• Journal of Korean Society for Quality Management
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• v.33 no.3
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• pp.105-113
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• 2005

Six sigma has been the most influential management innovation program in a number of leading companies and its application has extended beyond manufacturing processes. Most companies have taken company-wide six sigma initiatives to also improve transactional and R&D processes. However, due to the limitation of existing methodologies, numerous difficulties arise during the six sigma application to non-manufacturing processes. In this study, a modified six sigma framework is proposed to facilitate six sigma implementation to transactional processes. The newly developed framework is applied to a six sigma project of the new product launching process of a marketing area.

• Journal of Korean Society for Quality Management
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• v.36 no.4
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• pp.37-46
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• 2008

There are big differences in six sigma projects between manufacturing processes and transactional/service processes. This paper analyzed the differences between the two areas by examining 18 six sigma case studies in Korean companies. To characterize six sigma case studies, step-by-step questions and check criteria were developed based on 12 step DMAIC methodology. On the basis of those characterized data, the differences between two areas were analyzed. Significant differences were found in the number of CTQs, validation of measurement systems, methods to determine improvement objectives, the methods to elicit improvement plans and types of control systems. The most significant difference was that statistical tools were widely used in manufacturing processes, but qualitative tools were used in transactional/service processes during improvement phase.

• Proceedings of the Korean Society for Quality Management Conference
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• 2006.11a
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• pp.157-163
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• 2006

There are big differences in six sigma applications between manufacturing processes and transactional processes. This paper analyzes the differences between two areas by examining 18 six sigma case studies in Korean companies. To characterize six sigma cases, a step-by-step checklist is developed based on 12-step DMAIC methodology proposed by US six sigma academy. On the basis of those characterized data, the differences between two areas are analyzed. The most significant differences is that statistical tools are widely used in manufacturing processes, but qualitative tools are used in transactional processes during improvement phase.

• International Journal of Quality Innovation
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• v.5 no.2
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• pp.17-25
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• 2004

Despite the fact that much has been published about Six Sigma in the business and academic press and on the Internet in recent years, there is still confusion among many people, particularly those who work in the transactional and service environments, about the nature of Six Sigma. It is true that Six Sigma like other process improvement programs before was first applied to manufacturing processes; however, many corporations including General Electric and Sony have successfully applied Six Sigma to their transactional and service processes as well. Six Sigma is used by many companies not only to improve the quality of their products and services but also to achieve quantifiable financial results, improve management style and communication, and achieve customer and employee satisfaction. Whether in manufacturing or non-manufacturing environments, the application of Six Sigma differs from organization to organization. Although there are many common elements between these applications, however, special care must be taken when customizing Six Sigma to suit the organizations' needs. In this paper, the author provides some practical and useful guidelines for Six Sigma deployment. This paper is not about the use of numerous statistical tools and techniques that can be found in a typical Six Sigma toolbox. The main emphasis has been placed both on the concept and the implementation of Six Sigma, particularly within the non-manufacturing areas of business.

• Journal of Korean Society for Quality Management
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• v.31 no.2
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• pp.220-229
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• 2003

The methodology of Six Sigma originated at Motorola in the early 1980s is nowadays proved to be an effective way in Improving the quality. Many companies find adding Six Sigma to their current business system gives them all or almost all the elements of total quality management(TQM). However most companies where Six Sigma is implemented confront many difficulties in non-manufacturing parts such as transactional and administrative processes. In this paper we discuss distinct aspects of such non-manufacturing process, and investigate activating scheme of Six Sigma in those parts of industry.

• Journal of Korean Institute of Industrial Engineers
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• v.32 no.4
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• pp.338-346
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• 2006

In this paper, we analyze and review introduction, implementation, and revision phases in the six sigma movement in LG Electronics. In LG Electronics, 6 sigma activities have been activated not only at manufacturing divisions but also in transactional processes, business, product R&D, marketing, and software development. At present, six sigma is a tool to improve processes and also become the engine of the business innovation. In this paper, concepts and contents of an on-going LG innovation process called TDR(Tear Down & Redesign) are explained in detail. We also propose important aspects and give the future prospect for six sigma.

• Journal of Internet Computing and Services
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• v.16 no.1
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• pp.67-74
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• 2015

In recent years, frequent itemset mining for considering the importance of each item has been intensively studied as one of important issues in the data mining field. According to strategies utilizing the item importance, itemset mining approaches for discovering itemsets based on the item importance are classified as follows: weighted frequent itemset mining, frequent itemset mining using transactional weights, and utility itemset mining. In this paper, we perform empirical analysis with respect to frequent itemset mining algorithms based on transactional weights. The mining algorithms compute transactional weights by utilizing the weight for each item in large databases. In addition, these algorithms discover weighted frequent itemsets on the basis of the item frequency and weight of each transaction. Consequently, we can see the importance of a certain transaction through the database analysis because the weight for the transaction has higher value if it contains many items with high values. We not only analyze the advantages and disadvantages but also compare the performance of the most famous algorithms in the frequent itemset mining field based on the transactional weights. As a representative of the frequent itemset mining using transactional weights, WIS introduces the concept and strategies of transactional weights. In addition, there are various other state-of-the-art algorithms, WIT-FWIs, WIT-FWIs-MODIFY, and WIT-FWIs-DIFF, for extracting itemsets with the weight information. To efficiently conduct processes for mining weighted frequent itemsets, three algorithms use the special Lattice-like data structure, called WIT-tree. The algorithms do not need to an additional database scanning operation after the construction of WIT-tree is finished since each node of WIT-tree has item information such as item and transaction IDs. In particular, the traditional algorithms conduct a number of database scanning operations to mine weighted itemsets, whereas the algorithms based on WIT-tree solve the overhead problem that can occur in the mining processes by reading databases only one time. Additionally, the algorithms use the technique for generating each new itemset of length N+1 on the basis of two different itemsets of length N. To discover new weighted itemsets, WIT-FWIs performs the itemset combination processes by using the information of transactions that contain all the itemsets. WIT-FWIs-MODIFY has a unique feature decreasing operations for calculating the frequency of the new itemset. WIT-FWIs-DIFF utilizes a technique using the difference of two itemsets. To compare and analyze the performance of the algorithms in various environments, we use real datasets of two types (i.e., dense and sparse) in terms of the runtime and maximum memory usage. Moreover, a scalability test is conducted to evaluate the stability for each algorithm when the size of a database is changed. As a result, WIT-FWIs and WIT-FWIs-MODIFY show the best performance in the dense dataset, and in sparse dataset, WIT-FWI-DIFF has mining efficiency better than the other algorithms. Compared to the algorithms using WIT-tree, WIS based on the Apriori technique has the worst efficiency because it requires a large number of computations more than the others on average.

• Korea Trade Review
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• v.47 no.6
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• pp.335-353
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• 2022

There are two aspects of digital trade: the digitalisation of goods/services being traded and the digitalisation of the transactional act. Digital data (i.e. machine-readable industrial data and transactional data) is the major driving force for both aspects of digital trade. Digital data is a non-rivalrous input, whether for production or marketing activities, and is thus able to be used by many firms or government agencies without limiting the use of others. Digital platforms provide online infrastructure for the interactions between groups, for instance, consumers and producers. The externality effect refers to the situation in which prosperity in one group on a given platform will improve the returns of other groups on the same platform. In the era of the data-driven economy, strategic trade policy can involve data-related policies. The major objective of these policies is to improve the competitiveness of domestic firms. For instance, firms may be subsidised if they use cloud services provided by specific platforms. This strand of strategic trade policies might be useful for increasing the competitiveness of small-and medium-sized enterprises (SMEs) via the digitalisation of production/marketing processes. Alternatively, strategic trade policy may also exploit the externality effect via platform economy-related policies. Further, some countries may form data coalitions to facilitate cross-border data flow. This paper uses cases in Asian countries to illustrate which role these strategic trade policies can play in the digital economy.

• Journal of the Korea Safety Management & Science
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• v.12 no.1
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• pp.161-168
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• 2010

This paper presents a six sigma project for improving customer satisfaction of a franchise store. The project follows a disciplined process of five macro phase : define, measure, analyze, improve, and control(DMAIC). Due to the limitation of existing methodologies, numerous difficulties arise during the six sigma application to transactional processes. This paper expected to be helpful to service industry in which is very difficult to measure and implement.

• Journal of Korean Society of Water and Wastewater
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• v.28 no.1
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• pp.125-134
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• 2014

6 sigma is a management innovation strategy which improves most of all managerial processes including transactional and project based operations such as marketing, purchasing, accounting, and construction. Even though 6 sigma is trying to solve problems from the customer's viewpoint in the scientific manner, project leader feels some difficulties in implementation because of several reasons. Especially the difficulties are prevalent in construction site. This paper investigates the cause of the difficulties through questionnaires, analyzes the investigation results, and verifies the critical success factors of 6 sigma implementation. Factor analysis has been usually employed in reducing quantity of data and summarizing information chaos. In this study, several variables from questionnaires are grouped into just only four factors by the process of factor analysis. The critical success factors are extracted as project management system, implementation mechanism, site condition, and project ownership. Some ideas for each individual success factor are suggested, which are expected to be useful in successful implementation of 6 Sigma in construction site.