ResumoO rápido avanço da tecnologia da informação e da aplicação de computadores ao setor mineral permitiu a automatização de vários processos da cadeia de valor mineral. Os sistemas ERP (Enterprise Resource Planning) forneceram a plataforma para a integração eficiente de todas as atividades de suporte da mineração. Apesar do avanço obtido com a aplicação dos computadores, não se conseguiu, até o presente momento, integrar, efetivamente, as atividades primárias da cadeia de valor mineral. A principal razão, para isso, são as incertezas existentes no processo produtivo, que são intrínsecas ao negócio, e a dificuldade de se quantificarem e qualificarem os benefícios advindos de tal integração, isto por não existir uma definição clara dos indicadores-chave de desempenho (KPIs). Esse trabalho apresenta uma análise da aplicação de sistemas ERP, na mineração do Brasil, identifica os KPIs mais importantes utilizados em empresas de mineração e discute a importância de seu mapeamento e medição para a gestão efetiva do negócio "Mineração". Palavras

Geologic modeling is an important step in determining the benefits and final pit dimensions for mining operations. Geostatistical models and distance-based functions are the main methods used to estimate the grade behavior. However, these two methods, despite their similar mean values, differ in spatial variability. The objective of this article is to prove, by comparing the two methodologies, that models with different spatial variability using the Lerchs-Grossmann algorithm will output subtly different final pit dimensions and scheduling. Furthermore, with the direct block schedule (DBS), these differences can be considerable. The tests compared the methodologies using the following three models: inverse distance (ID), ordinary kriging (OK) and turning bands simulation (TBS). The results demonstrate that the Lerchs-Grossmann algorithm is only slightly sensitive to the spatial variability of the grade; however, DBS requires the model populations to be better defined because of its greater sensitivity to spatial variability.

The transport distance in a mining operation strongly influences a mine operation revenue and its operational cycle because it is a fundamental part of the total mining costs. Generally, the transport route is determined based on an engineer's practical knowledge, which does not consider any mechanism to optimize the possible routes to be taken. In an attempt to establish a methodology for calculating the path that results in minimum costs to transport the mined block to its destination, the Dijkstra methodology is applied to a tree graph analysis, where the mining blocks are analysed as nodes of the tree. The transport cost is reflected as the arc of the graphs, which can use the Euclidean distance or the transport time for the calculation of the minimum path. The result obtained from the Dijkstra algorithm provided a non-operational route; to overcome this problem, an adjustment was performed through non-parametric equations. In this manner, it was possible to determine the transport costs for each block of the model. The paths based on Euclidean distance and transport time showed a tendency to increase for deeper mining regions. Identifying areas of largest growth and correctly quantifying their values increase the efficiency of mining planning.

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