Research Outputs (Mining Engineering)

Now showing 1 - 3 of 3

First cycle experience of a business process re-engineering programme at Shabanie Mine.
(The Southern African Institute of Mining and Metallurgy., 2005-04) Musingwini, C.; Muzoriwa, C.; Phuti, D.; Mbirikira, D.
In the past ten to fifteen years, many organizations have applied business process re-engineering (BPR) to significantly improve their business competitiveness or stave off closures. The mining industry in Southern Africa is no exception and documented examples can be drawn from South Africa. Although the concept is superficially simple, its application has been marked by a high failure rate of about 70 per cent because it has been generally misunderstood. Shabanie mine, a chrysotile asbestos fibre producer in Zimbabwe took cognisance of this fact by cautiously embarking on a modular BPR programme in October of 2002. A year was used as a complete cycle or module for re-evaluation of the programme. Shabanie mine adopted BPR as part of management efforts to remain competitive amid serious threats to operational viability. These threats included hyper-inflation driven rising production costs, a declining world asbestos market and a possibility that Russia could take over the shrinking world asbestos market by dumping low-priced asbestos fibre. The only competitive advantage that the mine had was the high quality of its long-fibre chrysotile asbestos. The major BPR thrust was therefore to redesign processes for improved productivity and ultimately achieve a lower cost per ton of final asbestos fibre product. In addition, corporate culture change and cost-saving were also factored into the programme. This paper discusses the implementation experience of the BPR programme at the mine. The main BPR beneficial highlights are improved productivity, sizeable cost-savings, positive corporate culture change and identification of secondary projects. One of the lessons learnt from this programme is that mining companies will have to deal with the HIV/AIDS pandemic if they are to sustain high levels of productivity into the future.
Technical operating flexibility in the analysis of mine layouts and schedules.
(The Southern African Institute of Mining and Metallurgy., 2007-02) Musingwini, C.; Minnitt, R.C.A.; Woodhall, M.
Often overlooked factor in the analysis of mine layouts and schedules is technical operating flexibility (or tactical flexibility), mainly due to its nebulous nature. By glossing over technical operating flexibility the resultant mine layouts and schedules may be suboptimal. The need to incorporate technical operating flexibility into the analysis and comparison of mine layouts and schedules is increasing in importance. The nature of technical operating flexibility is illustrated, previous work on valuing of operating flexibility reviewed, and a proposal made on how technical operating flexibility can be quantified for tabular reef mines by using a platinum reef deposit as a case study. Once technical operating flexibility has been quantified it becomes possible to explore its incorporation into the analysis of mine layouts and schedules and subsequent optimization processes. This paper is a revised version of a paper presented in the Proceedings of the Second International Platinum Conference, 'Platinum Surges Ahead' in 2006. The work described in this paper is part of a current PhD study at the University of the Witwatersrand.
Modelling open pit shovel-truck systems using the Machine Repair Model.
(The Southern African Institute of Mining and Metallurgy., 2007-08) Krause, A.; Musingwini, C.
Shovel-truck systems for loading and hauling material in open pit mines are now routinely analysed using simulation models or off-the-shelf simulation software packages, which can be very expensive for once-off or occasional use. The simulation models invariably produce different estimations of fleet sizes due to their differing estimations of cycle time. No single model or package can accurately estimate the required fleet size because the fleet operating parameters are characteristically random and dynamic. In order to improve confidence in sizing the fleet for a mining project, at least two estimation models should be used. This paper demonstrates that the Machine Repair Model can be modified and used as a model for estimating truck fleet size in an open pit shovel-truck system. The modified Machine Repair Model is first applied to a virtual open pit mine case study. The results compare favourably to output from other estimation models using the same input parameters for the virtual mine. The modified Machine Repair Model is further applied to an existing open pit coal operation, the Kwagga Section of Optimum Colliery as a case study. Again the results confirm those obtained from the virtual mine case study. It is concluded that the Machine Repair Model can be an affordable model compared to off-the-shelf generic software because it is easily modelled in Microsoft Excel, a software platform that most mines already use. This paper reports part of the work of a MSc research study submitted to the University of Witwatersrand, Johannesburg, South Africa.

Research Outputs (Mining Engineering)

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