John Croston was the first to introduce a method that was designed specifically for intermittent data [21]. Croston proposed separating the data into two series: one for arrival times and another for positive demand. Croston noted, however, that the time series data for intermittent demand varied significantly from that of conventional time series data. The former have multiple zero-demand intervals that are different from the latter. He presented an alternative technique to forecast demand from intermittent time series data. Croston stated that his method that presupposes independence between demand size and demand intervals. However, Willemain et al. [22] casted doubt on this concept of autonomy. This, however, was maintained in subsequent work that improved Croston’s original method, such as Syntetos and Boylan [23]. Syntetos and Boylan investigated Croston’s method and found it to be biased. Later, they presented a modified version of Croston’s method to resolve the bias issue [24].

Leve’n and Segerstedt proposed a modification of Croston’s technique that attempts to eliminate its inherent bias [8]. Nevertheless, the Leve’n and Segerstedt method is more biased than the Croston method, particularly for highly intermittent series. Willemain et al. identified patterns of intermittent demand in several other scenarios such as heavy machinery, electronics, maritime spare parts, etc. [25]. Similarly, Syntetos and Boylan studied intermittency in automotive spare parts [26]. Ghobbar and Friend analyzed the demand for costly aircraft maintenance parts [1]. They observed that businesses were holding too much inventory due to inaccurate demand forecasts, resulting in subpar service levels. Whether the demand is large or small or arrives at the correct or incorrect time, both can lead to mistakes. Therefore, accurate demand projections are necessary to support inventory holding and replenishment decisions. In addition, as a result of shortened life cycles, technology migrations, time for lengthy production cycles, and protracted lead times for capacity expansion, electronic companies face complexity in inventory management and risk of excess supply and shortfall of important components [27].

The work of [28] performs intermittent prediction on data collected from the Internet of things (IoT) using a recurrent neural network (RNN). The performance evaluation metric is accuracy. In this specific prediction task, RNNs outperformed ANNs. As an alternative to the Croston method, a new method based on stochastic simulation is used to conduct research in [29]. Different evaluation metrics are used such as mean error (ME), mean absolute deviation (MAD), MASE, and D proposed by the authors. In conclusion, the proposed method did not outperform the existing standard. In [30], the ATA technique was compared with the Croston technique. The data from the M4 competition were utilized. The ATA and the exponential smoothing methods are similar, but their respective emphases differ. The study predicted six future time steps. As evaluation metrics, both mean squared error and standardized mean absolute percentage error are utilized. As a metric for out-of-sample evaluation, the ATA method is superior to the Croston method. In [31], a novel method, the modified SBA, is presented for intermittent forecasting. In conclusion, the proposed method can not compete with the current method. The authors of [32] employ a deep neural network (DNN) to predict sensor data. The ARIMA and generalized autoregressive conditional heteroskedasticity (GARCH) techniques have been surpassed by this method. It utilized simulations that require a well-considered parameter design for this purpose. Ref. [33] provides an example of a simulation design that investigates multiple parameter combinations. The study results demonstrate promising outcomes.

A study in [34] introduces a seasonal adjustment method and a dynamic neural network as the primary seasonal forecasting model. Zero demand is counteracted by preprocessing the initial input data and by adding input nodes to the neural network. It proposed a revised error measurement method for evaluating performance. The proposed framework for forecasting outperforms competing models for intermittent demand. Two ANN models, each using 36 observations as training data, were proposed in [35]. The proposed model was able to achieve competitive inventory performance relative to Croston despite low forecasting accuracy. In [36], an ANN and RNN are trained and compared for intermittent demand forecasting. The results show that ANN performance is superior to that of RNN in long-term demand series. Another work in [37] conducts an empirical analysis utilizing 5133 SKUs from an airline and acquired forecast performance and inventory performance outcomes for different methodologies. The findings indicate that proposed ANN approaches are less biased as compared to other evaluated methods.

Table 1 summarizes different datasets having different patterns including intermittent, lumpy, and smooth. It also shows the industry related to each dataset. The forecasting methods used in the referenced article and its evaluated metrics are also included. From the table, it can be observed that the performance of various methods varies with the input datasets.