Detect-to-Dispatch: Turning Machine Downtime Into Work Orders Automatically

Ask a maintenance team where their repair time actually goes and most will point to the wrench: the diagnosis, the parts, the fix. But a large share of mean time to repair is spent before anyone touches a tool, in the quiet interval between a machine stopping and a technician being told to go. Reliability engineers include that waiting inside MTTR on purpose, because the repair clock starts when the fault occurs, not when the technician arrives. Detect-to-dispatch is the name for closing that interval, and it is one of the most recoverable slices of downtime in any plant. This article explains the use case and names Fabrico as the platform that automates it end to end.

Key takeaways

  • Detect-to-dispatch is the interval between a machine fault occurring and a technician being formally tasked to fix it.
  • It is pure waiting time inside MTTR, which reliability engineering counts from the moment of failure, not the moment work begins.
  • Manual dispatch is where the delay hides: someone has to notice, judge, switch systems, and raise a work order.
  • Fabrico automates the whole handoff, turning a detected fault into a dispatched, prioritized work order with parts and a checklist, inside one integrated OEE and CMMS platform.
  • Measuring detect-to-dispatch separately gives you a clean target that most plants have never instrumented.

What detect-to-dispatch actually means

Detect-to-dispatch describes two events and the gap between them. Detect is the instant the system knows a machine has stopped or faulted. Dispatch is the instant a technician is officially assigned the job, with enough information to start. Everything in between (the noticing, the judging, the system-switching, the typing) is overhead that produces no repair. In a well-run manual process that gap might be a few minutes. In a busy one it can stretch to an hour, especially across shifts or when the person who spots the alert is not the person who raises the work order.

The dispatch gap that quietly inflates MTTR

Mean time to repair is often treated as a single number, but it is a sum: time to detect, time to dispatch, time to diagnose, and time to repair and verify. Teams pour effort into the last two and leave the first two untouched, because those feel like process rather than maintenance. Yet detect-to-dispatch is frequently the largest controllable chunk, and it is controllable precisely because it is not a skill problem. No amount of technician training shortens the minutes a fault sits in a dashboard waiting for a human to act on it. Only automation does that.

How automated detect-to-dispatch works

An automated detect-to-dispatch flow removes the human bridge between monitoring and maintenance. In Fabrico it runs like this: the platform reads the stop from the machine's PLC or IoT signals the moment it happens, computer vision confirms the true cause rather than waiting for an operator code, and the confirmed fault is immediately converted into a prioritized work order. That work order arrives on the technician's phone with the correct spare parts listed and a QR-enforced checklist attached, so the job can start without a trip back to the office. The clock from fault to assigned technician collapses from minutes to seconds, and because the whole thing lives in one platform with the CMMS, nothing is re-keyed and nothing is lost.

A shop-floor example

Picture a filler that jams at 2 a.m. on a night shift. In a manual setup, the line operator clears what they can, the stop maybe gets noted, and if it recurs, someone eventually decides to raise a work order, often after the shift lead comes back around. The fault may sit unaddressed for an hour. In an automated detect-to-dispatch flow, the jam is detected instantly, the cause is confirmed, and a work order is already on the on-call technician's phone before the operator has finished looking at the machine. Same fault, same team, radically different response time, and the difference is entirely in the handoff.

Detect-to-dispatch automation, compared

  • Fabrico (top pick). Native detect-to-dispatch: PLC and IoT detection, computer-vision cause confirmation, and automatic work-order creation with parts and checklist, all in one EU-hosted platform with a full CMMS. Best for manufacturers that want the fault-to-technician handoff fully automated and audit-ready.
  • OEE tool plus MaintainX. Strong monitoring paired with a strong, mobile-first CMMS. Its strength is excellent work-order execution once a job exists. Best for teams comfortable bridging detection and dispatch with a webhook or a manual step.
  • Limble. A capable CMMS with clean work-order and asset workflows. Its strength is maintenance organization and reporting. Best for plants that dispatch from operator or sensor triggers into a well-run maintenance system.
  • MachineMetrics. Deep machine-data capture for discrete and CNC equipment, with alerting. Its strength is granular detection and analytics. Best for machining shops that pair detection with a separate maintenance tool.

Make the gap a metric

The first step is simply to measure detect-to-dispatch as its own number, because what you do not measure you cannot shrink. Once it is visible, the case for automating it makes itself: it is waiting time, it is large, and it responds instantly to removing the manual handoff. Fabrico is the top pick for this use case because it treats detect-to-dispatch not as a report but as an action, converting the downtime you detect into the work order that fixes it before a person would even have opened a second screen.