scholarly journals The Use of Big Data for Sustainable Development in Motor Production Line Issues

2020 ◽  
Vol 12 (13) ◽  
pp. 5323
Author(s):  
Yao-Chin Lin ◽  
Ching-Chuan Yeh ◽  
Wei-Hung Chen ◽  
Wei-Chun Liu ◽  
Jyun-Jie Wang
Keyword(s):  
Big Data ◽  
Production Line ◽  
Big Data Analytics ◽  
Secondary Data ◽  
Smart Manufacturing ◽  
Production Lines ◽  
Core Technology ◽  
Expert Interview ◽  
Industry Sustainability ◽  
Motor Production

This study explores big data gathered from motor production lines to gain a better understanding of production line issues. Motor products from Solen Electric Company’s motor production lines were used to predict failure points based on big data analytics, where 3606 datapoints from the company’s testing equipment were statistically analyzed. The current study focused on secondary data and expert interview results to further define the relevant statistical dimensions. Only 14 of the original 88 detection parameters were required for monitoring the production line. The relationships between these parameters and the relevant motor components were established to indicate how an abnormal reading may be interpreted to quickly resolve an issue. Thus, a theoretical model for the monitoring of the motor production line was proposed. Further implications and practical suggestions are also offered to improve the production lines. This study explores big data analysis and smart manufacturing and demonstrates the promise of these technologies in improving production line efficiency and reducing waste to promote sustainable production goals. Big data thus constitute the core technology for advancing production lines into Industry 4.0 and promoting industry sustainability.

scholarly journals Implementation Criteria for Intelligent Systems in Motor Production Line Process Management

Processes ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 537 ◽  
Author(s):  
Yao-Chin Lin ◽  
Ching-Chuan Yeh ◽  
Wei-Hung Chen ◽  
Kai-Yen Hsu
Keyword(s):  
Production Line ◽  
Intelligent Systems ◽  
Process Management ◽  
Assembly Line ◽  
Intelligent System ◽  
Secondary Data ◽  
Robot Arm ◽  
Production Lines ◽  
Line Process ◽  
Motor Production

In this study, the factors that affect the implementation of intelligent systems in motor production lines are analyzed. A motor production line located in Vietnam is used as the research object. The research methods include secondary data collection, field study, and interviews. This study demonstrates the following: firstly, the implementation of intelligent systems in motor production lines is heading toward Industry 4.0. Secondly, it is proposed that three functional systems—robot arm, image recognition, and big data analysis—can be introduced in the motor production line. This study analyzes the process involved in coil and motor production lines and attempts to combine intelligent system functions. It is expected that in the future, manpower will be reduced, production line productivity will increase, and intelligent production lines will be proposed. The factors that affect the introduction of intelligent systems in motor production lines are improved, and the importance of intelligent systems, which has been rarely considered in previous studies, is highlighted. In the implementation criteria of the intelligent system in the process management of the motor production line, this study provides some suggestions (to coil and motor assembly line) for the production process management. These suggestions can be provided as a reference for production lines that acquaint with intelligent systems.

scholarly journals A Web Services, Ontology and Big Data Analysis Technology-Based Cloud Case-Based Reasoning Agent for Energy Conservation of Sustainability Science

2020 ◽  
Vol 10 (4) ◽  
pp. 1387
Author(s):  
Shih-Chin Chen ◽  
Sheng-Yuan Yang
Keyword(s):  
Big Data ◽  
Web Services ◽  
Energy Conservation ◽  
Energy Saving ◽  
Sustainability Science ◽  
Big Data Analytics ◽  
Case Based Reasoning ◽  
Core Technology ◽  
Reasoning Agent ◽  
Case Based

Energy conservation is one of the important topics for sustainability science, while case-based reasoning is one of the most important techniques for sustainable processing. This study aimed to develop a cloud case-based reasoning agent that integrates multiple intelligent technologies and supports, which can help users to quickly, accurately, and effectively obtain useful cloud energy-saving information in a timely manner for sustainability science. The system was successfully built with the support of Web services technology, ontology, and big data analytics. To set up this energy-saving case-based reasoning agent, this study reviewed the relevant technologies for building a web services platform and explored how to widely integrate and support the cloud interaction of the energy-saving data processing agent via the technologies. In addition to presenting relevant R&D technologies and results in detail, this study carefully conducted performance and learning experiments to prove the system’s effectiveness. The results showed that the core technology of the case-based reasoning agent achieved good performance and that the learning effectiveness of the overall system was also great.

scholarly journals Relevance of Big Data for Business and Management. Exploratory Insights (Part I)

2018 ◽  
Vol 13 (2) ◽  
pp. 153-163
Author(s):  
Claudia Ogrean
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Secondary Data ◽  
Supply Side ◽  
Critical Content ◽  
History Of ◽  
The Impact ◽  
Business And Management ◽  
Ubiquitous Presence

AbstractOver the last few decades Big Data has impetuously penetrated almost every domain of human interest/action and it has (more or less consciously) become a ubiquitous presence of day to day life. The main questions this exploratory paper seeks to address (throughout its two parts) are the following: What is the (actual) impact of Big Data on Business & Management and How can businesses (through their management) leverage the potential of Big Data to their benefit? A gradual, step by step approach (based on literature review and a variety of secondary data) will guide the paper in search for answers to the abovementioned questions: starting with a concise history of the topic Big Data as reflected in academia and a critical content analysis of the Big Data concept, the paper will then continue by emphasizing some of the most significant realities and trends that characterize the supply-side of the big data industry; the second part of the paper is dedicated to the investigation of the demand-side of the big data industry – by highlighting some evidences (and projections) on the impact of big data analytics on Business & Management (both at aggregate and granular level) and exploring what companies could and should do (through their management) in order to best capitalize on the opportunities of big data and avoid/minimize the impact of its threats.

Sign in / Sign up

Export Citation Format

Share Document