# Automotive OEM Demand Forecasting: A Comparative Study of Forecasting Algorithms and Strategies
## 3 Methodology
### Business Understanding  
The automotive industry accounts for one of the largest economies in the world, by revenue [40]. It is also considered a strong employment multiplier, a characteristic that is expected to grow stronger with the incorporation of complex digital technologies and the fusion with the digital industry [41]. Environmental concerns have prompted multiple policies and agreements, which foster the development of more environment friendly vehicles and rethinking of current mobility paradigms [42; 43; 44]. Nevertheless, global vehicle sales and automotive revenue are expected to continue to grow in the future [45; 46].  
Demand forecasting is a critical component to supply chain management as its outcomes directly affect the supply chain and manufacturing plant organization. In our specific case, demand forecasts for the automotive industry engine components worldwide were required on a monthly level, six weeks in advance. In Section 2, we highlighted related work, data, and techniques used by authors in the automotive industry. On top of data sources suggested in the literature for deriving machine learning features (past demand data, GDP, unemployment rates, and oil price), we incorporated three additional data sources based on experts' experience: Purchasing Managers' Index (PMI), copper prices, and sales plans.  
PMI is a diffusion index obtained from monthly surveys sent to purchasing managers from multiple manufacturing companies. It summarizes expectations regarding whether the market will expand, contract, or stay the same and how strong the growth or contraction will be.  
Prices of the products we forecast are tied to copper price variations used to manufacture them. Therefore, we consider the price of this metal and create derivative features to capture how it influences the products' price and how it may influence it in the future.  
The strategic sales department creates sales plans on a yearly and quarterly basis. Experts consider projected sales to be a good proxy of future demand as they inform buyers' purchase intentions. We found research that backs their claim (see, e.g., in [47]), showing that purchase intentions contribute to the forecast's accuracy. The research done in [48] shows that purchase intentions are good predictors of future demand for durable products and that this accuracy is higher for short time horizons. Research also shows that the purchase intention bias can be adjusted with past sales data.  
Much research was performed on the effect of aggregation on time series [34; 49; 50; 51], showing that a higher aggregation improves forecast results. Though research shows optimal demand aggregation levels exist [52], we considered forecasts at a monthly level to reflect business requirements specific to our use case.  
To understand demand forecasting models' desired behavior and performance, we consulted industry experts. They agreed that one-third of demand forecasts produced by planners have up to 30% error, and up to 20% forecasts may have more than 90% error.  
They also pointed out that 40% of all forecasts result in under-estimates. When issuing a forecast, it is more desirable to have over-estimates than under-estimates. We consider these facts to assess the forecast results.  
We address demand analysis in detail in Section 3.2.