# Automotive OEM Demand Forecasting: A Comparative Study of Forecasting Algorithms and Strategies
## 3 Methodology
### Data Understanding  
We make use of several data sources when forming features for machine learning, described in Table 1. We distinguish between internal data sources (non publicly available data regarding a manufacturing company provided by that same company) and external data sources used for the data enrichment process. When performing preliminary data analysis over the seven years of data, we found that GDP, crude oil prices, PMI values, and demand (see Figure 2) show a different pattern before and after the _year 4_ of our dataset. When searching for possible root causes, we observed that in _year 4_ some significant economic and political events took place affecting the economy worldwide. Among them, we found a stock market crash of a relevant country, a decrease in crude oil production, and several political events that affected the market prospects. We consider demand quantity as the executed orders of a given product leaving the manufacturing plant on a specific date. Even though demand data is available daily, we aggregate them monthly, satisfying business requirements and providing smoother curves and ease of forecasting. We also consider that months have different working days (due to weekdays and holidays). Thus, we computed the average demand per working day for each month. Future demand can be estimated using the average demand per working day, multiplying by the number of working days in the target month. Based on the demand classification in [12], we analyzed how many products correspond to erratic and smooth demands. We create features to capture this behavior. We present the products demand segmentation in Table 2. From the works in [23; 24; 26; 53], we understand that demands of a given type follow a certain distribution. Thus, most manufacturing companies' products may have a slightly different demand behavior but share enough characteristics that would reflect common patterns. We observed that demand values for each product follow a geometric distribution. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data** & **Source** & **Periodicity** & **Aggregation Level** \\ \hline History of deliveries & Internal & D & D \\ \hline Sales Plan & Internal & Y,Q & M \\ \hline Gross Domestic Product (GDP) & World Bank & Y & Y \\ \hline Unemployment rate & World Bank & Y & Y \\ \hline Crude Oil price & World Bank & M & M \\ \hline Purchasing Managers’ Index (PMI) & Institute of Supply Chain Management & M & M \\ \hline Copper price & London Metal Exchange & D & D \\ \hline Car sales & International Organization of & Y & Y \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Data sources. In the first and second columns, we indicate the kind of data we retrieve and its source. The third column provides information on how frequently new data is available. In contrast, the last column describes the aggregation level at which the data is published. Periodicity and aggregation levels can be at a yearly, quarterly, monthly, or daily level and are denoted by “Y”, “Q”, “M”, or “D”, respectively. The London Metal Exchange published copper prices for weekdays. To discover potential patterns, we made use of different visualizations. Demand seasonality was assessed with correlograms (see Figure 3), which show what lags in time most frequently display a statistically significant correlation (with a \(p\)-value = 0.05). Considering all data available, we found the strongest correlations for products at three, four, five, eight, and eleven months before the target month. However, we observed a different pattern in the last three years of data: the strongest correlations occurred at eight, ten, and eleven months before the target. Therefore, we choose only those statistically significant when analyzing correlation values, considering a confidence interval of 95%. Plotting products' monthly demand for every year, as shown in Figure 4, we found that most products were likely to behave similarly over the years for a given month.