In manufacturing systems, early quality prediction enables the execution of corrective measures as early as possible in the production chain, avoiding thus costly rework and waste of resources. The increasing development of Smart Factory Sensors and Industrial Internet of Things has offered wide opportunities for applying data-driven approaches for early quality prediction in real-time using Machine Learning (ML). With the multiplication of applications, further requirements on the quality of predictive ML models covering multiple aspects such as accuracy, robustness and explainability have to be fulfilled to build trustworthy ML-based solutions. In this context, we investigate the task of early quality classification using a Convolutional Neural Network (CNN) on time series sensor data of an automotive real-world case study. To do so, a gradient-based heat-mapping explanation method for CNNs is computed to determine the most discriminative time series patterns and localize them in time. These patterns are subsequently used to achieve quality prediction in real-time as early as possible.