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Integrating social media and warranty data for fault identification in the cyber ecosystem: A cloud-based collaborative framework

Integrating social media and warranty data for fault identification in the cyber ecosystem: A cloud-based collaborative framework

Ali, Syed Imran, Habib, Farooq, Ali, Abdilahi, Ali, Abdul ORCID: 0000-0002-3034-8628, Khan, Murtaza F. and Jamal, Arshad (2020) Integrating social media and warranty data for fault identification in the cyber ecosystem: A cloud-based collaborative framework. In: Jamal, Arshad, Hagan, Daniel, Bowen, Gordon, Jahankhani, Hamid and O’Dell, Liam M., (eds.) Strategy, Leadership, and AI in the Cyber Ecosystem: The Role of Digital Societies in Information Governance and Decision Making. Elsevier Academic Press, Cambridge, Massachusetts, Amsterdam, pp. 41-70. ISBN 9780128214428 ; 9780128214596, 0128214597 (doi:https://doi.org/10.1016/B978-0-12-821442-8.00012-4)

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Abstract

Fault identification during warranty is quite complex because of sophisticated product design and distributed manufacturing. Various supply chain facilities located at diverse geographical locations are usually utilised to manufacture a particular product. If a fault occurs in one component of a product, it may be linked with other components which are procured and manufactured by other segments of the globally distributed supply chain. Hence, in this multifaceted scenario, the information systems have to be integrated and responsive enough to respond proactively in sharing data from heterogeneous systems across the supply chain in the cyber ecosystem. To achieve this goal, in this chapter, we integrate warranty data from multiple datasets. Initially, social media dataset is used. Consumers increasingly engage in information sharing on weblogs, forums, Facebook, and Twitter, among others. This valuable information is mostly untapped by the automotive manufacturers. To explore the large amount of hidden fault-related data, we used data analytics. Then, we develop a cloud-based collaborative framework to manage the warranty data from other supply chain information systems, namely, design, manufacture and service. The framework provides integration and access of warranty data from multiple datasets of supply chain. The proposed ‘autonomous smart agents’ interaction assists to establish real-time warranty data exchange across the supply chain. The combined data can then be used for detailed expert analysis by fault learning and rectification agent. The execution of the framework is demonstrated using an illustrative execution process. Our contributions are clearly detailed, and some important managerial insights are provided for warranty management in globally distributed supply chain.

Item Type: Book Section
Uncontrolled Keywords: Cloud computing technology (CCT), product lifecycle management (PLM), social media, content analysis, sentiment analysis and warranty management
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / Department / Research Group: Faculty of Business
Faculty of Business > Department of Systems Management & Strategy
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Last Modified: 25 Aug 2021 09:22
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: None
URI: http://gala.gre.ac.uk/id/eprint/33427

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