The phenomenon of frugal innovation gains increasing attention among practitioners and academics. However, the literature on frugal innovation is still in its infancy, lacking theoretically grounded studies to explore why and how multinational corporations (MNCs) apply this innovation approach. We propose that frugal innovation holds cross-departmental and cross-market learning potential for multinational corporations. The organizational learning theory offers a fruitful perspective to investigate the underlying motivation of MNCs to engage in frugal innovation and to examine how frugal innovation can be implemented in firms’ global operations. Given the scarce empirical literature on frugal innovation, we decided to apply an explorative research agenda. As the healthcare industry is a vital breeding ground for frugal innovation, we gain in-depth insights into frugal innovation through a multi-case study of Siemens Healthineers projects. From our case study, we develop a comprehensive framework to depict the factors that drive and hinder MNCs’ engagement in frugal innovation from an internal as well as an external perspective. Furthermore, we suggest a five-step approach to align frugal innovation with traditional innovation in MNCs’ innovation strategy. Our approach includes full management support (Step 1), carving-out a frugal innovation satellite from the organization (Step 2), establishing of corresponding interfaces (Step 3), feeding-back information from the corporate level to the frugal innovation team (Step 4), and feeding-forward best practices and innovations from the frugal innovation team to the corporate level (Step 5). This study provides valuable implications for practitioners in MNCs, offering a hands-on approach to leverage the benefits of frugal innovation while exploiting traditional innovation know-how. Additionally, our research fosters academic understanding of the underlying motivations of MNCs to engage in frugal innovation. Our findings open promising avenues for further research, including longitudinal qualitative and large-scale quantitative investigations.