Field note 02 · Heavy civil operations · Equipment lifecycle cost
Quarterly lifecycle-cost analysis, from 2–3 weeks to about 6 hours
Lifecycle-cost models for each equipment model were built by hand every quarter. A Python engine now applies the same logic to every model and produces the outputs for review.
Built by RootN’s founder as an engineer inside a Western Canadian heavy-civil contractor’s equipment operation. The employer and its systems are not named.
- 8 h → ~20 min
- per equipment model
- 2–3 wk → ~6 h
- for the full quarterly analysis
- Every model
- run with the same logic
- Quarterly
- refresh, from current data
The situation
Lifecycle-cost models support repair, rebuild and replace decisions for each equipment model, every quarter. Built by hand, each one meant pulling cost and hours data from several sources, working out cost per hour over the machine’s life and formatting the results for review: about 8 hours per model, and 2–3 weeks for a full cycle.
What was built
A Python engine that takes in the cost and hours data, applies one lifecycle-cost method to every equipment model and produces standardized, formatted outputs ready for the capital review.
The result
About 20 minutes per model instead of about 8 hours, and about 6 hours for the full quarterly analysis instead of 2–3 weeks. Because every model uses the same logic, results can be compared across the fleet.
What’s still manual
The decision. The engine prepares the numbers; the keep, rebuild or replace call stays with the people accountable for the fleet.
What this means for you
Where the same pattern applies
If your replacement timing depends on one analyst’s spreadsheet, or the analysis is so slow it only happens once a year, this is the place to start.