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Human-Centered Lean Transformation: Integrating Organizational Culture, Digital Capability, and Sustainable Performance Hermanto MZ; TOGAR P.O SIANIPAR; Nefo Alamsyah
JURNAL TEKNIK DAN SISTEM INDUSTRI Vol 4 No 01 (2026): Edisi Januari-Juni
Publisher : PROGRAM STUDI TEKNIK INDUSTRI UNIVERSITAS TRIDINANTI

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Abstract

The increasing complexity of manufacturing systems has shifted Lean Manufacturing from a tool-oriented approach toward a comprehensive organizational transformation strategy. However, previous studies have predominantly examined organizational, human, digital, and operational factors independently, resulting in a fragmented understanding of sustainable Lean transformation. This study aims to develop an Integrated Human-Centered Lean Transformation Framework by synthesizing the current body of knowledge on Lean Manufacturing. A Systematic Literature Review (SLR) combined with bibliometric analysis was conducted using 118 Scopus-indexed journal articles to identify research trends, thematic evolution, and conceptual relationships among key constructs. The findings reveal five interrelated dimensions that drive Lean transformation: Organizational Foundation, Human Capital Development, Digital Capability, Lean Transformation Process, and Strategic Performance Outcomes. The proposed framework highlights that successful Lean transformation is achieved through the sequential integration of these dimensions rather than through isolated implementation of Lean practices. This study contributes to literature by offering a comprehensive conceptual framework that bridges organizational readiness, human-centered development, digital enablement, and Lean implementation within a single transformation pathway. From a practical perspective, the framework provides strategic guidance for manufacturing organizations seeking sustainable operational excellence. Nevertheless, this study is limited to Scopus-indexed publications and has not empirically validated the proposed relationships. Future research should examine the framework using quantitative or mixed method approaches across different manufacturing contexts to assess its generalizability and predictive capability.