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A post-industrial revolution is encouraging the deployment of novel concepts both for designing smart factories and for creating a new generation of monitoring, control and man-machine collaboration systems. In general, companies are embracing an era of smart manufacturing built upon Cyber Physical Systems (CPS), the Internet of Things (IoT), and Cloud and Cognitive computing. Using digital technologies with advanced manufacturing tools can provide opportunities for building smart decision support systems (DSS) to improve manufacturing analysis, monitoring, output, and performance. Despite the potential of improved Decision Support Systems (DSS), the major challenge is successfully adapting smart manufacturing processes to use new digital technologies that can enable the implementation of Intelligent systems and improved DSS. Additionally, to move towards smart manufacturing, better means are required for technology deployments. The speed of technology implementation should be significantly faster, and machines should have greater accuracy of calibration in comparison to traditional manufacturing. One approach to enhancing deployment is incorporating optimization models into manufacturing systems. This change should provide a design that provides ease of use for operators and decision makers in real-time during the manufacturing process. This chapter defines requirements for various types of DSS (see Power, 2002 and 2004, for details on the typology of DSS) in a smart manufacturing environment based upon increased use of optimization. It focuses on identifying key barriers which prevent the development and use of enhanced or “smart” DSS in manufacturing and then provides the requirement and architecture for a system engineering design for using optimization and other techniques with advanced computing and manufacturing technologies. This review aims to promote a standard design or framework that is useful for both the manufacturing and academic communities that can facilitate needed efforts and innovation while stimulating adoption and use of smart manufacturing technologies.
IGI Global: International Publisher of Information Science and Technology Research
James Worth Bagley College of Engineering
Department of Industrial and Systems Engineering
Advanced Planning and Scheduling, Case-Based Reasoning, Enterprise Resource Planning, Manufacturing Execution System, Markov Decision Process, Order Penetration Point (OPP), Semantic Integration
Fathi, Mahdi; Khakifirooz, Marzieh; Pardalos, Panos M.; and Power, Daniel J., "Decision Support for Smart Manufacturing" (2019). Publications - James Worth Bagley College of Engineering. 9.