Authors:
Laila Esheiba
;
Amal Elgammal
and
Mohamed E. El-Sharkawi
Affiliation:
Faculty of Computers and Information, Cairo University, Cairo and Egypt
Keyword(s):
Product-service Systems, PSS Customization, Recommender Systems, Big Data Analytics.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Data Engineering
;
Enterprise Information Systems
;
Health Information Systems
;
Information Systems Analysis and Specification
;
Knowledge Discovery and Information Retrieval
;
Knowledge Management
;
Knowledge-Based Systems
;
Ontologies and the Semantic Web
;
Society, e-Business and e-Government
;
Symbolic Systems
;
User Profiling and Recommender Systems
;
Web Information Systems and Technologies
Abstract:
Product-service systems (PSSs) are being revolutionized into smart, connected products, which changes the industrial and technological landscape and unlocks unprecedented opportunities. 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. This vision imposes a new technology stack to support the vision of smart, connected products and services. In a previous work, we have introduced a novel customization PSS lifecycle methodology with underpinning technological solutions that enable collaborative on-demand PSS customization, which supports companies to evolve their product-service offerings by transforming them into smart, connected products. This is enabled by the lifecycle through formalized knowledge-intensive structures and associated IT tools that
provide the basis for production actionable “intelligence” and a move toward more fact-based manufacturing decisions. This paper contributes by a recommendation framework that supports the different processes of the PSS lifecycle through analysing and identifying the recommendation capabilities needed to support and accelerate different lifecycle processes, while accommodating with different stakeholders’ perspectives. The paper analyses the challenges and opportunities of the identified recommendation capabilities, drawing a road-map for R&D in this direction.
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