Evaluation of data-driven decision-making implementation in the mining industry

dc.contributor.authorBisschoff, Rudolph
dc.contributor.authorGrobbelaar, Schalk
dc.contributor.emailschalk.grobbelaar@up.ac.zaen_US
dc.date.accessioned2023-02-22T07:56:47Z
dc.date.available2023-02-22T07:56:47Z
dc.date.issued2022-11
dc.descriptionPresented at the 33rd annual conference of the Southern African Institute for Industrial Engineering, held from 3 to 5 October 2022 in Zimbali, South Africa.en_US
dc.description.abstractThe ability of organisations to collect and store vast amounts of data has become increasingly more accessible and affordable in recent decades thanks to the advancement of Industry 4.0. This ability is an enabler of data-driven decision-making (DDDM). However, converting data into knowledge that can inform decision-makers has proven challenging for many companies. The ability to perform DDDM effectively depends on a combination of capabilities that encompass the technological, analytical, and managerial aspects of a business. This research focuses on the mining industry, and used a scoping literature review to identify the different DDDM tools that are currently available, the potential benefits of DDDM, the key enablers of DDDM, and the lessons learnt from previous implementations. The objective of the paper is to assist mining industry organisations in developing a DDDM implementation framework.en_US
dc.description.abstractDie vermoë van organisasies om groot hoeveelhede data in te samel en te berg het in die afgelope dekades toenemend meer toeganklik en bekostigbaar geword danksy die vooruitgang van Industrie 4.0. Hierdie vermoë is 'n bemiddelaar vir data-gedrewe besluitneming (DGB). Dit was egter uitdagend vir baie maatskappye om data in kennis om te skakel vir besluitnemers. Die vermoë om DGB effektief uit te voer hang af van 'n kombinasie van vermoëns wat die tegnologiese, analitiese en bestuursaspekte van 'n onderneming insluit. Hierdie navorsing het op die mynbedryf gefokus en het 'n literatuuroorsig gebruik om die verskillende DGB-instrumente wat tans beskikbaar is, die potensiële voordele van DGB, die sleutel-instaatstellers van DGB, en die lesse geleer uit vorige implementerings te identifiseer. Die doel van die artikel is om mynbedryforganisasies te help met die ontwikkeling van 'n DGBimplementeringsraamwerk.en_US
dc.description.departmentGraduate School of Technology Management (GSTM)en_US
dc.description.librarianam2023en_US
dc.description.urihttp://sajie.journals.ac.zaen_US
dc.identifier.citationBisschoff, R.A.D.P. & Grobbelaar, S. 2022, 'Evaluation of data-driven decision-making implementation in the mining industry', South African Journal of Industrial Engineering, vol. 33, no. 3, pp. 218-232, doi : 10.7166/33-3-2799.en_US
dc.identifier.issn1012-277X (print)
dc.identifier.issn2224-7890 (online)
dc.identifier.other10.7166/33-3-2799
dc.identifier.urihttps://repository.up.ac.za/handle/2263/89749
dc.language.isoenen_US
dc.publisherSouthern African Institute for Industrial Engineeringen_US
dc.rights© 2022 Rudolph Bisschoff, Schalk Grobbelaar. This article is licensed under a Creative Commons Attribution License.en_US
dc.subjectAbilityen_US
dc.subjectOrganisationsen_US
dc.subjectData-driven decision-making (DDDM)en_US
dc.subjectVermoeen_US
dc.subjectOrganisasiesen_US
dc.subjectData-gedrewe besluitneming (DGB)en_US
dc.titleEvaluation of data-driven decision-making implementation in the mining industryen_US
dc.typeArticleen_US

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