Maturity Assessment: A Case Study toward Sustainable Smart Manufacturing Implementation

Author(s):  
Xiaonan Shi ◽  
Takenori Baba ◽  
Daisuke Osagawa ◽  
Mitsushiro Fujishima ◽  
Teruaki Ito
Author(s):  
Shaw C. Feng ◽  
William Z. Bernstein ◽  
Thomas Hedberg ◽  
Allison Barnard Feeney

The need for capturing knowledge in the digital form in design, process planning, production, and inspection has increasingly become an issue in manufacturing industries as the variety and complexity of product lifecycle applications increase. Both knowledge and data need to be well managed for quality assurance, lifecycle impact assessment, and design improvement. Some technical barriers exist today that inhibit industry from fully utilizing design, planning, processing, and inspection knowledge. The primary barrier is a lack of a well-accepted mechanism that enables users to integrate data and knowledge. This paper prescribes knowledge management to address a lack of mechanisms for integrating, sharing, and updating domain-specific knowledge in smart manufacturing (SM). Aspects of the knowledge constructs include conceptual design, detailed design, process planning, material property, production, and inspection. The main contribution of this paper is to provide a methodology on what knowledge manufacturing organizations access, update, and archive in the context of SM. The case study in this paper provides some example knowledge objects to enable SM.


2018 ◽  
Vol 11 (3) ◽  
pp. 291-310 ◽  
Author(s):  
Rafif Al-Sayed ◽  
Jianhua Yang

Purpose The purpose of this paper is to examine empirically China’s determined thrust to attain a high level of technological innovation and the factors affecting moving towards a smart and sophisticated manufacturing ecosystem in conjunction with the Belt and Road Initiative (OBOR). Design/methodology/approach This research provides empirical determination of the factors affecting moving towards smart manufacturing ecosystems in China. The method is based on combining two approaches: semi-structured interview and questionnaire-based with academics, experts and managers in various Chinese industrial sectors. The results are based on the multivariate analysis of the collected data. A case study of the current manufacturing ecosystem was also analyzed, in order to understand the present state as well as the potential for China’s competitive edge in the developed OBOR countries. Findings The results illustrate the importance of the infrastructure dimension comprising variables related to ecosystems, industrial clusters and Internet of Things IoT and other advanced technologies. A case study of the city of Shenzhen’s transformation into a smart cluster for innovative manufacturing points out how China’s OBOR initiative for regional collaboration will further transform the regional smart clusters into an ultra-large innovation based smart ecosystem. Originality/value This research is the first to study China’ policies towards playing a prominent role in the Fourth Industrial Revolution 4IR in the context of the OBOR initiative, through empirically defining the factors affecting moving towards a knowledge-intensive smart manufacturing ecosystem where the added value is mostly innovation based.


2019 ◽  
Vol 11 (5) ◽  
pp. 837-862 ◽  
Author(s):  
Diamantino Torres ◽  
Carina Pimentel ◽  
Susana Duarte

Purpose The purpose of this study intends to make a characterization of a shop floor management (SFM) system in the context of smart manufacturing, through smart technologies and digital shop floor (DSF) features. Design/methodology/approach To attain the paper objective, a mixed method methodology was used. In the first stage, a theoretical background was carried out, to provide a comprehensive understanding on SFM system in a smart manufacturing perspective. Next, a case study within a survey was developed. The case study was introduced to characterize a SFM system, while the survey was made to understand the level of influence of smart manufacturing technologies and of DSF features on SFM. In total, 17 experts responded to the survey. Findings Data analytics is the smart manufacturing technology that influences more the SFM system and its components and the cyber security technology does not influence it at all. The problem solving (PS) is the SFM component more influenced by the smart manufacturing technologies. Also, the use of real-time digital visualization tools is considered the most influential DSF feature for the SFM components and the data security protocols is the least influential one. The four SFM components more influenced by the DSF features are key performance indicator tracking, PS, work standardization and continuous improvement. Research limitations/implications The study was applied in one multinational company from the automotive sector. Originality/value To the best of the authors’ knowledge, this work is one of the first to try to characterize the SFM system on smart manufacturing considering smart technologies and DSF features.


Author(s):  
Dwitama Heryadi Kurniawan ◽  
Yova Ruldeviyani ◽  
Mohammad Rizky Adrian ◽  
Sutia Handayani ◽  
M. Rizki Pohan ◽  
...  

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