A framework with revised rough-DEMATEL to capture and evaluate requirements for smart industrial product-service system of systems

Abstract

Smart industrial product-service system of systems (SiP-S3) is a new extension of industrial PSS via smart technology and SoS (system of systems) engineering. A framework with revised rough-DEMATEL method is proposed to capture and evaluate requirements for SiP-S3. From the various interactions in value stream mapping of SiP-S3, business, functional and non-functional requirements can be captured and converged into SiP-S3 requirements. Due to the interrelation between requirements of SiP-S3 and uncertainty of expert judgments, rough-DEMATEL is adopted but is revised in two aspects. Generally, SiP-S3 requirement items are plentiful, a set of programming code for rough set approximation is firstly given to reduce manual calculation burden. Moreover, compared to multiple operators of modified-CFCS (converting fuzzy values into crisp scores) plus SVL (single vector-length), a feasible and simpler operator of AVL (average vector-length) on rough-prominence and rough-relation is firstly devised to prioritise requirements. As such, roughness can be remained till at the end of calculation procedure to avoid uncertain assessment information loss. Surface mount technology (SMT) is not trivial in electronic manufacturing service industry, an illustrative case study of SMTE-S3 (SMT equipment-service system of systems) is demonstrated to verify feasibility and potential of proposal methodological framework.

Keywords:

• SiP-S3
• system of systems
• requirements
• revised rough-DEMATEL
• average vector-length

Acknowledgement

The authors appreciate the editor and anonymous reviewers for their helpful comments and suggestions on this article.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The author would like to thank Shanghai Institute of Producer Service Development (SIPSD), Shanghai Research Centre for industrial Informatics (SRCI²) and National Natural Science Foundation of China (grant number 71632008) for the funding support to this research.