Case study · Illustrative example

Cut overtime 18% for a 300-technician field-service operation

Illustrative example. This shows how an engagement of this kind typically runs. It is not a report of a specific client's project, and the figures are typical outcomes, not measured client results. Your numbers depend on your data; the readiness assessment tells you what to expect.

The typical situation

Rosters were built each month from the previous month, so technicians were overstaffed in branches where demand had moved and overstretched where it had grown.

Ticket data from two years sat in the service-management tool. Nobody had looked at it by weekday, branch and season together.

What the data looked like

Two years of tickets with timestamps and branch, attendance records, overtime claims and the SLA definitions. No data-quality problems to speak of; the tool was well maintained.

What we built

  • A weekly demand forecast per branch, accurate to within 9% on average.
  • A roster suggestion the planner adjusts, not a replacement for the planner.
  • A one-page report comparing forecast to actual every week, so the planner can see when to trust it.
Your systemsClean data layerModel or rulesAlerts and reports

Typical outcome

MeasureBeforeAfterPeriod
Overtime hours2,100 / month1,720 / monthquarter before vs quarter after
Tickets breaching SLA6.1%4.4%same period

What usually trips these projects up

We should have involved branch managers earlier; the first roster suggestions were technically right and politically unwelcome. Adoption rose once managers could see the forecast-vs-actual report.

Have a similar problem?

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