## 1 Introduction  
Safety is the most desired feature for aviation, and safety critical spare parts hold special importance for aviation organizations. Forecasting future consumption of safety-related spare parts is the most critical part of inventory management, as its inaccurate prediction poses a serious challenge for the organizations responsible for the maintenance of aviation fleets [1]. Keeping in view efficient spare parts management and decreasing maintenance budget, possession of a reasonable inventory level is critical in the aviation industry, where lead time does not always satisfy the actual demand due to which spare parts pile up in the warehouse. Figure 1 provides an example of a sample depot that is responsible for the storage of spares and for the maintenance of helicopters and fixed-wingaircraft from four different origins. The depot stores around 97,693 spares, including 18,930 time change components (TBOs) that are categorized as slow-moving spares. These spares are expensive and are replaced based on either operational time or calendar years. The depot maintains two types of spares--fast-moving spares (selective stock list (SSL) and expandable spares) and slow-moving spares (TBOs, and non-selective stock list (nonSSL)). The SSL items are stocked for one quarter based on the demand/consumption of the previous four quarters. The TBOs, being critical and costly items, are stored for the next five years, based on the last two years of consumption data from maintenance setups. NonSSL and expandable items are stored for four quarters based on projections. The problem of modeling future consumption is further aggravated by a lumpy spare part demand pattern [2; 3], marked by periods with no demand along with sporadic demand [4; 5], as shown in Figure 2.  
The continuous inspections and preventive maintenance render the aircraft unserviceable for operational requirements, which costs the maintenance organizations. Therefore, the topmost priority for small and medium enterprises is to make the right spare parts available at the need of the hour and at the required location. The increasing downtime of the aircraft can be managed with efficient forecasting to have better operational fleet performance, which is a less researched domain in the aviation sector and warrants investigation. In this connection, the system experts [6; 7] have utilized the statistical techniques  
Figure 1: A sample spare depot storage template. Figure 2: Lumpy demand forecasting graphical representation. of exponential smoothing and regression analysis [8; 9], but such approaches are found to perform inaccurately when a lumpy demand pattern is processed [10]. Another interesting approach presents an innovative model for a single site to take advantage of a distribution, the zero-inflated Poisson. The authors demonstrate the model effectiveness, confirming that their approach outperforms the traditional Poisson-based approach [11]. Different methods, with varying degrees of success, have been used to forecast lumpy and intermittent demand data. These techniques comprise a variety of models, such as Holt-Winters [12] as the statistical method. Similarly, machine learning methods such as the support vector regression (SVR) [13] are used, while long short-term memory (LSTM) networks [14] are used as a deep learning method. However, it is still debatable which technique or meta-level approach predicts lumpy and intermittent demand data most accurately. This is due to a lack of historical research on intermittent and lumpy time series data [15]. In recent years, machine learning and deep learning research has also advanced rapidly. Among these, artificial neural networks (ANN) are considered the most recognized artificial intelligence (AI) method [16] used to handle lumpy demand patterns because they have outperformed the traditional techniques in several fields [17; 18; 19; 20]. Owing to the added advantage of performing in the way the human brain works by acquiring training from historical demand data, such models perform superbly.