International Iberoamerican University (UNIB) researcher Ernesto Bautista Thompson, together with other researchers, proposes an artificial neural network (ANN) model to accurately predict the parts of an aircraft that need to be replaced.
Safety is the most important feature for aviation organizations. However, they face difficulties in accurately forecasting the parts requiring spare parts when data is irregular or inaccurate.
It is also difficult to accurately forecast the safety-related spare parts needed in the future; this is the most complex part of inventory management. This represents a challenge for organizations responsible for the maintenance of aviation fleets. This issue is the least researched in the field of aviation and needs to be studied.
Given the above, this research proposes an artificial neural network (ANN) model to accurately predict faulty parts. The model aims is to ensure that small and large aviation companies have the right spare parts available at the right time and in the right place.
Since demand for spare parts is irregular and intermittent, it is difficult to establish a pattern of demand.
Results
This research has a very important role to play in forecasting the demand for aviation spare parts through a new and efficient way of forecasting demand. ANN has been determined to be the best forecasting technique for handling and inferring future irregular demands.
In summary, this model takes into consideration the quantity demanded instead of the quantity issued. The model also takes into account previous information that indicates a lack of spare parts in the warehouse to avoid shortages and assist future demands for spare parts. This method also forecasts irregular periods of spare part demands, and most importantly, detects parts that need to be replaced.
It also contributes to aviation safety by reducing the number of flights with faulty components. Accurate prediction of critical and expensive spare parts is expected to help companies save costs, minimize downtime and optimize the readiness of drones, fixed-wing aircraft and helicopters.
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