A SYSTEMATIC APPROACH TO THE OPTIMISATION OF EXTRA LOW PROFILE (XLP) MECHANISED MACHINE PERFORMANCE

Michael James Andrews

Research output: ThesisMaster's Thesis (University Course)

Abstract

An investigation aimed at optimising the eXtra Low Profile (XLP) mechanised machine fleet performance was conducted at Anglo Platinum’s Bathopele Mine during 2009 and 2010. A systematic approach was applied using Goldratt’s Theory of Constraints (TOC) in an attempt to methodically quantify the XLP mechanised breast machine fleet performance, identify system bottleneck activities within the XLP cycle and address these where possible. The purpose of applying the TOC model was to ensure that suitable attention was given to the bottleneck machinery within the mining cycle, thereby ensuring that any attempts to improve bottleneck performance would have a direct (and substantial) improvement on overall mine productivity and output. The scope of the investigation was limited to the XLP machine performance only; although the TOC methodology could be applied to the entire mine system and environment, it was decided that suitable scope was available with such a focused approach. The study commenced with a time- and-motion study in an attempt to provide updated machine performance per cycle for each of the machines in the XLP suite. It was generally seen that the XLP Roof Bolter was the slowest performing machine indicating this as the bottleneck machine within the XLP suite. The next slowest machine was identified as the Load Haul Dump (LHD) machine.
Translated title of the contributionA SYSTEMATIC APPROACH TO THE OPTIMISATION OF EXTRA LOW PROFILE (XLP) MECHANISED MACHINE PERFORMANCE
Original languageEnglish
Supervisors/Advisors
  • Moser, Peter, Supervisor (internal)
Award date21 Oct 2010
Publication statusPublished - 2010

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