• Journal of the Korean Home Economics Association
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• v.40 no.8
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• pp.61-69
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• 2002

The research of the current situation of made-to-measure production by ready-made men's suit companies was done by interviewing and surveying the made-to-measure specialists. It was apparent that most frequently purchased items in Mass Customization were jackets of formal dress. The results of the research on the current situation of Mass Customization by ready-made clothes companies were as follows. The Mass Customization accounted for 3∼10%. The advantages of Mass Customization were more acceptable sizes, designs and details while the initial reasons for it were unusual physical figures and designs. The posture of a customer during measuring turned out to be critical enough to influence fitness after production. The companies in the research were found out to have materials for Mass Customization in storage and the sizes and other data of fixed circle of customers who would want Mass Customization saved in computer in most of the cases.

• Proceedings of KHEA Conference
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• 2002.04a
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• pp.65-65
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• 2002

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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• pp.429-432
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• 2004

다수의 품목을 개별적으로 주문하기 보다는 묶어서 한꺼번에 주문하는 경우 수송비용과 주문비용을 줄일 수 있으며, 같은 공급자에게서 구매하는 경우에는 가격할인까지 기대할 수 있다. 이와 같이 하나의 공급자들 통해 다수의 품목을 구매하는 경우에 대한 최적의 구매전략을 다룬 문제가 다품목 일괄 주문모형으로 Joint Replenishment Problem(JRP)으로 잘 알려져 있으며 지난 수십 여년간 많은 연구가 이루어진 생산재고관리 영역의 문제들과는 달리 일반적인 JRP를 해결하기 위한 발견적 기법들에 대한 연구는 수없이 많은 반면 JRP를 현실적으로 확장한 연구는 국내외적으로 전무한 형편이다. 본 연구에서는 일반적인 JRP를 제약식을 고려한 문제로 확장하여 유전자 알고리즘을 이용한 해법을 개발하고 이 문제의 확장 가능성에 대해 소개하고자 한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.11a
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• pp.1361-1364
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• 2012

현대 산업 발전에 따라 대량 생산 체제에서 소비자 개별 주문 요건을 반영하여 생산하는 대량맞춤 생산(Mass Customization) 체제로 변화해가고 있다. 이런 대량맞춤 생산 체제의 제품구성(Product Configuration) 시에 나타나는 CSP(Constraint Satisfaction Problem) 해결 방법론을 제시하고 있다. 기존 방법에서는 복잡 다양한 제약조건을 수학적으로 표현하여 제약 조건 검사에서 소요시간과 검색 공간을 최소화 할 수 있는 알고리즘이 제시했다. 그런데 소비자의 주문 요건들이 빠르게 변화되고 복잡 다양 해지고 있다. 그래서 대상 모델 및 제약조건이 변경이 되었을 경우 기존 방법에서 제시하고 있는 CSP 해결에서는 수정 관리 하기가 쉽지 않고 확장성이 낮다. 본 연구에서는 룰 기반 모델링으로 CSP 문제 해결 프로세스 정립을 하여 관리의 편리성을 제공하고 확장성을 향상시킨다.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.177-190
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• 2021

Due to the development of the fourth industrial revolution technology, efforts are being made to improve areas that humans cannot handle by utilizing artificial intelligence techniques such as machine learning. Although on-demand production companies also want to reduce corporate risks such as delays in delivery by predicting total production time for orders, they are having difficulty predicting this because the total production time is all different for each order. The Theory of Constraints (TOC) theory was developed to find the least efficient areas to increase order throughput and reduce order total cost, but failed to provide a forecast of total production time. Order production varies from order to order due to various customer needs, so the total production time of individual orders can be measured postmortem, but it is difficult to predict in advance. The total measured production time of existing orders is also different, which has limitations that cannot be used as standard time. As a result, experienced managers rely on persimmons rather than on the use of the system, while inexperienced managers use simple management indicators (e.g., 60 days total production time for raw materials, 90 days total production time for steel plates, etc.). Too fast work instructions based on imperfections or indicators cause congestion, which leads to productivity degradation, and too late leads to increased production costs or failure to meet delivery dates due to emergency processing. Failure to meet the deadline will result in compensation for delayed compensation or adversely affect business and collection sectors. In this study, to address these problems, an entity that operates an order production system seeks to find a machine learning model that estimates the total production time of new orders. It uses orders, production, and process performance for materials used for machine learning. We compared and analyzed OLS, GLM Gamma, Extra Trees, and Random Forest algorithms as the best algorithms for estimating total production time and present the results.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.11a
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• pp.246-251
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• 2003

공급체인관리는 개별적 기업 에이전트(마케팅, 자재, 구매, 생산, 유통등)를 통합하여 운용하도록 하는데, 이러한 통합은 한 기업에서 국한되는 것이 아니라 공급자로부터 소비자까지의 공급체인의 모든 에이전트에게 적용된다. 공급체인의 에이전트들 간의 통합이 실질적으로 효과를 나타내기 위해서는 상호 정보 공유 및 조정된 방법으로 운영되어야 한다. 전자거래에 대한 요구가 증대되면서 새로운 변화에 유연하게 대처하고 예측하지 못했던 상황, 즉 Order에 대한 주문량 변경/취소, 새로운 고객 등장(긴급을 요구하는 Project등)으로 새로운 Order 생성, 계절적 요인으로 인한 주문량 폭주, Event에 의한 특정 상품에 대한 Cost Down 및 Cost 복원, 원자재/제품의 불량처리에 대한 Delivery 일정 수정 등과 같은 사건에 능동적으로 대처할 수 있도록 공급체인 내에서 Intelligent 기능을 가진 Coordination Agent의 도입이 요구되고 있다. 따라서 본 연구에서는 전자거래와 공급체인을 연계한 e-SCM에서의 조정 에이전트 시스템을 구현하고 Agent의 Coordination 능력을 분석한다.

• Journal of the Korean Society of Clothing and Textiles
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• v.31 no.12
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• pp.1710-1720
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• 2007

Environmental changes, including intensive international competition, unpredictable consumer demand, and market trends of variety and short product life cycles, have compelled the U. S. textiles and apparel industry to focus increasingly on the consumer as a way to meet these challenges. The industry began expanding into mass customization that used information technology, flexible processes, and organizational structures to deliver a wide range of products and services that met specific needs of individual customers but on a mass scale. This paper presents cases of leading-edge technology application on customization and breakthrough concepts in personalization, with a view to raising the level of debate on these issues to its highest level.

• The KIPS Transactions:PartD
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• v.10D no.1
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• pp.161-166
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• 2003

Information lead time is defined as the time spent by processing orders from some buyers, whereas order lead time is defined as producing and supplying the products. The information lead time significantly serve to magnify the increase in variability due to demand forecasting. This paper models a decentralized supply chain composed of cascade type which has four type phases (or divisions) such as retailer, wholesaler, distributor, and factory. Each phases is managed by different centers individually with their own local inventory information. We investigate whether each phase's Information lead time affects companies networked a value chain. In particular, on several experiments performed with a programmed simulation (like a MIT beer game), we study the following question ; Can information lead times do better than material lead times in cost-benefit perspective\ulcorner Can more much Information lead times in downstream reasonably do worser than in upstream when playing the simulation\ulcorner In the conclusion, we show the importance of information lead time on a SC and, besides, guarantee that improvement of information lead time in upstream do more effective than one in downstream in cost-benefit perspective.

• 한국경영정보학회:학술대회논문집
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• 2007.11a
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• pp.521-526
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• 2007

농업인의 소득증대와 직거래 기반 조성을 위한 농산물 전자상거래는 99년부터 농림부 및 각 산하기관과 지방자치단체를 중심으로 진행되어 왔으나 각 추진기관별로 쇼핑몰 플랫폼이 다르고 개별 운영되고 있어 정보의 공유와 소비자의 참여, 시스템의 개방이 온라인 비즈니스에서 핵심성공요인이 되고 있는 Web2.0 시대에 큰 문제점이 되고 있다. 또한, 농산물전자상거래 활성화를 위한 기존의 연구들은 대부분 B2B와 B2C를 구분하지 않고 포괄적으로 분석함으로써 농업인이 전자상거래를 수행함에 있어 겪게 되는 문제점을 제대로 파악하지 못하는 한계를 보여주고 있다. 본 연구에서는 Web2.0 시대의 비즈니스 특성을 중심으로 농업인의 전자상거래를 활성화할 수 있는 방안을 도출하였으며, 그 방안으로 각 기관별 쇼핑몰 플랫폼의 상품정보, 소비자 정보, 주문정보 등을 일괄관리하며, 친환경농산물인증정보와 GAP농산물 인증정보, 생산이력정보 등을 포괄하여 달라진 소비자의 구매성향에 대응하는 방안과 농업인의 농산물 마케팅 방도의 변화 및 정부기관의 대농업인 전자상거래 지원방식의 전환방안을 제시하고 있다.

• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.891-899
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• 2024

The Fourth Industrial Revolution is centered on a personalized demand fulfillment economy and is all about transformation and flexible processing that can deliver what customers want in real time across space and time. This paper implements the construction and operation of a packaging platform that can instantly procure the required packaging products based on real-time orders and evaluates its performance. The components of customer satisfaction are flexible and dependent on the situation which requires efficient management of enterprise operational processes based on an e-SCM platform. An OMS optimized for these conditions plays an important role in maximizing and differentiating the efficiency of a company's operations and improving its cost advantage. OMS is a system of mass customization that provides efficient MOT(Moment of Truth) logistics services to meet the eco-friendly issues of many individual customers and achieve optimized logistics operation goals to enhance repurchase intentions and sustainable business. OMS precisely analyzes the collected data to support information and decision-making related to efficiency, productivity, cost and provide accurate reports. It uses data visualization tools to express data visually and suggests directions for improvement of the operational process through statistics and prediction analysis.