What is the most effective wayPlants and asset-heavy operationsUse case, not a client result
How many technicians and spares do we need to hit our uptime target?
The decision it informs
Technician cover by shift, which spares to hold and which maintenance routine to adopt.
A dashboard showsDowntime and repair times.
An investigation tells youWhat one fewer technician, or one more spare, would do to uptime.
Asset age
Technician cover by shift
Spares availability
Preventive routine
Breakdown clustering
Repair time
01
A downtime mapThe few assets and times behind most of the lost hours.
02
A cover modelUptime against technician cover and spares policy, with the cost of each option.
03
A routine recommendationWhich preventive schedule to adopt, with the evidence.
- What if night shift gains one technician?
- What if the three slowest spares are stocked?
- What if the preventive routine moves to a fixed monthly slot?
Method 1Failure pattern analysisWhen and where breakdowns cluster, and which assets drive most of the downtime.
Method 2Queue simulationHow waiting for a technician or a spare builds up under different cover.
Method 3Routine comparisonWhich preventive routine goes with fewer failures, tested against the history.
Data we would look at
Breakdown and work-order historyRepair times and technician logsSpares stock and lead timesAsset age and service recordsProduction schedule
First week: the questions we ask
What uptime target matters, and what does a lost hour cost?How do breakdowns cluster by line, shift or asset age?Which spares cause the longest waits?What does the maintenance team do that never reaches a ticket?