# Automotive OEM Demand Forecasting: A Comparative Study of Forecasting Algorithms and Strategies
## 2 Related Work  
Products' demand forecasting is a broad topic addressed by many authors in the scientific literature. Different demand patterns require specific approaches to address their characteristics. Multiple authors proposed demand classification schemas to understand which techniques are appropriate for a particular demand type. For example, the work in [15] focused on demand variance during lead times, while the work in [16] introduced the concept of average demand interval (ADI), which was later widely adopted. \[ADI=\frac{\textit{Total Periods}}{\textit{Total Demand Buckets}} \tag{1}\]  
The work in [12] complemented this view of demand introducing the coefficient of variation (CV). Both concepts allow us to divide demand types into quadrants, classifying them as intermittent, lumpy, smooth, and erratic demands (see Figure 1). Smooth and intermittent demand have little variability in demand quantities. Smooth demand has little variability regarding demand intervals over time, while intermittent demand displays a higher demand interval variability. Erratic and lumpy demands have higher variability in demand quantities, which comprehends an additional forecasting model challenge. Erratic demand has little variability regarding demand intervals over time. In lumpy ones, this is an essential factor to be considered. Following demand types proposed in [12], in this work, we focus on smooth and erratic demands. \[CV=\frac{\textit{Demand Standard Deviation}}{\textit{Demand Mean}} \tag{2}\]  
Planners, who regularly create demand forecasts, must understand the products they sell, the market they target, the economic context, and customer expectations. Over time they learn how buyers behave, the vast array of factors that can influence product demand, and create their estimates. Each planner can weigh different factors and have distinct ways to ponder them. Most of this information can be collected and fed to machine learning (ML) models, which learn how demand behaves over time to provide a forecast. In the scientific literature addressing demand forecasting in the automotive industry, most authors do not use only demand data, but also incorporate data regarding exogenous factors that influence demand, such as the effect of personal income on car ownership [17; 18], or the effect of the GDP [5; 17; 19; 20], inflation rate [18; 19], unemployment rate [5], population density[20], and fuel prices [5; 18; 20; 21; 22] on vehicles demand. Figure 1: Demand types classification by [PERSON] et al. [12]. Quadrants correspond to (**I**) intermittent, (**II**) lumpy, (**III**) smooth, and (**IV**) erratic demand types. Demand distributions can also be considered as another source of information for demand forecasting: research performed by many authors confirmed a relationship exists between demand types and demand distributions [23; 24; 25; 26]. A wide range of models was explored in the literature addressing car, and car components demand forecasting. [PERSON]. [5] developed a custom additive forecasting model with seasonal, trend, and calendar components. The authors used a phase average to compute the seasonal component, experimented with Multiple Linear Regression (MLR) and Support Vector Machine (SVM) for trend estimation, and used a Linear Regression to estimate the number of working days within a single forecasting period (calendar component). The models were built with car sales data from Germany, obtaining the best results when estimating trends with an SVM model and providing forecasts quarterly. Models' performance was measured using Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) metrics. Ref. [18] compared three models: an adaptive network-based fuzzy inference system (ANFIS), an autoregressive integrated moving average model (ARIMA), and an artificial neural network (ANN). The forecasting models were built considering new automobile monthly sales in Taiwan, obtaining the best results with the ANFIS model. Ref.