Peter Knights

Rethinking Pareto analysis: maintenance applications of logarithmic scatterplots

Pareto histograms are commonly used to determine maintenance priorities by ranking equipment failure codes according to their relative cost or downtime contribution. However, such histograms do not readily enable identification of the dominant variables influencing downtime and repair costs, namely the failure frequency, mean downtime and mean repair cost associated with each failure code. Advances an alternative method for analysing equipment downtime and repair costs using logarithmic (log) scatterplots. By applying limit values, log scatterplots can be divided into four quadrants enabling failures to be classified according to acute or chronic characteristics and facilitating root cause failure analysis. Log scatterplots permit the identification of frequently occurring failures that consume relatively little repair cost or downtime yet cause frequent operational disturbances leading to production losses. In addition, by graphing the trend of failure data over successive time periods, log scatterplots provide a useful visual means of evaluating the performance of maintenance improvement initiatives. Provides examples of the practical application of log scatterplots by a number of mining companies and mining equipment suppliers in Chile.

Critical spare parts ordering decisions using conditional reliability and stochastic lead time

Abstract Asset-intensive companies face great pressure to reduce operation costs and increase utilization. This scenario often leads to over-stress on critical equipment and its spare parts associated, affecting availability, reliability, and system performance. As these resources impact considerably on financial and operational structures, the opportunity is given by demand for decision-making methods for the management of spare parts processes. We proposed an ordering decision-aid technique which uses a measurement of spare performance, based on the stress–strength interference theory; which we have called Condition-Based Service Level (CBSL). We focus on Condition Managed Critical Spares (CMS), namely, spares which are expensive, highly reliable, with higher lead times, and are not available in store. As a mitigation measure, CMS are under condition monitoring. The aim of the paper is orienting the decision time for CMS ordering or just continuing the operation. The paper presents a graphic technique which considers a rule for decision based on both condition-based reliability function and a stochastic/fixed lead time. For the stochastic lead time case, results show that technique is effective to determine the time when the system operation is reliable and can withstand the lead time variability, satisfying a desired service level. Additionally, for the constant lead time case, the technique helps to define insurance spares. In conclusion, presented ordering decision rule is useful to asset managers for enhancing the operational continuity affected by spare parts.

A decision-making framework to integrate maintenance contract conditions with critical spares management

Maintenance outsourcing is a strategic driver for asset intensive industries pursuing to enhance supply chain performance. Spare parts management plays a relevant role in this premise since its significant impact on equipment availability, and hence on business success. Designing critical spares policies might therefore seriously affect maintenance contracts profitability, yet service receivers and external providers traditionally attempt to benefit separately. To coordinate both chain parties, we investigated whether the spare components pool should be managed in-house or contracted out. This paper provides a decision-making framework to efficiently integrate contractual conditions with critical spares stockholding. Using an imperfect maintenance strategy over a finite horizon, the scheme maximizes chain returns whilst evaluating the impact of an additional part to stock. As result, an original joint value – preventive interval and stock level – sets the optimal agreement to profitably allocate the components pool within the service contract. Subsidization bonuses on preventive interventions and pooling costs are also estimated to induce the service provider to adjust its policy when needed. The proposed contractual conditions motivate stakeholders to continuously improve maintenance performance and supply practices, thus obtaining higher joint benefits.

Dynamic Short term production scheduling and machine allocation in underground mining using mathematical programming

Abstract Maximising value is the main objective when developing long term mine production schedules. These results provide input for the development of a short term schedule that aims to meet process plant feed requirements so as to produce a quality saleable product. This paper reviews previous work on optimised short- and long term production scheduling and real time fleet management systems. A new dynamic mathematical model using mixed integer programming is proposed to optimise short term production scheduling and machine allocation for application in sublevel stoping operations. The objective of the model is to minimise deviation from targeted metal production. The dynamic nature of the model not only optimises the shift based schedule but also allows rapid equipment reassignment to take place as underground operating conditions change. Optimal results are generated in less than 1 min when trialled on a conceptual sublevel stoping dataset.

Business-oriented prioritization: A novel graphical technique

Traditionally, Pareto analysis has been used to select the most critical components and failure modes of a system. A clear disadvantage of this technique is that it requires preselecting a single criterion to establish priorities. More recently, a graphical log-scatter diagram technique has been proposed. It considers three key performance indicators simultaneously: reliability (MTBF), maintainability (MTTR), and unavailability (D). This technique considers only times and does not include economical effects explicitly. This article extends both techniques to explicitly consider both direct and indirect costs to prioritize from the point of view of an asset manager or from a maintenance decision-maker, as required. Due to the economic-based approach of this article, cost discounting is also considered inside financial costs such as—but not limited to—reliability-related investments. Also, the results are displayed on simple and accessible graphs which make them particularly useful for conveying results to non-technical managers. The methodology is illustrated by analyzing a shovel from the copper mine industry, and it clearly shows how the proposed technique facilitates business oriented decisions and how they should change under different market conditions.

Integration of real options into short-term mine planning and production scheduling

Commodity prices have fallen sharply due to the global financial crisis. This has adversely affected the viability of some mining projects, including leading to the possibility of bankruptcy for some companies. These price falls reflect uncertainties and risks associated with mining projects. In recent years, much work has been published related to the application of real options pricing theory to value life-of-mine plans in response to long term financial uncertainty and risk. However, there are uncertainties and risks associated with medium/short-term mining operations. Real options theory can also be applied to tactical decisions involving uncertainties and risks. This paper will investigate the application of real options in the mining industry and present a methodology developed at University of Queensland, Australia, for integrating real options into medium/short-term mine planning and production scheduling. A case study will demonstrate the validity and usefulness of the methodology and techniques developed.

Applications of discrete mine simulation modeling in South America

ABSTRACT This paper reviews applications of discrete event simulation modeling within the mining industry in South America. The paper examines the types of simulation studies undertaken, the simulation languages used and reflects on the factors that have restricted the more widespread use of simulation modeling within South America.

Optimal replacement intervals for shovel dipper teeth

This article presents an analysis of wear rates and optimal replacement intervals for the teeth of an Optima 62 cubic yard dipper with a PD Ab/Wr wear package in operation at the Morenci Mine. The current replacement strategy is to change the entire set of nine teeth when significant wear is observed or when teeth or adapters are found to be missing. Over an 8 week period, 13 change-outs of teeth sets were recorded. For eight of these change-outs, a laser profile was taken of each tooth upon removal to determine metal loss. A technique known as ‘grouped failure data with multiple suspensions’ was applied to quantify the failure rates of the teeth. Failure rate functions were also determined for subgroups including the outer four teeth located near the dipper wing shrouds and the inner five teeth located nearest to the dipper center line. Weibull cumulative distribution functions were fitted to the dipper teeth failure data. Wear rates for the outer four teeth located next to the dipper wing shrouds were found to lag those experienced by the five center teeth of the dipper by ∼83,000 tons. A group replacement strategy whereby entire tooth sets are changed out was found to give better cost per ton performance when compared with a staged policy whereby the outer four teeth are changed out at double the replacement interval of the inner five teeth. Potential exists to reduce unplanned maintenance downtime by decreasing the average replacement interval for dipper teeth sets from 255,000 tons to 200,000 tons. This is equivalent to reducing the replacement interval from one tooth set every 4 days of continuous operation to one tooth set every 3 days of continuous operation. The benefits of adopting such a strategy are estimated at a saving of US

Quantifying the benefits of simulator training for dragline operators

300,000 per shovel per year.

Modelling of In-Pit Crusher Conveyor alternatives

Abstract Dragline operator performance significantly effects the profitability of strip coal mines. To this effect, the mining industry is increasingly making use of interactive simulators that employ a high-fidelity virtual reality environment to train dragline operators. To date it has been difficult to quantify the benefits of investing in simulator training for dragline operators. Performance and machine duty data can be collected, but need to be analysed in relation to a control group of operators considering the potential for bias because of the ‘Hawthorne effect’. This study advances a methodology to quantify the improvements in dig productivity and machine duty for a group of four experienced operators exposed to simulator training. The size of the group was limited by operational and time restrictions. Dig productivity (bench cubic metres/dig hour) and machine duty (cumulative boom stress index) were logged for a 1 month period following training. The performance of a ‘control’ group of three operators, who did not undertake simulator training, but were provided with verbal feedback on their performance, was also monitored. Three of the four experienced operators who participated in this study achieved a significant short term improvement in one aspect of their performance without sacrificing machine duty. Two of these three operators sustained this improvement over the subsequent month of observation. Two of the control group of three operators demonstrated no appreciable change in performance, while the third achieved a significant reduction in boom stress index at the expense of decreased dig productivity. As such the performance improvements observed by operators trained in the immersive simulator cannot be attributed to feedback alone or to the Hawthorne effect. The results indicate that immersive simulator training can provide a quantifiable short term improvement in the performance of experienced dragline operators. Further work is required to quantify the long term sustainability of these performance improvements.