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Manufacturing Process Innovation-Oriented Knowledge Evaluation Using MCDM and Fuzzy Linguistic Computing in an Open Innovation Environment

Gangfeng Wang (), Xitian Tian (), Yongbiao Hu (), Richard David Evans (), Mingrui Tian () and Rong Wang ()
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Gangfeng Wang: Key Laboratory of Road Construction Technology and Equipment of MOE, Chang’an University, Xi’an 710064, China
Xitian Tian: Institute of CAPP & Manufacturing Engineering Software, Northwestern Polytechnical University, Xi’an 710072, China
Yongbiao Hu: Key Laboratory of Road Construction Technology and Equipment of MOE, Chang’an University, Xi’an 710064, China
Richard David Evans: Business Information Management and Operations, University of Westminster, London NW1 5LS, UK
Mingrui Tian: Key Laboratory of Road Construction Technology and Equipment of MOE, Chang’an University, Xi’an 710064, China
Rong Wang: Department of Information Engineering, Engineering University of CAPF, Xi’an 710086, China

Sustainability, 2017, vol. 9, issue 9, 1-19

Abstract: In today’s complex, constantly evolving and innovation-supporting manufacturing systems, knowledge plays a vital role in sustainable manufacturing process planning and problem-solving, especially in the case of Computer-Aided Process Innovation (CAPI). To obtain formalized and promising process innovation knowledge under the open innovation paradigm, it is necessary to evaluate candidate knowledge and encourage improvement suggestions based on actual innovation situations. This paper proposes a process innovation-oriented knowledge evaluation approach using Multi-Criteria Decision-Making (MCDM) and fuzzy linguistic computing. Firstly, a comprehensive hierarchy evaluation index system for process innovation knowledge is designed. Secondly, by combining an analytic hierarchy process with fuzzy linguistic computing, a comprehensive criteria weighting determination method is applied to effectively aggregate the evaluation of criteria weights for each criterion and corresponding sub-criteria. Furthermore, fuzzy linguistic evaluations of performance ratings for each criterion and corresponding sub-criteria are calculated. Thus, a process innovation knowledge comprehensive value can be determined. Finally, an illustrative example of knowledge capture, evaluation and knowledge-inspired process problem solving for micro-turbine machining is presented to demonstrate the applicability of the proposed approach. It is expected that our model would lay the foundation for knowledge-driven CAPI in sustainable manufacturing.

Keywords: manufacturing process innovation; computer-aided innovation; CAPI; knowledge management; open innovation; multi-criteria decision-making (search for similar items in EconPapers)
JEL-codes: Q Q0 Q2 Q3 Q5 Q56 O13 (search for similar items in EconPapers)
Date: 2017
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