A standardised model to quantify the financial impact of poor engineering information quality in the oil and gas industry
Date
2019
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Southern African Institute for Industrial Engineering
Abstract
ENGLISH 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.
AFRIKAANSE 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 world
AFRIKAANSE 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 world
Description
CITATION: 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.
The original publication is available at http://sajie.journals.ac.za
The original publication is available at http://sajie.journals.ac.za
Keywords
Data Quality, Quality assurance — Management, Quality assurance — Standards, Oil and Gas industry, Engineering — Standards, Economic impact analysis
Citation
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