Analytics is widely regarded as having the potential to generate significant productivity improvements in manufacturing by enabling innovation of processes, such as quality and maintenance. Despite this considerable potential, many manufacturers struggle to adopt analytics for process innovation and realize value, owing to the complexity of the adoption process and uncertainty regarding where and how to adopt the technology. This dissertation aims to improve understanding of process innovation with analytics and develop prescriptive knowledge to support manufacturers’ adoption. The focus has specifically been on how manufacturers can quickly explore and assess the potential of analytics for process innovation. Leveraging an empirical design-oriented approach, the research featured close collaboration with a large Danish manufacturer and retailer. Development of analytics systems prototypes and participative observation in analytics development initiatives were used to explore process innovation with analytics empirically and develop prescriptive knowledge. The results of the research consist of four main parts. First, a conceptualization of process innovation with analytics and an associated research agenda is proposed. Second, key factors influencing development and exploration speed are presented. Third, an approach and design principles for fast development of analytics systems prototypes aimed at process innovation are presented. Fourth, managerial recommendations are presented based on lessons learned from the research. Taken together, the research provides insights into process innovation with analytics, which can support manufacturing companies in their adoption efforts. From a research perspective, it contributes with knowledge at the intersection of analytics and process innovation and underlines the importance of digital infrastructure.