A graph-based requirement elicitation approach in the context of smart product-service systems
The trend of servitization has promoted the prosperity of product-service systems (PSS), by generating integrated solution bundles with more values for both industrial companies and customers. Nowadays, with the advances of Information and Communication Technology (ICT) and artificial intelligence (...
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| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2020
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/144993 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | The trend of servitization has promoted the prosperity of product-service systems (PSS), by generating integrated solution bundles with more values for both industrial companies and customers. Nowadays, with the advances of Information and Communication Technology (ICT) and artificial intelligence (AI) techniques, an emerging paradigm known as smart productservice systems (Smart PSS) has been proposed and discussed. Smart PSS is an IT driven value co-creation business strategy, consisting of smart, connected products (SCP) as tools and medium, stakeholders as participants, and intelligent systems as the supporting tools. Owing to the unique characteristics of Smart PSS, huge-volume, high velocity and heterogeneous data can be collected and analyzed to extract useful knowledge with wisdom. This is especially important in the requirement elicitation process. Though many existing studies have discussed methods to support requirement elicitation process, few studies emphasize how the data and information play their roles with products and services in the context of Smart PSS. Aiming to address it, a graph-based requirement elicitation approach considering system-in-use information in the context of Smart PSS is proposed. It leverages the graph embedding technique, i.e. deepwalk, together with the pre-defined product/service/condition ontologies to depict their in-context relations, and hence discover prospective latent needs. Finally, a case study of smart bike design requirement elicitation is illustrated at last to demonstrate its feasibility and effectiveness. |
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