A framework for data-driven decision making in advanced manufacturing systems: Development and implementation

Abstract

Integration of sophisticated technologies such as Internet of Things, cyber physical systems and big data analytics have revolutionized the advanced manufacturing systems (AMS). However, implementation of data-driven decision making in AMS still remains challenging due to data heterogeneity, real-time processing demands, and integration complexities. This paper overcomes this challenge by presenting a novel framework for adoption of DDDM in AMS to enhance its decision-making capabilities. This framework consists of six stages: manufacturing stage, sensing stage, data stage, knowledge stage, decision stage, and application stage. The proposed framework leverages big data analytics to extract actionable insights from diverse datasets, integrates CPS to create a seamless interaction between physical and digital systems, and employs IoT technologies for real-time data acquisition and monitoring. The framework is validated through a comprehensive case study involving a CNC milling machine dataset, demonstrating significant improvements in operational efficiency, decision accuracy, and response time. The case study involves detailed data collection steps, preprocessing, and analysis, showcasing the framework’s practical implementation and effectiveness. The results show that the proposed framework addresses existing challenges and provides a scalable solution for DDDM implementation in AMS.