Optimisation of an Open Pit Diamonds Mine’s Mine-To-Mill Value Chain Using Probabilistic Value Stream Mapping

Abstract

This study optimised a mine-to-mill value chain of a case study open-pit diamonds mine that experienced an average of 26% production shortfall between 2017 and 2022. A mixed methods approach was applied, combining quantitative and qualitative methods. Quantitative analysis of historical data (2018–2022) from production reports, processing plant records, and block models was supported by qualitative insights from expert interviews and field observations. Probabilistic value stream mapping and Monte Carlo Simulation (MCS) were used to diagnose inefficiencies, model variability, and improve process performance. MCS was used to simulate fluctuations in processed tonnes, feed grade, recovered carats and recovered grade, with cumulative distribution functions visualising predictability and variability. Current state mapping and process activity mapping were used to categorise activities across the value chain into Value-Adding (VA), Non-Value-Adding (NVA), and Necessary but Non Value-Adding (NNVA) activities, helping to identify material flow bottlenecks and inefficiencies. Blasting, crushing and screening, dense media separation, recovery and sorting were identified as VA activities. NNVA activities included loading and hauling and stockpiling. Activities sending material to the tailings dam were classified as NVA. Excessive stockpiling, unplanned equipment downtime, inefficient haulage cycles, redundant material rehandling, and misalignment between blasting and downstream processing rates were identified as inefficiencies. A proposed future state map incorporated lean principles, predictive maintenance, real-time density monitoring, and Radio Frequency Identification (RFID) enabled ore reconciliation. The results showed significant improvements in recovery efficiency, throughput, and process stability. Sensitivity analysis further identified feed grade and recovery efficiency as the most critical drivers of variability, while a cost–benefit analysis confirmed the economic viability of the proposed optimisations. The study contributes a scalable methodology for value chain optimisation in high-variability mining environments and recommends the integration of digital tools, standardised operational practices, and continuous improvement to achieve sustainable mine performance.