The automobile industry is one of the world’s largest manufacturing sectors and a key contributor to economic growth. Demand forecasting plays a critical role in supply chain management within the automotive sector. Reliable forecasts are essential for production planning, inventory control, and meeting market demands efficiently. However, accurately predicting demand remains a challenge due to the influence of external factors such as socioeconomic trends and weather conditions. This study presents a systematic literature review of the forecasting methods employed within the automotive industry, encompassing both vehicle and spare parts demand. Following PRISMA guidelines, 63 publications were identified and analyzed, covering traditional statistical models such as ARIMA and SARIMA, as well as state-of-the-art artificial intelligence approaches, including artificial neural networks. The review finds that classical statistical models remain prevalent for vehicle demand forecasting, Croston’s method dominates spare parts forecasting, and AI-based techniques increasingly outperform conventional models in recent studies. Furthermore, the review compiles a broad set of external variables influencing demand and highlights the common challenges associated with demand forecasting. It concludes by outlining potential directions for future research.

Google Ads is a powerful tool for companies wishing to gain visibility on Google searches, as it offers impression privileges for advertisers, by featuring the ad above the organic results listing. This chapter contributes to the optimization of Google Ads campaigns. It includes an empirical study with a sample of marketing professionals exploring their views on the challenges of Google Ads as a digital marketing tool. According to the participants in this study, Google Ads campaign profitability depends, largely, on the ability to choose a keyword pool that achieves the company's goals. Moreover, the complexity of these pay-per-click decisions, the costs involved, and its business implications demand more reasoned, quantified, and, if possible, optimized solutions. The chapter develops linear programming optimization models based on impressions, clicks, conversions, and billing. The models are tested on a real example using Excel optimization add-ins.