Integration of Big Data and Business Analytics in Lean Manufacturing: A Strategic Approach

Dependence on antiquated data-gathering techniques and reactive problem-solving methods in contemporary lean manufacturing frequently results in inefficiencies like higher downtime and less-than-ideal decision-making. To systematically solve these issues, the research suggests combining business analytics with big data in a strategic way. The proposed system makes use of sensors, IoT devices, and advanced analytics to ensure real-time data flow throughout production environments. Machine learning (ML) algorithms improve efficiency and agility by enabling operational insights and predictive maintenance. The implementation roadmap calls for careful data preparation, cloud-based data integration, and data collecting using strategically positioned IoT sensors. Predictive skills are further improved through the selection and optimization of ML models. Proactive decision-making is made possible by real-time data, which also allows for quick adjustments to maximize output. Continuous system improvement and adaptation are ensured by feedback loops and continuous monitoring. When compared to traditional approaches, the comparative study shows considerable improvements in production efficiency (up to 35%), downtime reduction (45%), predictive maintenance accuracy (92%), and resource utilization (28% increase). With such a combination, lean manufacturing undergoes an evolutionary shift and provides a scalable and effective solution to satisfy contemporary industrial demands.