Maintenance Management That Catches It Before the Line Stops.
Dharte AI tracks the condition of every machine, runs time- and usage-based preventive schedules, logs breakdowns through to resolution with MTTR, and ties spare parts directly to store inventory so a repair is not delayed by a missing component.
What is maintenance management software?
Maintenance management software records the condition and service history of production equipment, schedules preventive work before failure, and manages breakdowns from the moment they are reported to the moment the machine is back in production.
Its effect on output is indirect but large: unplanned downtime does not only cost the hours lost, it invalidates the production plan that everything downstream was built on.
Machine and equipment management
Machine health
Current status, last check and recorded condition for every machine in the plant.
Preventive schedules
Maintenance plans driven by elapsed time or accumulated usage.
Breakdown logging
Capture, assign and resolve failures with mean time to repair tracked throughout.
Spare parts linkage
Maintenance requirements tied directly to store inventory, so spares availability is known before the job starts.
Predictive failure scoring
Risk flags raised from recorded machine history before a small problem becomes a shutdown.
A failure at one plant becomes a check at every other
For multi-site manufacturers, the same machine types usually run in several plants. Because maintenance records share a structure across sites, a recurring failure pattern identified at one location can raise preventive checks against the same equipment elsewhere rather than waiting to be rediscovered.
Maintenance connected to the production plan
Scheduling preventive work without visibility of what is running is how maintenance windows get cancelled. Dharte AI holds maintenance schedules alongside the production plan and the store inventory that supplies the spares, so the window you book is one you can actually keep.
On the roadmap
A full Total Productive Maintenance module — OEE per machine, per shift and per site, autonomous maintenance and Kobetsu-Kaizen — is in active development and not yet generally available.
How this connects to the rest of the platform
Every Dharte AI module shares the same records, so the modules below act on the data this one produces — and vice versa.
Production Management
Work orders, route cards, plan versus actual and per-shift throughput on live data.
Learn moreInventory Management
Raw material, WIP and finished goods with batch traceability and reorder alerts.
Learn moreManufacturing ERP
The connected system of record for production, quality, materials, procurement and people.
Learn moreManufacturing Automation
Approval chains, reorder alerts, RFQs and audit documents that run themselves.
Learn moreFrequently asked questions
Yes. Preventive schedules can be driven by elapsed time or by accumulated machine usage, and the required spare parts are linked to store inventory so availability is confirmed before the work is scheduled.
Dharte AI scores risk from the machine history it holds — recorded condition checks, breakdown frequency and maintenance records — and raises a flag when a machine's pattern indicates elevated risk. It is a scoring model built on your logged maintenance data.
Machine health in Dharte AI is built from recorded checks, usage and maintenance history rather than from live sensor feeds. Dharte AI provides open APIs, so data from a machine monitoring system you already run can be sent into it.
Yes. Spares are held in the same store inventory as production materials, so a maintenance job draws against real stock and can raise a shortage alert like any other item.
See it running on a real manufacturing operation.
Thirty minutes, with your product types and your workflows — not a slide deck.
Book a Live Demo