Abstract 

The steel industry in Bangladesh plays a critical role in driving national infrastructure, shipbuilding, and construction projects. Yet, the sector faces persistent challenges, including operational inefficiencies, frequent unplanned downtime, waste generation, and dependence on conventional practices. This paper introduces a Lean-based Digital Twin (DT) framework designed to address these barriers by integrating real-time simulation and predictive analytics with Lean Manufacturing principles. The proposed model leverages industrial IoT data, process simulations, and predictive maintenance to reduce unplanned stoppages, enhance machine availability, and optimize production efficiency. By embedding Lean tools such as Value Stream Mapping and Kaizen within the DT environment, the framework ensures systematic waste elimination and continuous process improvement. However, challenges such as high initial investment costs, infrastructure limitations, and skill development gaps must be addressed for effective implementation. The following study presents a scalable, technology-driven solution to address existing challenges and foster sustainable growth and competitiveness in Bangladesh’s heavy industries. Overall, the framework offers a scalable, technology-driven solution that supports sustainable growth and strengthens the global competitiveness of Bangladesh’s heavy industries, while aligning with the broader vision of industrial digital transformation.

Keywords

Digital Twin, Lean Manufacturing, Predictive Maintenance, Process Improvement, Steel Industry.