Part Analytics using Digital Threads
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Abstract
Determining whether a component is Beyond Economical Repair (BER) depends on multiple technical and business factors, including repair cost thresholds, replacement/market value, component availability and lead time, persistency of faults (recurrence after repair), and the time required to restore operational readiness. This work evaluates a predictive, data-driven approach that uses a digital thread tying serialized parts to their host aircraft and leverages aircraft utilization forecasts (e.g., from FlightRadar24) to generate flight-hour-based usage forecasts. By combining usage forecasts with part-level reliability models and historical failure data, we produce one-year risk forecasts for part failure (remaining useful life / failure probability).
Enriching the digital thread with part attributes (repair cost, new-purchase cost, repair cycle time, vendor lead time) and part-family trend analytics enables a cost–benefit analysis of repair versus replacement that explicitly considers total cost of ownership and operational downtime. Applying configurable business rules and inventory state (on-hand, pipeline, critical spares) allows the system to recommend actions (repair, replace, or retire excess inventory) that minimize expected lifecycle costs and mission-impacting downtime. This paper presents the approach that supports proactive supply-chain and maintenance decisions, improves fleet availability, and identifies systemic part-family issues that may warrant engineering or supplier corrective action.
How to Cite
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Parts Analytics, Digital Threads, Beyond Economic Repair
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