Authors: Laila Esheiba ; Amal Elgammal and Mohamed El-Sharkawi

Affiliation: Faculty of Computers and Information, Cairo University, Cairo and Egypt

ISBN: 978-989-758-372-8

Keyword(s): Product-service Systems, PSS Customization, Recommender Systems, Big Data Analytics.

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. (More)

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Paper citation in several formats:
Esheiba, Laila; Elgammal, A. and El-Sharkawi, M. (2019). Recommendation Framework for on-Demand Smart Product Customization.In Proceedings of the 21st International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-758-372-8, pages 177-187. DOI: 10.5220/0007684401770187

author={Esheiba, Laila and Amal Elgammal. and Mohamed E. El{-}Sharkawi.},
title={Recommendation Framework for on-Demand Smart Product Customization},
booktitle={Proceedings of the 21st International Conference on Enterprise Information Systems - Volume 2: ICEIS,},


JO - Proceedings of the 21st International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - Recommendation Framework for on-Demand Smart Product Customization
SN - 978-989-758-372-8
AU - Esheiba, Laila
AU - Elgammal, A.
AU - El-Sharkawi, M.
PY - 2019
SP - 177
EP - 187
DO - 10.5220/0007684401770187

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