‘BETTER SIDE BY SIDE’: An Integrative Framework Between Lean Manufacturing and Circular Economy

ABSTRACT The integration of Lean Manufacturing (LM) and the Circular Economy (CE) represents a promising strategy to foster more sustainable production systems aligned with the principles of cleaner production. This study conducts an integrative systematic literature review, based on the PRISMA protocol, with the aim of mapping, organizing, and consolidating existing knowledge on the convergence between LM and CE. A total of 51 articles indexed in the Scopus and Web of Science databases were analyzed. The results reveal that lean practices support the adoption of circular principles by promoting waste elimination, resource efficiency, and environmental impact reduction, with positive effects on organizations' operational, economic, and environmental performance. However, significant gaps remain, particularly concerning the scarcity of empirical studies, the limited diversity of geographic and sectorial contexts, and the lack of assessments regarding the social and organizational impacts of LM–CE integration. In response to these gaps, the study presents 10 recommendations to guide future research and practice, including the adoption of mixed‐methods approaches, the exploration of under‐investigated sectors (such as agriculture, construction, remanufacturing, and textiles), and the incorporation of socio‐environmental performance metrics. Moreover, this study proposes, innovatively, an integrative conceptual framework grounded in the Resource‐Based View (RBV), which positions the LM–CE synergy as a strategic, valuable, and hard‐to‐imitate organizational capability, capable of generating sustainable competitive advantage. The proposed model offers theoretical contributions by expanding the understanding of sustainable value creation mechanisms and provides relevant managerial and practical implications, offering robust guidance for managers, policymakers, and researchers committed to the transition toward circular production models.