Process mining is a discipline positioned between business process management and data mining. It applies algorithms on real event data extracted from information systems that support business processes, to construct as-is process models, and improve them automatically. The benefits can be versatile, from gaining insight into the real execution of a process, to detecting process bottlenecks, activity loops, or social networks of process resources. Several literature reviews have focused on the application of process mining in the healthcare industry and on process mining discipline in general, without the reviews of other application domains. This paper presents the results of a systematic literature review on case studies of process mining projects applied in the manufacturing industry. Case studies are analyzed according to the following aspects: project goals, information systems or devices/equipment that generate event data, particular business processes, event log characteristics, different types and perspectives of process mining performed, tools and techniques used for preprocessing activities, discovery, conformance checking, process enhancement, and social network analysis. Finally, an attempt is made to discover the impact of goals, types of processes, and event log characteristics on the selection of process mining types, perspectives, tools, and techniques.