Evaluation of smart manufacturing performance using a grey theory-based approach: a case study

Abstract

PurposeThe purpose of the article is to report a study on evaluation of smart manufacturing (SM) performance using a grey theory-based approach.Design/methodology/approachIn total, 30 criteria and 79 attributes for SM performance have been developed. A grey theory-based approach has been used for SM performance evaluation. The grey index has been calculated, and weaker areas have been derived. Performance level of SM has been evaluated using the Euclidean distance approach.FindingsThe SM performance index is found to be (3.036, 12.296). The ideal grey performance importance index (GPII) is obtained as (3.025, 4.875). The level of visibility and traceability, vertical integration, lead time and configuration data espionage and control ability are strong performing attributes. Integration abilities of services and manufacturing systems, ability of self-control, worker and raw material productivity, collaboration among buyers and suppliers and dynamic scheduling are identified as weaker areas, and suggestions for improvement have been derived. SM performance level has been identified as “Good.”Research limitations/implicationsAdditional performance measures could be included as a part of evaluation. Practitioners can overcome weaker areas in the early phase. Management achieves confidence and practitioners attain success in implementation of SM in industry through the developed SM performance indexing system.Originality/valueIdentification of SM performance measures and analysis of SM performance is the original contribution of the authors. The developed approach assists practitioners and managers to focus more on specific areas for performance improvement.

Correlation analysis between talent training quality and regional economic development based on multivariate statistical analysis model

“…Cluster analysis is a widely used exploratory identification technique, which in essence means that targets to be evaluated with similar characteristics are grouped in an aggregation-by-aggregation approach based on the proximity or similarity of the system, which can visually reflect the similarity and difference of data between classes [17][18][19]. In this paper, the spatial clustering analysis of water quality data at all monitoring sections was performed using the variable cluster analysis method in the systematic cluster analysis method.…”