Mine Performance Assessment by Means of Stochastic Frontier Analysis

Ioannis E Tsolas

doi:10.1155/2019/8657348

Abstract

This paper employs stochastic frontier analysis (SFA) in assessing efficiency at the mine level. An SFA model is derived using annual operational data from the Kardia Field mine of the Greek Public Power Corporation (PPC) S.A. for the 1984-2006 period and the causes of inefficiency are investigated by means of regression techniques. The proposed two-stage model can be used as a diagnostic tool to identify causes of mine inefficiency and as a tool for designing and specifying interventions to improve mine performance.

Highlights

Performance measurement in mining deals with the establishment of criteria for determining how well mining production units meet their objectives
It is worth noting that the two-stage approach was adopted in this paper, the one-step approach was performed as an experiment for the benefits of robustness; the results show that the only significant factor in explaining inefficiency is the incremental stripping ratio (ISR)
This study advances the literature by incorporating stochastic frontier analysis (SFA) and the Tobit regression analysis to identify the drivers that can explain the performance at the mine level

Summary

Introduction

Performance measurement in mining deals with the establishment of criteria for determining how well mining production units meet their objectives. Among performance metrics the most frequently employed are labor productivity [1] and efficiency [2]. Productivity is the quotient obtained by dividing the output by each input or the sum of inputs. Productivity measures include [3] partial factor and multifactor or total factor productivity (TFP). Output is being considered to one class of input and the most used partial metric is labor productivity. Multifactor productivity is measured as the ratio of output to a set of combined inputs such as labor capital and land

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Conclusion