Operational Analytics
Time-usage definitions, delay coding and short-interval control that turn FMS data into decisions during the shift.
The problem
What the operation is seeing
An FMS generates more data than most sites can use, and much of it cannot be trusted because statuses and delays are coded inconsistently. Reports arrive the next morning, after the decision they should have informed.
Signs this applies to you
- Different departments publish different utilisation figures
- Shift reports that nobody reads before the next shift starts
- Delay codes that are too many, too vague, or both
- Health alarms that never reach the people who could act on them
Where it shows up
The losses this service goes after
- Fleet capability
- Tonnes moved
DelaysStandby: shift change, breaks, fuel, blasting
Hot-seat changes that take forty minutes and delay codes that say "other".
TBOS intervention
What we do
- 1
Time-usage model
One agreed set of definitions from calendar time to productive time, applied the same way across operations and maintenance.
- 2
Delay and status coding
Code lists rationalised, operator prompts redesigned, and data quality monitored so losses land in the right bucket.
- 3
Short-interval control
Hourly and in-shift views built around the decisions supervisors actually make.
- 4
Health and production signals
Equipment health and production data linked so availability losses are caught before they cost a shift.
Deliverables
What you receive
- Agreed time-usage model and KPI dictionary
- Rationalised delay and status code structure
- Short-interval control views and routines for supervisors
- Data-quality checks that run every shift
Outcome
What changes, and how it is measured
- One version of utilisation and availability across the business
- Losses visible during the shift, not the next morning
- Data trustworthy enough to base decisions on
Not sure this is where your tonnes are going?
The performance review shows which losses matter most at your operation, before you commit to any service.