• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.1
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• pp.8-14
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• 2015

In this paper, even though there exist various problems to be solved in SCM field, we focus on problems arising from demand-supply discrepancy and introduce the concept of CPFR (Collaboration Planning Forecasting and Replenishment) as one solution for outstanding problems. Furthermore, by presenting a real CPFR project case between the S Company and V Company, we try to help potential benefactors of CPFR in two aspects : 1) how to implement CPFR, and 2) what they can expect from CPFR.

• Proceedings of the CALSEC Conference
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• 2001.08a
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• pp.439-448
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• 2001

Data Alignment is achieved when all trading partners information systems are maintained automatically synchronising with the suppliers information systems on a continuing basis. Electronic catalogues facilitate the ongoing synchronisation of data between trading partners and large retailers in United States and Canada use electronic catalog in order to get rid of non-value added paperwork and manual reconciliation. Data Alignment will dramatically improve the effectiveness of E-Commerce and Supply Chain initiatives including electronic Marketplaces, Collaborative Planning and Forecasting and continuous replenishment processes.

• Journal of the Korea Society for Simulation
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• v.23 no.3
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• pp.19-25
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• 2014

Demand forecasting is the basis of management activities including marketing strategy. Especially, the demand of a part is remarkably important in supply chain management (SCM). In the fields of various industries, the part demand usually has the intermittent characteristic. The intermittent characteristic implies a phenomenon that there frequently occurs zero demands. In the intermittent demands, non-zero demands have large variance and their appearances also have stochastic nature. Accordingly, in the intermittent demand forecasting, it is inappropriate to apply the traditional time series models and/or cause-effect methods such as linear regression; they cannot describe the behaviors of intermittent demand. Markov bootstrap method was developed to forecast the intermittent demand. It assumes that first-order autocorrelation and independence of lead time demands. To release the assumption of independent lead time demands, this paper proposes a modified bootstrap method. The method produces the pseudo data having the characteristics of historical data approximately. A numerical example for real data will be provided as a case study.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.04a
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• pp.661-663
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• 2000

본 논문은 수요 예측함에 있어서 여러 가지 수요 예측 방법을 통해 매 시기 마다 적절한 수요예측 기법을 사용하여 좀더 정확한 수요예측 결과를 추정하기 위한 방법을 연구하며 특히, 수요 예측하기 어려운 제품에 대해 여러 인자를 고려하여 좀더 나은 예측치를 구하기 위한 방법을 연구하고 있다. 마지막으로 각 ERP나 SCM, MRP application에 연계하여 필요한 자료를 되게 얻고 이를 다시 보내 줄 수 있는 일반적인 연계 방법을 연구하고 있다. 본 논문에서는 데이터 베이스 연계부분에서는 ODBC 를 사용하였으며, 예측 기법은 Moving Average 기법과 Exponential Smoothing 기법, 그리고 Neural Networks 중 BP 를 이용하여 구현하였다. 앞으로 좀 더 많은 예측 기법을 적용하여 향상된 수요 예측을 위한 모듈을 연구 및 구현하려 한다.

• Journal of Distribution Research
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• v.6 no.1
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• pp.22-35
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• 2001

The focus of Business Process Re-engineering (BPR) has been extended to inter-business process that cuts across independent companies. Combined with Supply Chain Management (SCM), inter-business process reengineering (IBPR) focuses on synchronization of business activities among trading partners to achieve performance improvements in inventory management and cycle time. This paper reviews the business process reengineering movement from the historical perspective and presents a case of inter-business process reengineering using the latest internet-based Business-to- Business (B2B) technology based on Collaborative Planning, Forecasting, and Replenishment (CPFR). The case demonstrates how CPFR technology reengineers the distribution process between Heineken USA and its distributors. As world's first implementor of web-based collaborative planning system, Heineken USA reduces cycle time from determining the customer need to delivery of the need by 50% and increases sales revenue by 10％. B2B commerce on the internet is predicted to grow from $90 billion in 1999 to $2.0 trillion in 2003. This paper provides the management with the bench-marking case on inter-business process reengineering using B2B e-commerce technology.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.32 no.1
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• pp.117-129
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• 2009

The bullwhip effect is known as the significant factor which causes unnecessary inventory, lost sales or cost increase in supply chains. Therefore, the causes of the bullwhip effect must be examined and removed. In this paper, we develop two analytical tools for the bullwhip effect control in supply chains. First, we develop the quantitative models for computing the bullwhip effect in a three-stage supply chain consisted of a single retailer, a single distributor and a single manufacturer when the fixed-interval replenishment policy is applied at each stage. The quantitative models are developed under the different conditions for the demand forecasting and share of customer demand information. They are validated through the computational experiments. Second, we develop a simulation-based decision support system for the bullwhip effect control in a more diverse dynamic supply chain environment. The system includes a what-if analysis function to examine the effects of varying input parameters such as operating policies and costs on the bullwhip effect.

• Journal of Distribution Science
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• v.11 no.12
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• pp.25-37
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• 2013

Purpose - The structure of retailing has changed as retailers develop markets in response to business environment changes. This study aims to analyze the general situation of retailers in order to predict future global strategy using case studies of overseas expansion strategy and the Metro Group's global strategy. Research design, data, and methodology - The backgrounds to the new retail business model and retailer classification are analyzed as theoretical data. In addition, the key success point of the Metro Group's "cash and carry" strategy is analyzed as is the Metro Group's global CFAR (collaborative planning, forecasting, and replenishment) strategy. Finally, the plan for cooperation and precise forecasting under the Metro Group's supply chain management are analyzed from the promotion environment viewpoint. Related materials analyzed included the 2012 annual report, the Metro Group's web page, and a video interview with the executive in charge of global strategy and the new market development department. Some data were revised to avoid disrupting essential aspects of the case studies. Results - The important finding was that the Metro Group could be a world-class retail company with its successful global expansion strategy. The Metro Group's global strategy's primary goal is to have a leading business position in Eastern and Western Europe. The "cash and carry" strategy is highest priority in its overseas expansion strategy. Moreover, the Metro Group has standardized product planning capacity, which could be applied in various countries with different structural and cultural backgrounds. This is the main reason that the Metro Group could rapidly become successful in the Eastern Europe and Asian markets through its structural overseas expansion strategies. In addition, the Metro Group emphasizes the importance of supply chain management. Conclusions - First, retailers should create additional value through utilizing the domestic market, market power, and economies of scale to launch a global strategy to maximize benefits from diversification. Second, the political, economic, and cultural background of the target country needs to be understood to successfully implement the overseas expansion strategy. Third, the main factor of successful cooperation with a local partner is how quickly the company gains total understanding of the business resources and core competence of its partner. All organizations should focus on the achievement of goals in order to successfully operate the partnership. Fourth, retailers should improve their business, financial and organizational structure. Moreover, the work processes and company culture should also be improved to respond strongly in the competitive global market. Fifth, the essential point of a successful retail business is the control capacity of its branding and format. The retailer could avoid forecasting errors through supply chain management by perfectly distributing the actual amount of its inventory. In addition, the risks along the supply chain are effectively shared between the supply chain partners. Finally, the central tendency of the market is to gain in strength with this taking place across all parts of the business.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.4
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• pp.218-233
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• 2022

The long-term (1986~2020) predictability of the number of days of heat and cold damages for each growth stage of soybean is evaluated using the daily maximum and minimum temperature (Tmax and Tmin) data produced by Pusan National University Coupled General Circulation Model (PNU CGCM)-Weather Research and Forecasting (WRF). The Predictability evaluation methods for the number of days of damages are Normalized Standard Deviations (NSD), Root Mean Square Error (RMSE), Hit Rate (HR), and Heidke Skill Score (HSS). First, we verified the simulation performance of the Tmax and Tmin, which are the variables that define the heat and cold damages of soybean. As a result, although there are some differences depending on the month starting with initial conditions from January (01RUN) to May (05RUN), the result after a systematic bias correction by the Variance Scaling method is similar to the observation compared to the bias-uncorrected one. The simulation performance for correction Tmax and Tmin from March to October is overall high in the results (ENS) averaged by applying the Simple Composite Method (SCM) from 01RUN to 05RUN. In addition, the model well simulates the regional patterns and characteristics of the number of days of heat and cold damages by according to the growth stages of soybean, compared with observations. In ENS, HR and HSS for heat damage (cold damage) of soybean have ranged from 0.45~0.75, 0.02~0.10 (0.49~0.76, -0.04~0.11) during each growth stage. In conclusion, 01RUN~05RUN and ENS of PNU CGCM-WRF Chain have the reasonable performance to predict heat and cold damages for each growth stage of soybean in South Korea.