Connects the platform’s predictions to the systems that actually create work orders and order parts, so an alert becomes a scheduled job.
A predictive maintenance alert that lands in a dashboard nobody owns produces nothing. Value appears only when the prediction becomes a work order with a part reserved and a slot in the schedule.
Bidirectional integration creates work orders from platform findings with the evidence attached, and reads back execution and findings so the models learn from what was actually found. Master data alignment between the platform and the maintenance system is treated as a prerequisite, not as a later clean-up.
Integration complexity depends almost entirely on the state of the maintenance system, and heavily customised ERP installations frequently need more integration effort than the predictive capability itself. Where equipment registers disagree between systems, that must be resolved first — which is master data work, not integration work.
Every capability page states a real limitation. If a vendor cannot tell you where their product stops, you are not being told the truth about where it starts.
Puts every piece of equipment into one reliability taxonomy so that failure rates mean the same thing across all fields, and maintenance is planned from them.
Establishes one authoritative record per well that every other system agrees with, including a single answer to how deep it is and where the top of the reservoir sits.
Tracks what tubulars and critical spares actually exist, where they are, and what the drilling programme will need them for.
A technician photographs a nameplate and the equipment record is created or matched, instead of being typed from memory later.
Operators record readings and observations on a phone that works with no signal, and everything syncs when the device gets back to coverage.