> ## Documentation Index
> Fetch the complete documentation index at: https://docs.a7manager.se/llms.txt
> Use this file to discover all available pages before exploring further.

# A7 manager

> Lightweight Asset management platform for railway vehicle owners

## Purpose

Vehicle owners who procure traffic operations need a platform for data collection and analysis
of their assets. The tools available on the market are full-scale EAM systems (SAP, Maximo)
that are expensive and oversized relative to the owner's needs. Without a dedicated tool,
the owner becomes dependent on the operator's system, and when operators change, history
and oversight are lost.

A7 manager is a lightweight tool for asset tracking, analysis and management in accordance
with ISO 55000. It gives the vehicle owner data ownership and continuity regardless of the
contracted operator, along with a standardised data model that enables comparisons across
contracts, operators and fleets.

The maintainer delivers data via a standardised import pipeline that validates
vehicle/component identity, odometer readings, mounting rules and duplicates.

## Scope

| Area                        | Description                                                                                                                                                                  |
| --------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Project configuration**   | Vehicle types with an EN 15380-2 structure, component types, mounting slots, and maintenance plans with kilometre and time intervals. Everything else builds on this         |
| **Assets**                  | Vehicles with running odometer readings and full configuration — which component sits where, with complete change history — plus per-component readings and lifecycle status |
| **Service book**            | Corrective and preventive maintenance, due status with tolerance windows, out-of-service periods, cleaning and emptying                                                      |
| **Strategic KPI**           | MDBF, MTTR, defect rate per product group, planned-versus-unplanned ratio, compliance, recurring faults                                                                      |
| **Maintenance planning**    | Projected workload in activities, manhours and cost, and a scheduler for placing the work                                                                                    |
| **Wheel analysis**          | Wear trends, remaining useful life, and a Monte-Carlo reframing demand forecast                                                                                              |
| **Warranty**                | Claims raised directly from recorded defects and driven with the supplier through a structured workflow, with grouped claims for systematic faults                           |
| **Inspections**             | Campaigns built from the vehicle structure, carried out in an offline-capable field app, with AI-summarised reports                                                          |
| **High-value components**   | Tracking dismounted high-value components through repair at a subcontractor                                                                                                  |
| **Ask AI** *(experimental)* | Questions put to the data in natural language, answered with aggregates and interactive charts                                                                               |

## Technical stack

A Django REST backend over PostgreSQL, a React web application, and a separate offline-first
PWA for inspectors. Bulk data arrives through an Azure Data Factory pipeline.

See [Technical stack](/platform/technical-stack) for details, and
[Environments](/platform/environments) for the production topology and how the development
environment differs.