16 Apr 2019
How big data is influencing rotable part inventory and predictive analytics to minimise aircraft on ground situations. By analysing part failure rates and stock inventory operators can insure inventory is in the correct location and part failures are minimal.
Big data systematically analyses data to extract trends from an appointed number of very large data sets, and then processes the information with statistics. This has the potential to monitor behavioural patterns of components and systems and can be used to predict when and where an aircraft component will fail.
This will transform the management of rotable components from a reactive process to a proactive one. It will allow airlines and operators to mitigate costly aircraft-on-ground (AOG) situations and reduce aircraft downtime because a component change can be managed and planned before it fails. In addition, a higher level of part availability can be achieved without a disproportionately large quantity of stock having to be held.
Airlines and MROs plan their parts and component inventories at a particular level based on the probability of failures. High stock levels often have to be held at several locations to prevent protracted AOG situations. The aim is to balance most component failures without holding excessive stock. The amount of rotable stock held is carefully calculated to minimise the unavailability of needed parts. It is estimated, however, that more than 40% of rotable inventory is surplus. Many airlines and organisations hold inactive and obsolete inventory items that have depreciated and hold no useful value. There are several ways that big data analytics can reduce and optimise rotable inventory.
AJW provides component and rotable inventory support solutions. It harvests data from its customer base and logistic suppliers, has access to data from repair vendors, and can source reference material from its own supply facility, AJW Technique in Canada…
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