Correlation analysis is a data interpretation tool that draws inferences between two or more variables. Mine dispatch systems produce massive amounts of data that requires interpretation in order to identify contributing factors to both tonnage and cost losses. These losses are analysed to identify the significance of factors that contribute to these losses. Such factors as: loading rate, daily tonnage and truck loads, particle size distribution, fuel costs, labour costs, tyre costs, repairs and maintenance etc. For each analysis, the R (correlation coefficient) parameters were determined to classify the significance of each loss factor. A scatter graph was generated to determine the correlation factors for each key performance indicator (KPI) and corresponding loss. The scatter graphs illustrated the strength, direction, correlation coefficient and association of the relationship between the two variables. This analysis was run using data from an open pit mines’ January 2021 to October 2021 production reports sourced from the mine's Modular Dispatch System. The analysis showed that labour costs and tyre costs both had the highest correlation factor of 1 when inferred to total mining costs, meaning they were the most significant contributors to mining costs and could contribute greatly to financial losses. Loading rate had the highest correlation factor (0.7147) out of all the other factors inferred to tonnage losses. The results showed the importance of correlation as a tool to reveal information about the root causes of productivity losses.