Forecast-Based Decision Framework for Replacement of Ros and Onboard Components in Autonomous Loaders: A Case Study at GBC Mine PT Freeport Indonesia - Paskah Handikardo Benjamin Nainggolan; Nur Budi Mulyono (Eduvest - Journal of Universal Studies, 2026)
The mining industry plays a critical role in global economic activities, particularly in supporting the supply of essential raw materials. In Indonesia, PT Freeport Indonesia operates the Grasberg Block Cave (GBC) underground mine, which utilizes autonomous loaders integrated with the Remote Operating System (ROS). The reliability of ROS and its onboard components is essential to ensure continuous production and operational efficiency in a complex underground environment. This study aims to develop a forecast-based decision framework to determine the optimal timing for component replacement in autonomous loaders. A quantitative approach was employed using time-series forecasting based on historical downtime and component replacement data from 2023 to 2025. The Autoregressive Integrated Moving Average (ARIMA) model was applied to identify failure patterns and predict future degradation trends. The results indicate that component failures, particularly in joystick and other critical onboard systems, follow a non-random and progressive degradation pattern influenced by environmental and operational factors such as vibration, dust, humidity, and intensive usage. The ARIMA (1,0,1) model demonstrates adequate performance in capturing temporal failure behavior and supporting maintenance planning. The proposed decision framework integrates forecasting outputs, degradation trends, and operational risk considerations to support proactive maintenance strategies. The implementation of this framework is estimated to reduce downtime-related production loss by approximately 62.5%, equivalent to around 5,750 tons per month. These findings highlight the practical value of integrating forecasting models into maintenance decision-making processes. This study contributes to predictive maintenance practices by bridging the gap between failure prediction and operational decision-making in autonomous mining systems.