Building business cases for maintenance improvement projects

Many maintenance managers find it difficult to justify investments in maintenance improvement initiatives. In part, this is due to a tendency by mine managers to regard maintenance purely as a cost centre, and not as a process able to influence productive capacity and profit. It is also hindered by a lack of alignment between commonly used maintenance performance measures and key business drivers, and the lack of formal business training amongst maintenance professionals. With this in mind, a model to assist maintenance managers in evaluating the benefits of maintenance improvement projects was recently formulated. The model considers four cost saving dimensions. These are: 1. reduction in the cost of unplanned repairs and maintenance, 2. increased or accelerated production and/or sales, 3. spares inventory reduction, and 4. reduction in over-investment in physical assets and operating costs. This paper discusses the application of this model and a number of numerical examples are given to justify investments in maintenance improvement projects having varying objectives.

Quantification of geological uncertainty and risk using stochastic simulation and applications in the coal mining industry

Stochastic simulation is a recognised tool for quantifying the spatial distribution of geological uncertainty and risk in earth science and engineering. Metals mining is an area where simulation technologies are extensively used; however, applications in the coal mining industry have been limited. This is particularly due to the lack of a systematic demonstration illustrating the capabilities these techniques have in problem solving in coal mining. This paper presents two broad and technically distinct areas of applications in coal mining. The first deals with the use of simulation in the quantification of uncertainty in coal seam attributes and risk assessment to assist coal resource classification, and drillhole spacing optimisation to meet pre-specified risk levels at a required confidence. The second application presents the use of stochastic simulation in the quantification of fault risk, an area of particular interest to underground coal mining, and documents the performance of the approach. The examples presented demonstrate the advantages and positive contribution stochastic simulation approaches bring to the coal mining industry