• 제목/요약/키워드: Dynamic Supply Chain

검색결과 89건 처리시간 0.03초

SCM 기업들의 IT전략이 IT투자와 경영성광에 미치는 영향 (Investigating the Impacts of IT Strategy on IT Investment and Management Performance in SCM Companies)

  • 김종원;김은정
    • 한국산업정보학회논문지
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    • 제14권2호
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    • pp.59-71
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    • 2009
  • 급변하는 경영환경 속에서 기엽이 지속적인 경쟁우위를 획득하기 위해서는 성공적인 정보기술전략과 효율적인 정보기술 투자를 위한 투자성과의 체계적인 관리가 필요하다. 따라서 본 연구에서는 정보기술전략인 운영지향성과 시장지향성이 정보기술 투자방향 및 프로세스 혁신역량과 기엽의 비재무적 성과와 재무적 성과에 미치는 영향을 분석하고자 한다. 본 연구의 실증연구에서는 SCM을 실행하고 있는 기업을 대상으로 설문조사를 실시하여 온라인과 오프라인을 통해 자료를 수집하였으며, 최종적으로 82개 의 표본을 분석에 사용하였다.

e-비즈니스의 유통기업 성장성 및 수익성 기여 효과분석 (The Effect of E-Business on Firm's Growth and Profitability in the Distribution Industry)

  • 백철우
    • 유통과학연구
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    • 제15권1호
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    • pp.123-130
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    • 2017
  • Purpose - This research aims to examine the effect of e-business adoption on firm's growth and profitability in the distribution industry. The value added from the distribution industry acts as the cost of other industries. As the distribution industry develops, its stage becomes shorter and the distribution margin becomes smaller. Therefore, e-business is expected to have a different effect on the distribution industry than other industries. Research design, data and methodology - The previous research generally used e-business adoption as an independent variable and firm's performance as a dependent variable. This study elaborated the model using a dynamic panel model that includes the performance variable of the previous year as an independent variable. By employing system GMM (Generalized Method of Moments), the endogeneity problem in the dynamic panel model can be solved. For the analysis, I extracted the distribution companies as the raw data in the National Statistical Office's Business Activity Survey over the period 2006 to 2012. Results - The growth rate of firms adopting e-business was 0.299%p higher than that of the non-adopter. However, only ERP (Enterprise Resource Planning), KMS (Knowledge Management System) and SCM (Supply Chain Management) contributed positively to the growth rate. In the case of profitability, it was 0.04%p higher than the distribution companies that did not adopt e-business. ERP and LMS (Learning Management System) improve profitability, while SCM reduces profitability. Consequently, while ERP improves both growth and profitability, SCM improves growth but reduces profitability. In addition, KMS improves firm's growth only, and LMS does only profitability, showing that each e-business has a differentiated effect. Conclusions - Since the distribution industry has different characteristics from manufacturing and other service industries, the introduction of e-business may not guarantee the growth and profitability of distribution companies. Careful introduction considering the characteristics of the distribution industry is required. In particular, it is necessary to select an e-business meeting the characteristics and needs of a distribution company, and thereafter, it is required for the company's own efforts to internalize it within the system.

SCM 성과 결정요인에 관한 통합적 연구: 공급업체 관점으로 (Studies on Determinant Factors of SCM Performance: From the Supplier Perspective)

  • 박광오;장활식
    • Asia pacific journal of information systems
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    • 제21권1호
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    • pp.1-27
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    • 2011
  • In an attempt to cope with widespread, dynamic, and accelerating changes in both internal and external business environments, companies often utilize information technologies such as SCM(Supply Chain Management). To date, SCM research has mainly focused on the effects of dynamic factors on SCM success and emphasized adoption strategies and critical success factors. Consequently, the effects of more static factors such as interdependency between SCM partners have been largely ignored. The purpose of this study, therefore, is to examine the effects of both dynamic and static factors on SCM performance by controlling for information quality and partnership quality. The five factors examined in this study include innovative ness, mutual dependency, quality of information, partnership quality, and SCM performance. All factors were examined from the perspective of part suppliers, except the mutual dependency which was examined from two aspects: supplier's dependency on customer and customer's dependency on supplier. Data was collected through five hundred survey questionnaires distributed to the part supplier companies that have implemented SCM systems for at least one year. As a result, a total of 170 valid responses were obtained. A structural equation research model was fitted using SAS 9.1.3 and SMART-PLS 2.0. The results of this study can be summarized as follows. First, innovativeness positively affected SCM information quality. SCM partnership quality, and ultimately SCM performance. The path coefficient between innovativeness and information quality was 0.387, with a t-value of 3.528. Innovativeness also had a positively direct effect on partnership quality. The path coefficient was 0.351 with a t-value of 3.366. The total effect of innovativeness on partnership quality was significant, although its indirect effect on partnership quality by altering information quality was negligible. The total indirect effect of innovativeness on SCM performance by affecting information quality and partnership quality was significant with a p-value of 0.014. Innovativeness played an important role in determining SCM performance. Second, mutual dependency showed no significant effect on SCM information quality. This result contradicts the earlier assertion that the more dependent two companies are, the more accurate and timely the information they exchange ought to be. This study showed that this may not be the case; a partner may provide information of poor quality even when it is strongly dependent on the other. Mutual dependency showed significant effect on partnership quality. However, when the mutual dependency perceived by suppliers was divided into two parts, one being a supplier's dependency on its customer company and the other being a customer's dependency on the supplier, the latter showed a significant impact on the perceived SCM partnership quality. This result indicates that a customer company can hardly improve the partnership quality perceived by suppliers by making them more dependent. It improves only when the suppliers perceive that their partners, typically having more bargaining power, are more dependent on them. The overall effect of mutual dependency of any kind on SCM performance, however, was not significant. Although mutual dependency has been mentioned as an important static factor influencing almost every aspect of cooperation on a supply chain, its influences may not be as significant as it was initially perceived to be. Third, the correlation between information quality and partnership quality was 0.448 with a p-value of less than 0.001. Information quality had a path coefficient of 0.256 to partnership quality with a t-value of 2.940. The quality of information exchanged between partners may have an impact on their partnership quality. Fourth, information quality also had a significant impact on SCM performance with a path coefficient of 0.325 with a t-value of 3.611. In this study, SCM performance was divided into four categories: product quality, cost saving, service quality, and order fulfillment. Information quality has Significant impacts on product quality, cost saving and service quality, but not on order fulfillment. Fifth, partnership quality, as expected, had a significant impact on SCM performance. The path coefficient was 0.403 with a t-value of 3.539. Partnership quality, like information quality, had positive impacts on product quality, cost saving and service quality, but showed no impact on order fulfillment. It seemed that order fulfillment is the hardest category of performance that SCM can satisfy. One major limitation of this study is that it surveyed only the suppliers. To better understand the dual aspects of SCM, it is important to survey both suppliers and the assemblers, especially in pairs. This research, to our best knowledge, was the first attempt to study the level of dependency between the two groups by measuring the dual aspects of SCM and studying mutual dependency from the categories of suppliers and assemblers each.. In the future, a more comprehensive and precise measurement of SCM characteristics needs to be achieved by examining from both the supplier's and assembler's perspectives.

유전 알고리듬을 적용한 지능형 ATP 시스템 개발 (Development of Intelligent ATP System Using Genetic Algorithm)

  • 김태영
    • 지능정보연구
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    • 제16권4호
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    • pp.131-145
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    • 2010
  • ERP, SCM 등과 같은 기업용 정보 시스템을 활용함에 있어, 고객의 문의에 따라 제품 판매 가능 유무와 가능일자를 계산하여 통보해 주는 지능형 ATP 시스템은 전산 정보를 활용하여 고객 만족도를 최대화할 수 있는 유용한 기능이라고 할 수 있다. 그렇지만 공급 사슬 환경에서 ATP 시스템을 적용하려고 할 경우, 고객이 문의해 온 Retailer에게 납품 가능한 모든 분배센터(Distribution Center)와 공장(Plant)의 미래 시점의 재고량 변화와 운송 능력 등을 모두 고려하여야 하므로 계산량이 방대한 NP-Complete 문제가 된다. 따라서 시스템 사용자가 빠른 시간 내에 해를 구하여 고객에게 결과를 알려 줄 수 있는 ATP 시스템의 개발은 공급 사슬 관리를 효과적으로 활용하기 위하여 반드시 필요한 일이라고 할 수 있다. 본 논문에서는 동적 생산 함수의 개념을 이용하여 비 정수 타임 랙을 고려하여 ATP 시스템을 모델링하고, 해당 수리 모형으로부터 효율적으로 해를 얻기 위하여 유전 알고리듬을 개발하였다. 비 정수 타임 랙을 활용한 ATP 시스템은 비 정수 타임 랙을 올림이나 내림을 통하여 정수화 시킨 후 모형 수립하는 기존의 방법보다 정교하게 현실을 반영할 수 있고, ATP 시스템을 위한 유전 알고리듬의 진화 시스템은 문제크기가 작은 것에서부터 큰 것까지 최적해에 매우 근사한 값을 매우 빠른 시간 내에 풀 수 있음을 알 수 있었다.

DC와 DC의 상호작용을 고려한 분배망 분석 기법 (A Dynamic Analysis of Distribution Network for SCP)

  • 나윤지;고일석;조동욱
    • 정보처리학회논문지D
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    • 제10D권7호
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    • pp.1207-1212
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    • 2003
  • 전자상거래의 발전과 함께 물류유통을 위한 분배망의 구성은 점차 복잡해지고 있으며, 이에 따라 분배계획이 점차 중요해지고 있다. 분배계획에 있어서 분배망의 관리는 중요하다. 분배망은 분배센터(DC)와 그 상호작용으로 나타낼 수 있다. DC 내부요인 및 DC의 상호작용으로 인한 외부요인은 분배망에 많은 영향을 미친다. 따라서 효율적인 분배망 관리계획의 수립을 위해서는 DC와 DC의 상호작용을 고려한 분배망의 분석이 필요하다. 지금까지 자원할당 같은 공급망 관리 관점의 연구가 이루어졌지만 공급망을 구성하는 분배망의 분석에 대한 연구가 이루어지지 않았다. 본 논문은 DC와 DC의 상호작용을 고려한 분배망 분석 기법을 제안한다. 제안 기법은 크게 두 가지 단계로 이루어진다. 먼저, 분배망은 DC와 DC의 상호작용을 포함한 그래프 형태로 모델링된다. 두 번째, 그래프로 모델링된 분배망은 도달성 트리를 이용하여 분석된다. 또한 예제모델을 제시하고, 이것의 분석을 통해 제안기법의 효용성을 보였다.

제조 기업의 대응성 향상을 위한 로제타넷 기반 비즈니스 통합 모델 (RosettaNet based Business Integration Model for Enhancing Manufacturing Firm's Responsiveness)

  • 박목민;박정호;신기태;박진우
    • 한국전자거래학회지
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    • 제15권1호
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    • pp.89-101
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    • 2010
  • 치열한 글로벌 경쟁에서 기업의 경쟁력이 제품 측면에서 점차 서비스 측면으로 옮겨가고 있다. 이는 치열한 경쟁 상황 등으로 인해 제품 생산 비용이나 제품의 품질에서 차별화를 확보하기 힘들어지고 수요의 불확실성과 동적인 시장 상황으로 인해 점차 유연성과 대응성 등 서비스 측면이 차별화 요인으로 대두되고 있기 때문이다. 기업의 유연성과 대응성은 기업의 판매주문처리를 통해 다른 기업들에게 평가된다. 따라서 기업은 판매주문처리에 관심을 가져야 한다. 그러나 기존의 판매주문처리 관련 연구들은 주로 생산 비용을 최소화하기 위한 납기 결정과 생산 일정 수립 및 납기 준수를 위한 리드타임 결정에 비중을 두고 있다. 이는 지금까지 판매주문처리 관련 연구들이 대상으로 하는 목적함수가 비용 관련 요소에 비중을 두고 있었으며, 유연성과 대응성 향상을 위한 고객과의 유연하면서 긴밀한 연결 비용과 실시간 주문 관련 정보 취득 비용이 높았기 때문이다. 그러나 정보 기술(IT)과 유비쿼터스 기술(UT)의 발전으로 점차 연결성과 가시성 확보와 관련한 비용이 줄었으며 기술적으로 구현 가능해지고 있다. 본 연구에서는 정보 기술과 유비쿼터스 기술의 발전으로 인해 변화된 기업의 판매주문관리에서의 유연성과 대응성 향상을 위해 납기를 재협의하는 프로세스와 이를 지원하기 위해 로제타넷 PIP(Partner Interface Process) 기반의 비즈니스 통합 모델을 제안하고자 한다.

기업 물류망 최적 설계 및 운영을 위한 알고리즘 설계 및 소프트웨어 구현 사례 (A case study on algorithm development and software materialization for logistics optimization)

  • 한재현;김장엽;김지현;정석재
    • 대한안전경영과학회지
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    • 제14권4호
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    • pp.153-168
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    • 2012
  • It has been recognized as an important issue to design optimally a firm's logistics network for minimizing logistics cost and maximizing customer service. It is, however, not easy to get an optimal solution by analyzing trade-off of cost factors, dynamic and interdependent characteristics in the logistics network decision making. Although there has been some developments in a system which helps decision making for logistics analysis, it is true that there is no system for enterprise-wise's on-site support and methodical logistics decision. Specially, E-biz process along with information technology has been made dramatic advance in a various industries, there has been much need for practical education closely resembles on-site work. The software developed by this study materializes efficient algorithm suggested by recent studies in key topics of logistics such as location and allocation problem, traveling salesman problem, and vehicle routing problem and transportation and distribution problem. It also supports executing a variety of experimental design and analysis in a way of the most user friendly based on Java. In the near future, we expect that it can be extended to integrated supply chain solution by adding decision making in production in addition to a decision in logistics.

An Application of Machine Learning in Retail for Demand Forecasting

  • Muhammad Umer Farooq;Mustafa Latif;Waseemullah;Mirza Adnan Baig;Muhammad Ali Akhtar;Nuzhat Sana
    • International Journal of Computer Science & Network Security
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    • 제23권9호
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    • pp.1-7
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    • 2023
  • Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

An Application of Machine Learning in Retail for Demand Forecasting

  • Muhammad Umer Farooq;Mustafa Latif;Waseem;Mirza Adnan Baig;Muhammad Ali Akhtar;Nuzhat Sana
    • International Journal of Computer Science & Network Security
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    • 제23권8호
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    • pp.210-216
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    • 2023
  • Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.