Making Informed Last-Time-Buy Decisions
Forecast with confidence. Reduce risk. Secure long-term spare-parts availability — even when production data is incomplete.
Why this matters
When production ends, suppliers must decide how many spare parts to secure for 15–30 years of service. Mistakes are expensive: shortages damage customer trust; overstock ties up capital and creates sustainability risks.
- Variant diversity — many variants create thousands of forecasts
- Time pressure — tight windows to produce LTB forecasts
- Data challenges — incomplete or distorted series (warranty effects, misclassifications)
- Documentation — every LTB quantity must be justified and auditable
What you’ll learn in this white paper
Data evaluation
How to validate time series, detect structural breaks and measure data quality.
PEM & Asymptotic models
Techniques to reconstruct production volumes when serial numbers are absent.
Wear-part modelling
Modeling age-dependent failure, fleet structure and multiple demand peaks.
Key insights
Objective forecasts
Move from gut-feel to reproducible, auditable forecasts.
Proven models
20+ years of domain-specific methods embedded in software.
Scalable
Batch forecasts for thousands of items with quality flags.
Wear-part example
Brake pads commonly show peak demand after specific mileage thresholds. Modeling age and usage prevents underestimates that cause runouts.
- Brake systems — mileage-driven peaks
- Batteries — age and climate effects
- Suspension parts — fleet-type dependency
Watch Webinar on miFLEETS
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