptionCITATION: Coetzer, E. O. & Vlok, P. J. 2019. A standardised model to quantify the financial impact of poor engineering information quality in the oil and gas industry. South African Journal of Industrial Engineering, 30(4):131-142, doi:10.7166/30-4-2080.
dc.descriptionThe original publication is available at http://sajie.journals.ac.za
dc.description.abstractENGLISH ABSTRACT: Industry needs quality data, but digital formats increase the risk of lost data quality, implying huge risk. The benefits of data quality are difficult to calculate in order to justify the expense. A survey was developed and validated at an operating asset as a precedent. The elements are productivity and production loss, and increased cost and risk. A Monte Carlo method was field tested. The results were presented in graphical and Pareto form to facilitate funding and prioritisation. The results prove that the cost is significant. As a first exploration of the subject, opportunities exist for more sophisticated models, and for investigating causality.en_ZA
dc.description.abstractAFRIKAANSE OPSOMMING: Die energie-industrie benodig hoë gehalte data, maar digitale formate verhoog die moontlikheid van gehalteverlies, wat groot risiko tot gevolg mag hê. Die voordele van data gehalte is moeilik berekenbaar en dus so ook die regverdiging van uitgawes verbonde aan verbeteringsprojekte. ʼn Meningspeiling is ontwikkel en gevalideer by ʼn aanleg in bedryf om sodoende ʼn presedent te skep. Die elemente is verlies aan produksie en produktiwiteit en verhoogde koste en risiko. ʼn Monte-Carlo simulasiemodel is gebou en getoets. Die resultate is grafies en in Pareto-formaat aangebied om befondsing en prioritisering te vergemaklik. Die resultate bewys dat die kostes noemenswaardig is. Geleenthede bestaan vir meer gesofistikeerde modelle en ʼn ondersoek na die oorsake moet van stapel gestuur worldaf_ZA
dc.description.urihttp://sajie.journals.ac.za/pub/article/view/2080
dc.description.versionPublisher's version
dc.format.extent12 pagesen_ ZA
dc.identifier.citationCoetzer, E. O. & Vlok, P. J. 2019. A standardised model to quantify the financial impact of poor engineering information quality in the oil and gas industry. South African Journal of Industrial Engineering, 30(4):131-142, doi:10.7166/30-4-2080
dc.identifier.issn2224-7890 (online)
dc.identifier.issn1012-277X (print)
dc.identifier.otherdoi:10.7166/30-4-2080
dc.identifier.urihttp://hdl.handle.net/10019.1/107384
dc.language.isoen_ZAen_ZA
dc.publisherSouthern African Institute for Industrial Engineeringen_ ZA
dc.rights.holderAuthors retain copyrighten_ZA
dc.subjectData Qualityen_ZA
dc.subjectQuality assurance — Managementen_ ZA
dc.subjectQuality assurance — Standardsen_ ZA
dc.subjectOil and Gas industryen_ZA
dc.subjectEngineering — Standardsen_ZA
dc.subjectEconomic impact analysisen_ZA
dc.titleA standardised model to quantify the financial impact of poor engineering information quality in the oil and gas industryen_ZA
dc.typeArticleen_ZA
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