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Predicting First-Time Fix in Field Service

A return visit costs several times the first one. Predicting the likely fault and parts needed before dispatch, from the customer's own description.

Updated 2 min readBy SpiderHunts Technologies

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Quick answer — TL;DR

Most failed first visits are caused by the wrong parts or the wrong skills being sent. Both are predictable from the fault description and asset history, and the fix is usually better dispatch information rather than a better engineer.

The economics of the second visit

A return visit costs travel, labour and a slot that could have served another customer, plus the customer's time and goodwill. It is several times more expensive than getting it right first time.

Most organisations track first-time fix as a metric and treat it as a performance issue for engineers. Analysis usually shows the causes sit upstream, in what was known at dispatch.

What actually causes a failed first visit

  • The required part was not on the van and not ordered
  • The engineer did not have the right skills or certification for that asset
  • Access was not available - site closed, key holder absent, equipment in use
  • The reported fault was not the actual fault
  • A specialist tool was needed and not carried
  • Preparatory work by another trade had not been done

Only one of these is about the engineer's competence. The rest are information problems, which means they are addressable with better prediction at the point of dispatch.

Predicting the parts

The most valuable prediction is usually which parts will be needed. Inputs are the fault description, the asset type and age, its service history and what has been used on similar jobs.

The output should be a ranked list rather than a single part. Carrying the top three candidates is often practical where carrying every possibility is not, and that alone lifts first-time fix materially.

Van stock optimisation follows from the same model. Knowing what each engineer's likely job mix requires lets you stock vans by area and specialism rather than identically.

Fault descriptions come from customers who are not technical, through a call handler working quickly. 'It is making a noise' is common and carries little information.

ImprovementEffect
Structured triage questions by asset typeMuch better input, small time cost
Photos or video from the customerOften decisive for identifying a model or fault
Asset history surfaced to the call handlerContext the customer does not have
Suggested follow-up questions from the modelImproves the description as it is taken

The fourth row is a neat application. A model that suggests the next question based on what has been said so far improves the input rather than only working with it.

Measure the right thing

First-time fix rate alone can be gamed - by declining difficult jobs, or by recording a partial fix as complete. Pair it with repeat visits within a period and with customer-reported resolution.

Also separate what is controllable. A visit that failed because the site was locked is a scheduling and communication problem, not a parts problem, and lumping them together hides both.

Most failed first visits were decided at dispatch, not at the door.

FAQ

Frequently asked questions

The questions readers ask us after this guide.

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How much job history is needed?

Enough completed jobs per asset type and fault category to learn the parts patterns - typically a couple of years for a reasonable-sized operation.

Will engineers accept part suggestions?

Generally yes if presented as suggestions with the reasoning, and if they are usually right. Mandating them without evidence produces resistance.

What if our fault codes are inconsistent?

That is normal. Standardising them, including retrospectively on a sample, is usually the first piece of work.

Does this need integration with our field service system?

To act on predictions at dispatch, yes. Analysis can start from exported data to prove the value first.

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