This study develops a conceptual framework integrating Lean Production, Value Stream Mapping (VSM), Industry 4.0 capability, and information quality to enhance production logistics integration and logistics performance in the automotive industry. The motivation of this study arises from the fragmented nature of existing literature, where Lean Production, digital transformation, and logistics optimization are often discussed separately without a unified conceptual structure. The automotive industry, characterized by high complexity and dynamic supply chain interactions, requires a more integrated approach to improve efficiency and responsiveness across production and logistics systems.The study employs a conceptual paper approach based on literature synthesis to identify key relationships among Lean Production, Value Stream Mapping, Industry 4.0 capability, information quality, production logistics integration, and logistics performance. Lean Production focuses on waste reduction and process efficiency, while Value Stream Mapping supports the visualization of material and information flows to identify inefficiencies. Industry 4.0 capability enhances real-time data integration and system connectivity, improving information quality and decision-making processes. These elements collectively contribute to strengthening production logistics integration as a central mechanism for improving logistics performance. The proposed conceptual framework suggests that Lean Production, Value Stream Mapping, Industry 4.0 capability, and information quality positively influence production logistics integration, which in turn enhances logistics performance. The integration of lean and digital capabilities provides a more comprehensive approach to managing material flow, information flow, and operational coordination in automotive manufacturing systems. This study contributes to the literature by providing a unified conceptual model that bridges lean manufacturing and Industry 4.0 in the context of logistics systems, offering a foundation for future empirical validation and industrial application.
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