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. In order 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 viz. 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.
|Title of host publication||Strategy, Leadership, and AI in the Cyber Ecosystem|
|Subtitle of host publication||The Role of Digital Societies in Information Governance and Decision Making|
|Editors||Hamid Jahankhani, Liam M. O'Dell, Gordon Bowen, Daniel Hagan, Arshad Jamal|
|Place of Publication||London|
|Publisher||Elsevier Academic Press|
|Number of pages||27|
|Publication status||Published - 13 Nov 2020|