IAV Hirundo - Probabilistic Lifetime Prediction provides actionable insights into your fleet such as

Prioritize field issues with the highest projected future costs.

Perform a highly sophisticated risk assessment in case of recall.

Optimize pre-production of spare parts for vehicle fleet.

Our solution

Probabilistic approach with inherent accuracy assessment. This allows to quantify the uncertainty of the forecast.

Low budget risk. Our Software as a service scales with your needs.

Widely applicable: Only commonly available data like age index and date from fleet and failure parts are required.

Full automation from data cleansing to model selection.

Background

Warranty costs represent a significant expense, which can reach billions in the automotive industry. A significant fraction of these costs arise from the storage of parts that are either never needed or stored in insufficient quantities, leading to costly late-stage reproduction. A data-driven lifespan analysis that incorporates early data points into the durability forecast of key parts is crucial for reducing these costs.

Traditionally, lifespan analyses are conducted by fitting a single Weibull distribution to failure data using regression methods. Commonly, the estimates are point estimations without considering their uncertainty. As a result, the quality of the forecast is highly dependent on the procedure and the individual experience of the user.

Our novel approach models multiple failure mechanisms by estimating a weighted superposition of Weibull distributions, including their expected uncertainties. The optimal number of components and their training are determined with a probability-based method (EM Algorithm). The expected uncertainties are estimated with bootstrapping.