Show simple item record

dc.contributor.advisorBrand, H.G.
dc.contributor.authorBotes, Lee-Ann Alexis
dc.date.accessioned2021-11-30T07:46:57Z
dc.date.available2021-11-30T07:46:57Z
dc.date.issued2021
dc.identifier.urihttps://orcid.org/0000-0003-3194-7224
dc.identifier.urihttp://hdl.handle.net/10394/38060
dc.descriptionPhD (Development and Management Engineering), North-West University, Potchefstroom Campusen_US
dc.description.abstractThe South African mining industry is vital to the country’s economy, but it is under pressure to remain profitable and sustainable. Although many studies have focussed on how to optimise mining operations, they have used a single underutilised optimisation strategy as their data. Several industries use business intelligence (BI) to create value by gathering, analysing and presenting data. The South African mining industry is, however, still in the early stages of adopting BI. Practical outcomes from BI start by developing reports. These reports are used for making data-driven decisions that add value to businesses. Since no BI implementation guideline has been developed specifically for the mining industry, this study analysed the available guidelines critically to compare with the mining industry’s BI requirements. Three shortcomings were identified from the existing BI implementation guidelines. Firstly, none of the available guidelines evaluate the impact of developed reports on real-world operations. Secondly, these guidelines are too high level and lack the structure that will allow incremental improvements to be identified in reporting. Thirdly, there is a lack of practical guidance for report developers within the available guidelines. A comprehensive literature review was completed in three different research fields with the aim of addressing each shortcoming. The knowledge gained from the literature review was used to create a new report development framework for mining industries that addresses these shortcomings. The framework consists of three phases. In the first phase, planning is completed in a structured manner by using structured reporting qualities identified in the literature review. These reporting qualities are focus area, data availability, analytics, and visualisation. The structured reporting qualities allows report developers to assess each reporting quality and identify specific objectives for incremental improvement. Practical guidance is provided by using project management concepts such as identifying specific role players, prioritising objectives and completing them in an iterative manner to deliver practical results. The iterative execution of objectives takes place in the second phase of the framework to deliver a report that can be used for valuable data-driven decision-making. In the third phase of the framework, both the qualitative and quantitative impact of the developed report are evaluated through end-user surveys. The framework was verified by successfully applying it to three diverse case studies in the gold mining industry. In Case Study A, a report was developed to assist with mine operational water management by using available data that has not been used previously. Case Study B focussed on equipment condition monitoring and presented a data-rich problem for which a report was developed to rank equipment condition. Case Study C converted ad hoc calculations to interactive reports to evaluate future carbon tax liabilities. The case studies show that clear reporting objectives can be identified using structured reporting qualities. These objectives can be executed incrementally to achieve practical results. The qualitative and quantitative impact evaluation by end-user surveys indicate a total value add from R80 000 to R15.4 million for all three case studies. The information collected from the end-user surveys were validated by comparing the results with associated literature. This impact evaluation validated that increased data utilisation in the developed reports adds value to the respective mining operations. This study was found to be applicable to multiple possible use cases in addition to the case studies presented in this document. It is, therefore, expected that the application of the developed framework can add value to the mining industry in general.en_US
dc.language.isoenen_US
dc.publisherNorth-West University (South Africa).en_US
dc.subjectData-driven decision-makingen_US
dc.subjectBusiness intelligenceen_US
dc.subjectReportingen_US
dc.subjectMining industryen_US
dc.subjectStructured reportingen_US
dc.subjectPractical guidanceen_US
dc.subjectImpact evaluationen_US
dc.titleA value-add driven report development framework for mining industriesen_US
dc.typeThesisen_US
dc.description.thesistypeDoctoralen_US
dc.contributor.researchID20653301 - Brand, H.G. (Supervisor)


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record