A digital transformation strategy model for leadership in manufacturing: Considering the technological innovations to advance industry 5.0 in smart manufacturing

Purpose: This study investigates how manufacturing leaders can effectively navigate the transition from Industry 4.0 (I4.0) to Industry 5.0 (I5.0) by leveraging technological and data management advancements. It aims to bridge the digital transformation (DTx) gap and proposes a robust strategy model focused on human–machine interaction and sustainability. Design/methodology/approach: The study adopts a mixed-methods approach, combining a structured literature review with empirical insights from a quantitative survey of 2500 South African manufacturing leaders. The research applies statistical analysis and revises a seven-step DTx model to incorporate I5.0 technologies, including industrial artificial intelligence (IndAI) and human-centric smart manufacturing. Critical research factors are extracted from literature review and qualitative research. Most references are from 2020 onwards. Findings/results: Findings reveal critical success factors for successful Industry 5.0 migration, highlighting the importance of resilience, human–machine collaboration, sensor technology, connectivity infrastructure and the four-layered IndAI integration. The article introduces a multi-layered DTx strategy model adaptable to future industrial paradigms, including a nascent Industry 6.0. Practical implications: Manufacturing leaders can apply the refined DTx strategy model to enhance organisational readiness, technological agility and workforce alignment through ‘worker back in the loop’ considerations and ergonomics. The findings inform policies on upskilling, digital ethics and systems design for sustainable industrial growth. Originality/value: This article extends prior DTx frameworks by integrating emerging Industry 5.0 concepts and proposing a future-oriented strategy model. It contributes novel insights into human-centric design, IndAI governance and leadership challenges in digitally evolving manufacturing contexts.