• Journal of the Korean Data and Information Science Society
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• v.26 no.5
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• pp.1117-1128
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• 2015

In order to secure an important competitive advantage in manufacturing business, an automation and information system from manufacturing process has been introduced; however, small and medium enterprises have not met the power of information in the manufacturing fields. They have been managing the manufacturing process that is depending on the operator's experience and data written by hand, which has limits to reveal cause of defective goods clearly, in the case of happening of low-grade goods. In this study, we analyze critical factors which affect the quality of some manufacturing process in terms of 4M. We also studied the automobile parts processing of the small and medium manufacturing enterprises controlled with data written by hand so as to collect the data written by hand and to utilize sensor data in the future. Analysis results show that there is no deference in defective quantity in machines, while raw materials, production quality and task tracking have significant deference.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.3
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• pp.176-185
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• 2018

With the recent development of manufacturing technology and the diversification of consumer needs, not only the process and quality control of production have become more complicated but also the kinds of information that manufacturing facilities provide the user about process have been diversified. Therefore the importance of big data analysis also has been raised. However, most small and medium enterprises (SMEs) lack the systematic infrastructure of big data management and analysis. In particular, due to the nature of domestic manufacturing companies that rely on foreign manufacturers for most of their manufacturing facilities, the need for their own data analysis and manufacturing support applications is increasing and research has been conducted in Korea. This study proposes integrated analysis platform for process and quality analysis, considering manufacturing big data database (DB) and data characteristics. The platform is implemented in two versions, Web and C/S, to enhance accessibility which perform template based quality analysis and real-time monitoring. The user can upload data from their local PC or DB and run analysis by combining single analysis module in template in a way they want since the platform is not optimized for a particular manufacturing process. Also Java and R are used as the development language for ease of system supplementation. It is expected that the platform will be available at a low price and evolve the ability of quality analysis in SMEs.

• Journal of the Korea Safety Management & Science
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• v.25 no.2
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• pp.159-165
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• 2023

The entire industry is increasing the use of big data analysis using artificial intelligence technology due to the Fourth Industrial Revolution. The value of big data is increasing, and the same is true of the production technology. However, small and medium -sized manufacturers with small size are difficult to use for work due to lack of data management ability, and it is difficult to enter smart factories. Therefore, to help small and medium -sized manufacturing companies use big data, we will predict the gross production time through machine learning. In previous studies, machine learning was conducted as a time and quantity factor for production, and the excellence of the ExtraTree Algorithm was confirmed by predicting gross product time. In this study, the worker's proficiency factors were added to the time and quantity factors necessary for production, and the prediction rate of LightGBM Algorithm knowing was the highest. The results of the study will help to enhance the company's competitiveness and enhance the competitiveness of the company by identifying the possibility of data utilization of the MES system and supporting systematic production schedule management.

• Journal of Information Technology Applications and Management
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• v.26 no.2
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• pp.13-26
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• 2019

The purpose of this study is to examine the effect of each of the two technology acceptance factors(employees' attitude toward smart factory technology, and ease of smart factory technology use) on the introduction level of each of the three smart factory technologies (manufacturing big data technology, automation technology, and supply chain integration technology), and in turn, the effect of each of the three smart factory technologies on manufacturing performance. This study employed PLS statistics software package to empirically validate a structural equation model with survey data from 100 domestic small-and medium-sized manufacturing firms (SMMFs). The analysis results revealed the followings. First, it is founded that employees' attitude toward smart factory technology influenced all of the three smart factory technology introduction levels in a positive manner. In particular, SMMFs of which employees had more favorable attitude toward smart factory technology tended to increase introduction levels of both automation technology and supply chain integration technology more than in the case of manufacturing big data technology. Second, ease of smart factory technology use also had a positive impact on each of the three smart factory technology introduction levels, respectively. A noteworthy finding is this : SMMFs which perceived smart factory technology as easier to use would like to elevate the introduction level of manufacturing big data technology more than in the cases of either automation technology or supply chain integration technology. Third, smart factory technologies such as automation technology and supply chain integration technology had affirmative impacts on manufacturing performance of SMMFs. These results shed some valuable insights on the introduction of smart factory technology : The success of smart factory heavily depends on organization-and people-related factors such as employees' attitude toward smart factory technology and employees' perceived ease of smart factory technology use.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.2
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• pp.43-57
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• 2021

Smart factories can be defined as intelligent factories that produce products through IoT-based data. In order to build and operate a smart factory, various new technologies such as CPS, IoT, Big Data, and AI are to be introduced and utilized, while the implementation of a MES system that accurately and quickly collects equipment data and production performance is as important as those new technologies. First of all, it is very essential to build a smart factory appropriate to the current status of the company. In this study, what are the essential prerequisite factors for successfully implementing a smart factory was investigated. A case study has been carried out to illustrate the effect of implementing ERP and MES, and to examine the extensibilities into a smart factory. ERP and MES as an integrated manufacturing information system do not imply a smart factory, however, it has been confirmed that ERP and MES are necessary conditions among many factors for developing into a smart factory. Therefore, the stepwise implementation of intelligent MES through the expansion of MES function was suggested. An intelligent MES that is capable of making various decisions has been investigated as a prototyping system by applying data mining techniques and big data analysis. In the end, in order for small and medium enterprises to implement a low-cost, high-efficiency smart factory, the level and goal of the smart factory must be clearly defined, and the transition to ERP and MES-based intelligent factories could be a potential alternative.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2003.11a
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• pp.109-131
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• 2003

This Study intends to find the impact of industry type on the relationship between Electronic Commerce(EC) implementation and performance. For this purpose, empirical study of domestic small and medium enterprises(SMEs) is carried out to test the relationship between EC implementation and EC performance and the moderating effect of industry type on the relationship between them. Previous empirical studies on EC mostly focused on the adoption of EC by business firms, and also have been carried out with the data from big enterprises. More often than not, the results obtained from the large business firms are used to provide the guidelines for SMEs. SMEs are, however, different from large business firms in many aspects, and need to be studied on their own. Empirical test shows that there are differences between manufacturing industry and service industry in utilizing EC and in achieving EC performance. The results of data analysis indicate that the industry type of SMEs is moderating the relationship between EC implementation variables(EC type, EC strategy, EC formality and EC character) and EC performance variables(EC utilization, EC satisfaction and EC usefulness).

• Information Systems Review
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• v.20 no.1
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• pp.81-98
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• 2018

ERP supports the automation and integration of business processes of enterprises and records voluminous data about the business activities of enterprises. The academe and business enterprises are focusing on process mining, which improves the performance of business processes and strengthens compliance. However, these studies focused on analysis of the business process of large companies, which adopts foreign ERP, such as SAP ERP or Oracle ERP. In comparison with foreign ERP, domestic ERP lags behind in terms of logging and managing of event data. Therefore, the application of process mining to domestic ERP is a challenging task. The present study aims to analyze domestic ERP based on process mining to overcome this challenge. This study discusses the lessons learned from a case study in a Korean cosmetics manufacturing company. Our results are expected to strengthen the competitiveness of Korean small and medium-sized enterprises that adopt domestic ERP and realize the outcomes of the large investment of the Korean government on the ERP implementation of enterprises.

• The Journal of the Korea Contents Association
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• v.19 no.7
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• pp.360-375
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• 2019

This paper tries to study the ecosystem after constructing the network of the continuing transactions associated with distribution industry with the data of more than 50 thousands firms provided by the Korean enterprise data (KED) for 2015. After applying the clustering method, one of social network analysis tools, we find the firms in the network grouped into 732 clusters occupying about 80% of whole distribution industry sales in KED data. The firms in a cluster have most of their transactions with other firms in the cluster. But the clusters have smaller firm numbers in the cluster and sales portion of the biggest firms in the industry than the case of the manufacturing industry. The Input-output analysis for the biggest distribution firms show that the small and medium size enterprise(SME)s have very high sale dependency on a main firm in some clusters. This fact implies more efficient fair transaction policies within the clusters. And small number of big distribution firms have very high rear production linkage effects on SMEs or on the 10th or 31th group with high portion of SME employment. They should be considered important in the SME growth and employment policies.

• Korean small business review
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• v.41 no.4
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• pp.1-36
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• 2019

The purpose of this study is to identify the relationships among purposes and contents of smart factory building and continuous utilization of smart factory. Specifically, this study identifies two types of purposes of smart factory building as follows: (1) improving productivity, (2) increasing flexibility. In this study, three aspects of smart factory building contents were suggested like this: (1) automation area (facility automation vs. work automation), (2) big data system focus (radical transformation vs. incremental improvement), and (3) value chain integration area (internal value chain integration vs. external value chain integration). In addition, we looked at how firm size moderates the purposes - contents - continuous utilization of smart factory relationship. A questionnaire survey was conducted on 151 manufacturing companies. More specifically, out of 151 companies, 100 are small-and-medium-sized enterprises and 51 large-sized enterprises. All questionnaires were targeted at companies with Smart Factory level above level 2. The analysis results of this study using Smart PLS statistical programs are as follows. First, the purposes of smart factory building including increasing productivity and flexibility had positive impacts on all of the contents of smart factory building. Second, all of smart factory building contents had positive impacts on the continuous use of smart factory except big data system for incremental improvement of manufacturing process. Third, the impacts of smart factory building purposes implementation on smart factory building contents varied depending on whether the purpose is productivity improvement or flexibility. Fourth, it was founded that firm size moderated the relationships of purposes - contents - continuous utilization of smart factory in such a way that large-sized firms tend to empathize the link between flexibility and smart factory building contents for continuous use of smart factory, while small-and-medium-sized-firms emphasizing the link between productivity and smart factory building contents. Most of the previous studies have focused on presenting current smart factory deployment cases. However, it is believed that this research has made a theoretical contribution in this field in that it established and verified a research model for the smart factory building strategy. Based on the findings from a working-level perspective, corporate practitioners also need to have a different approach to smart factory building, which should be emphasized depending on whether their purpose of building smart factory is to increase productivity or flexibility. In particular, since the results of this study identify the moderating effect of firm size, it is deemed necessary for firms to implement a smart factory building strategy suitable for their firm size.

• Journal of Distribution Science
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• v.9 no.2
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• pp.111-122
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• 2011

The purpose of this study is to survey the successful cases of small and medium Business Systematization Cognition by examining their entrepreneurial characteristics and analysing the factors affecting their success. To that end, previous studies on the association types of small businesses were studied. A research model was developed, and research hypotheses for an empirical analysis were established upon it. Suh et al. (2010) insist on the importance of Small Business Systematization in Korea but also show that small business performance is suffering: they are too small to stand alone. That is why association is so crucial for them: they must stand together. Unfortunately, association is difficult, as they have few specific links and little motivation. Even in franchising networks, association tends to be initiated by big franchisers, not small ones. In that sense, association among small businesses is crucial for their long-term survival. With this in mind, this study examines how they think and feel about the issue of 'Industrial Classification', how important Industrial Classification is to their business success, and what kinds of problems it raises in the markets. This study seeks the different cognitions among the association types of small businesses from the perspectives of participation motivation, systematization expectation, policy demand level, and management performance. We assume that different industrial classification types of small businesses will have different cognitions concerning these factors. There are four basic industrial classification types of small businesses: retail sales, restaurant, service, and manufacturing. To date, most of the studies in this area have focused on collecting data on the external environments of small businesses or performing statistical analyses on their status. In this study, we surveyed 4 market areas in Busan, Masan, and Changwon in Korea, where business associations consist of merchants, shop owners, and traders. We surveyed 330 shops and merchants by sending a questionnaire or visiting. Finally, 268 questionnaires were collected and used for the analysis. An ANOVA, T-test, and regression analyses were conducted to test the research hypotheses. The results demonstrate that there are differences in cognition depending upon the industrial classification type. Restaurants generally have a higher cognition concerning job offer problems and a lower cognition concerning their competitiveness. Restaurants also depend more on systematization expectation than do the other industrial classification types. On the policy demand level, restaurants have a higher cognition. This study identifies several factors that are contributing to management performance through differences in cognition that depend upon association type: systematization expectation and policy demand level have positive effects on management performance; participation motivation has a negative effect on management performance. We confirm also that the image factors of different cognitions are linked to an awareness of the value of systematization and that these factors show sequential and continual patterns in the course of generating performances. In conclusion, this study carries significant implications in its classifying of small businesses into the four different associational types (retail sales, restaurant, services, and manufacturing). We believe our study to be the first one to conduct an empirical survey in this subject area. More studies in this area will likely use our research frameworks. The data show that regionally based industrial classification associations such as those in rural cities or less developed areas tend to suffer more problems than those in urban areas. Moreover, restaurants suffer more problems than the norm. Most of the problems raised in this study concern the act of 'associating itself'. Most associations have serious difficulties in associating. On the other hand, the area where they have the least policy demand is that of service types. This study contributes to the argument that associating, rather than financial assistance or management consulting, promotes the start-up and managerial performance of small businesses. This study also has some limitations. The main limitation is the number of questionnaires. We could not survey all the industrial classification types across the country because of budget and time limitations. If we had, we could have produced many more useful results and enhanced the precision of our analysis. The history of systemization is very short and the number of industrial classification associations is relatively low in Korea. We should keep in mind, though, that this is very crucial to systemization entrepreneurs starting their businesses, as it can heavily affect their chances of success. Being strongly associated with each other might be critical to the business success of industrial classification members. Thus, the government needs to put more effort and resources into supporting the drive of industrial classification members to become more strongly associated.