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In this comprehensive article series, 'Asset Management in the Mining Industry: A Blueprint for Operational Excellence’, we delve deep into the world of mining to unearth the secrets of effective asset management. Our focus will extend from the intricate planning stages to the operational stage where effective Reliability Engineering drives performance, reduces unnecessary expenditure, and maximizes the lifespan of equipment. Readers will gain invaluable insights from case studies and the latest research in the field. Whether you're a seasoned industry professional or a newcomer eager to learn, this series promises to equip you with the knowledge and tools necessary to achieve operational excellence in the mining industry.
Every Reliability Engineer will be familiar with the Weibull Analysis. Most of us even have an Excel template laying around that we refer to!
The problem is, that when we have to handle Suspended data (e.g. components that haven’t failed yet at time of observation), the Excel sheet must use VBA in the background if the user wants a “single-button” tool.
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The mining industry in Australia has for some time been plagued by high turnover in its skilled maintenance workforce. The tough lifestyle and remote/regional locations are some of the main contributing causes, and have been for years, however other industries of employment offering competitive wages and a better lifestyle have in the recent years pulled the workforce from mining, leaving a void of labor numbers behind that have proved difficult to fill.
The common query, “How do I keep equipment reliability up, even with high turnover?” prompts a standardized response from most maintenance professionals – proceduralize the maintenance system to reduce variability and maintain consistent output. However, while this approach bears truth, it overlooks a substantial aspect…
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Industrial operations that have operating horizons exceeding the lifespan of their assets face a crucial decision as they approach this timeline’s end (but not enough to operate the equipment until its full economically optimal life).
Specifically, they must decide whether to overhaul the asset, replace it with a new one, or rent the equipment until operations conclude. Given the numerous variables with inherent uncertainties in the financial models, how can they be confident in their decision?
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Asset management in the mining industry is an intricate dance of financial and operational factors. It demands a firm understanding of complex financial concepts to make strategic decisions effectively, one of which is the discount rate. This number, far from being just an abstract figure, can fundamentally shape a business’s strategic path, particularly concerning asset replacement.
The discount rate is essentially the interest rate used to calculate the present value of future cash flows. The concept comes into play when considering an asset replacement. This decision involves weighing costs and benefits over a time horizon that often spans several years. The discount rate serves as the conduit that translates these future values into present terms, enabling apples-to-apples comparisons. Most of use use this in NPV calculations where we evaluate business improvement initiatives, and compare the viability and return of various projects.
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Jack-Knife Diagrams, also known as Log-Scatter Plots, serve as an invaluable visual tool in the realm of Reliability Engineering for prioritizing areas of downtime that require improvement. While many engineers rely on Pareto Charts, we will explore the shortcomings of this approach and how the Jack-Knife Diagram overcomes them.
Now, let’s delve into the distinction between the two methods.
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