Publication Details
Abstract
The research aims to designing a multi-objective straight-line programming model to reduce the product life cycle (PLC) costs utilization big data analytics and robotic procedure automation (RPA), and to study its influence on product sustainability and customer satisfaction. Advancements in artificial intelligence, big data analytics, and robotic process mechanization are causing profound changes in the manufacturing sector in the digital era. Industrial companies are urged to adopt intelligent analytical models to improve operational performance and save money, while maintaining customer satisfaction and product sustainability as a result of these developments. The aim of this research is to develop a linear programming model that combines multiple objectives to reduce the total cost related with the product's lifecycle. Big data analytics and robotic process automation tools are integrated in the model, which examines their combined impact on product sustainability and customer satisfaction. Real-world data collected from the General Electric's Systems Company was used to employ an applied research methodology. Production expenses, maintenance expenditures, even upgrades, customer satisfaction metrics, and environmental impact indicators are all part of the dataset. Big data analysis techniques were used to extract key performance indicators affecting decision-making processes, while robotic process automation (RPA) technology was employed to link the mathematical model to the business's actual operational systems, enabling real-time and continuous data updates without human intervention. Three primary objective functions are the focus of the mathematical model. A weighted sum approach was employed to solve the model, resulting in a balanced solution to conflicting objectives. To achieve comprehensive and sustainable operational performance, the study suggests expanding this modeling approach to include other industrial units.