• International Commerce and Information Review
• /
• v.12 no.2
• /
• pp.183-211
• /
• 2010

This paper empirically investigated whether the dependence of manufacturing firms effects the influence strategies and long-term orientation based on the data from manufacturing firms in Korea. U.S., and Japan. Also, the proposed model was proven by the structural equation model with the data gathered from 105 manufacturing firms in Korea, 103 in U.S., and 83 in Japan. The findings were as follows. First, the dependence of all of manufacturing firms, regardless of country, positively affected the coercive influence strategies of distributors, whereas the dependence positively affected the noncoercive influence strategies in U.S. and Japan but in the case of Korea, it showed the reverse direction and were not statistically significant. Second, the dependence of Korean manufacturing firms positively affected the long-term orientation but American manufacturing firms showed the reverse direction and it was not statistically significant. In the case of Japanese manufacturing firms, the direction predicted in the paper was shown but was not statistically significant. Third, the coercive influence strategies positively affected the long-term orientation in Korea but it showed the negative relationship in Japan. Fourth, the noncoercive influence strategies positively affected the long-term orientation in all countries. Lastly, a few implications, limitations and future study issues were discussed.

• Journal of the Korean Society of Manufacturing Technology Engineers
• /
• v.26 no.3
• /
• pp.272-277
• /
• 2017

CCTV image processing techniques have been developed for safety management in manufacturing sites. However, CCTV growth has become a social problem for video surveillance with regard to privacy. This study aims to manage the safety system efficiently and protect privacy simultaneously. In this study, the CCTV monitoring system is composed of five steps (accident monitoring, detection, notification, management, restoration). De-identified image is observed when we are in a normal situation. De-identified image changes to identified image when it detects an accident. As soon as it detects an accident, the accident information is sent to the safety administrator. Then the administrator could conduct safety measures. Afterward, accumulated accident data could be used for statistical data that could be utilized as analyzing expecting accident.

• Journal of Korean Institute of Industrial Engineers
• /
• v.42 no.6
• /
• pp.370-376
• /
• 2016

Manufacturing data analysis and its applications are getting a huge popularity in various industries. In spite of the fast advancement in the big data analysis technology, however, the manufacturing quality data monitored from the automated inspection system sometimes is not reliable enough due to the complex patterns of product quality. In this study, thus, we aim to define the level of trusty of an automated quality inspection system and improve the reliability of the quality inspection data. By correlation analysis and feature selection, this paper presents a method of improving the inspection accuracy and efficiency in an SVM-based automatic product quality inspection system using thermal image data in an auto part manufacturing case. The proposed method is implemented in the sealer dispensing process of the automobile manufacturing and verified by the analysis of the optimal feature selection from the quality analysis results.

• Journal of Labour Economics
• /
• v.32 no.2
• /
• pp.95-124
• /
• 2009

In this article, I were trying to analyze the listed manufacturing companies' trend of productivity and the corporate education & training effect after the financial crisis. According to the analysis, the listed manufacturing companies have decreased their productivity since financial crisis, and from such declining trend. jobless growth and a growth without physical and human capital investment has been observed. Furthermore, there is no efficient labor force coordination within the manufacturing industry; In order to analyze the effect of education & training investment on productivity more deeply, I have practiced the dynamic panel data analysis from constructing the micro panel data which consists of company level information 1997~2008. According to the consequences, dynamic panel data analysis solved the problem of the overestimating education & training effect fairly well.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2021.10a
• /
• pp.89-92
• /
• 2021

In a manufacturing fields, an abnormality or breakdown of equipment is a factor that causes product defects. Recently, with the spread of smart factory services, a lot of research to predict and prevent machine's failures is actively ongoing. However, there is a big difficulty in developing a classification model because the number of abnormal or failure data of the machine is severely smaller than normal data. In this paper, we present an algorithm for detecting abnormalities in an MCT at manufacturing work site depending on the differences between inputs and outputs of Autoencoder model and analyze its performance. The algorithm detects abnormalities using only features of normal data from manufacturing data of the MCT in which abnormal data does not exist.

• Journal of the Korea Safety Management & Science
• /
• v.12 no.3
• /
• pp.333-339
• /
• 2010

Data mining is the process of finding and analyzing data from a big database and summarizing it into useful information for a decision-making. A variety of data mining techniques have been being used for wide range of industries. One application of those is especially so for gathering meaningful information from process data in manufacturing factories for quality improvement. The purpose of this paper is to provide a methodology to improve manufacturing quality of fuel tanks which are auto-parts. The methodology is to analyse influential attributes and establish a model for optimal manufacturing condition of fuel tanks to improve the quality using decision tree, association rule, and feature selection.

• The Journal of Society for e-Business Studies
• /
• v.27 no.2
• /
• pp.205-218
• /
• 2022

Recently, digital transformation in manufacturing has been accelerating. It results in that the data collection technologies from the shop-floor is becoming important. These approaches focus primarily on obtaining specific manufacturing data using various sensors and communication technologies. In order to expand the channel of field data collection, this study proposes a method to automatically collect manufacturing data based on vision-based artificial intelligence. This is to analyze real-time image information with the object detection and tracking technologies and to obtain manufacturing data. The research team collects object motion information for each frame by applying YOLO (You Only Look Once) and DeepSORT as object detection and tracking algorithms. Thereafter, the motion information is converted into two pieces of manufacturing data (production performance and time) through post-processing. A dynamically moving factory model is created to obtain training data for deep learning. In addition, operating scenarios are proposed to reproduce the shop-floor situation in the real world. The operating scenario assumes a flow-shop consisting of six facilities. As a result of collecting manufacturing data according to the operating scenarios, the accuracy was 96.3%.

• Korea Trade Review
• /
• v.45 no.2
• /
• pp.229-262
• /
• 2020

The price-to-earnings ratio (PER) is an important indicator to measure the stock price and profitability of a firm; it is also the most used valuation indicator among investors. When using the PER to compare the investment values of different stocks, these stocks must come from the same sector. This study mainly focuses on the China's listed manufacturing firms. By learning from previous research results and analyzing the current situation, we studied the correlation between the manufacturing sector's PER and its influencing factors from both macro and micro perspectives, the combination of which eventually sheds light on such correlation. Analyzing GDP growth rate data, Manufacturing Purchasing Managers' Index, and other macroeconomic variables from 2008 to 2018, we conclude that these variables jointly have a certain impact on the average PER of the manufacturing sector. We then form panel data based on relevant (2014-2018) data gathered from 317 of China's A-listed manufacturing firms to study the impact of micro-variables on PER. By using Stata and other software to analyze the panel data, we reach the conclusion that the Debt to Asset Ratio, Return on Equity, EPS growth rate, Operating Profit Ratio, Dividend Payout Ratio, and firm size have a significant impact on PER. The Current Ratio, Treasury Stock ratio and Ownership Concentration have no distinct effect on PER. Based on our empirical findings, we design a theoretical model that affects the PER.

• Proceedings of the Safety Management and Science Conference
• /
• 2006.04a
• /
• pp.45-50
• /
• 2006

The main objective of this study is to provide feature analysis of industrial accidents in manufacturing industries using CHAID algorithm. In this study, data on 10,536 accidents were analyed to create risk groups, Including the risk of disease and accident. The sample for this work chosen from data related to manufacturing industries during three years $(2002\sim2004)$ in Korea. The resulting classification rules have been incorporated into development of a developed database tool to help quantify associated risks and act as an early warning system to individual industrial accident in manufacturing industries.

• 연구논문집
• /
• s.27
• /
• pp.167-174
• /
• 1997

Point of Production/Manufacturing Execution Systems are an essential component of operations in today's competitive business environments, which require greater production efficiency and effectiveness. POP/MES focuses on the valuing-adding processes, helping to reduce manufacturing cycle time, improve product quality, reduce WIP, reduce or eliminate paperwork between shifts, reduce lead time and empowering plant operations staff. In this paper, we implement POP/MES to manage real-time plant floor data which is gathered by I/O server into database management system. I/O server is a software allows data exchange between factory real-time database and several hardware devices such as PLC, DCS, robot and sensor through ethernet TCP/IP protocol.