The challenge
A mining operation in Indonesia manages a large fleet of heavy machinery, vehicles and critical equipment where unexpected failures can cause costly downtime. Traditional maintenance planning often relies on fixed service intervals or action taken only after abnormal equipment behaviour is detected. Spare parts purchasing can also become inefficient when project duration is not considered. For example, when a mining project is approaching completion, purchasing expensive OEM or original parts may create unnecessary cost if a suitable third-party replacement can reliably cover the remaining operating period.
What changed with Synorex
The company implemented Synorex One to support an AI-driven Predictive Maintenance and spare parts lifecycle planning workflow. The solution analyses equipment operating data, abnormal trends and expected component life to estimate when machines, vehicles or key equipment may require maintenance or replacement. It also considers the remaining project duration so maintenance teams can evaluate whether to continue using OEM / original parts or select suitable third-party replacement parts whose expected lifespan is sufficient for the remaining project period.
Business outcome
The mining team gained earlier visibility into potential equipment failures, helping reduce unexpected downtime and improve maintenance planning. By combining predicted component life with the project lifecycle, the company can make more practical spare parts purchasing decisions, avoid unnecessary high-cost replacement parts near the end of a project and improve overall maintenance cost efficiency.
