Recent research initiatives in the domain of business process management such as process intelligence, monitoring, and mining have shown significant results in automated process environments. However, such techniques fall short to provide efficient solutions and support for non-fully automated business processes i.e., processes that embody dynamic, continuous, and manual activities such as in logistics. More precisely, things turn to be very challenging when it comes to the monitoring and Service Level Agreement (SLA) violation prediction throughout context-dependent and manual processes. Unlike current approaches that mainly focus on the model of the process as a whole, we shift in this work toward instance-based and specific processing for each activity depending on its context. We showcase a contextualized template-driven framework while providing the missing link of continuous monitoring and early prediction within manual activities.