Authors:
Michalis Pingos
1
;
Amal Elgammal
2
;
Indika Kumara
3
;
Panayiotis Christodoulou
1
;
Mike P. Papazoglou
3
and
Andreas S. Andreou
1
Affiliations:
1
Department of Computer Engineering and Informatics, Cyprus University of Technology, Limassol and Cyprus
;
2
Department of Computers & Information, Cairo University and Egypt
;
3
Department of Economics and Management, Tilburg University and Netherlands
Keyword(s):
Product-Service Systems, Smart Product Customization, Smart Shop-floor Monitoring, M2M, H2M.
Related
Ontology
Subjects/Areas/Topics:
Agents
;
Artificial Intelligence
;
Bioinformatics
;
Biomedical Engineering
;
Data Communication Networking
;
Enterprise Information Systems
;
Information Systems Analysis and Specification
;
Internet of Things
;
Methodologies and Technologies
;
Model Driven Architectures and Engineering
;
Operational Research
;
Sensor Networks
;
Simulation
;
Software Agents and Internet Computing
;
Software and Architectures
;
Software Engineering
;
Telecommunications
;
Tools, Techniques and Methodologies for System Development
Abstract:
Nowadays, Product-Service-Systems (PSS) are being modernized into smart connected products that target to transform the industrial scenery and unlock unique prospects. This concept enforces a new technological heap and lifecycle models to support smart connected products. The intelligence that smart, connected products embed paves the way for more sophisticated data gathering and analytics capabilities ushering in tandem a new era of smarter supply and production chains, smarter production processes, and even end-to-end connected manufacturing ecosystems. The main contribution of this paper is a smart shop-floor monitoring framework and underpinning technological solutions, which enables the proactive identification and resolution of shop-floor distributions. The proposed monitoring framework is based on the synergy between the novel concept of Manufacturing Blueprints and Complex Event Processing (CEP) technologies, while it encompasses a middleware layer that enables loose coupling
and adaptation in practice. The framework provides the basis for actionable PSS and production “intelligence” and facilitates a shift toward more fact-based manufacturing decisions. Implementation and validation of the proposed framework is performed through a real-world case study which demonstrates its applicability, and assesses the usability and efficiency of the proposed solutions.
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