• Journal of Korean Institute of Industrial Engineers
• /
• v.31 no.1
• /
• pp.27-35
• /
• 2005

Due to the importance of lead time demand in the design of inventory management systems, researchers and practitioners have paid continuous attention and a few analytic models using the compound distribution approach have been reported. However, since the nature of compound distributions is hardly amenable, the analytic models have been done by non‐recognition of the compound nature of some components to reduce the analytic task. This study concerns some of the important aspects in the analytic models. Through the theoretic examination of the analytic model approach and the comparison with the rigid compound stochastic process approach, this study clarifies the assumptions implicitly made by the analytic models and provides some precautions in using the analytic models. Illustrative examples are also presented.

• Journal of Korean Institute of Industrial Engineers
• /
• v.5 no.1
• /
• pp.59-66
• /
• 1979

The objective of this paper is to develop a stochastic inventory system model under the so-called continuous-review policy with a Poisson one-at-a-time demand process, iid customer inter-arrival times {Xi}, backorders allowed, and constant procurement lead time $\gamma$. The distributions of the so-called inventory position process {$IP_{(t-r)}$} and lead time demand process {$D_{(t-r,t)}$} are formulated in terms of cumulative demand by time t, {$N_t$}. Then, for the long-run expected average annual inventory cost expression, the "ensemble" average is estimated, where the cost variations for stock ordering, holding and backorders are considered stationary.

• IE interfaces
• /
• v.18 no.3
• /
• pp.343-349
• /
• 2005

This study presents the empirical results of comparing several demand forecasting methods for Supply Chain Management(SCM). Adaptive exponential smoothing using change detection statistics (Jun) is compared with Trigg and Leach's adaptive methods and SAS time series forecasting systems using weekly SCM demand data. The results show that Jun's method is superior to others in terms of one-step-ahead forecast error and eight-step-ahead forecast error. Based on the results, we conclude that the forecasting performance of SCM solution can be improved by the proposed adaptive forecasting method.

• Journal of Korean Institute of Industrial Engineers
• /
• v.31 no.3
• /
• pp.228-239
• /
• 2005

In this paper, we propose a new conceptual HVRPCR(Heterogeneous Fleet Vehicle Routing Problem with Customer Restriction) model including additional restrictions that are consisted of loadage limit and possible visit number of demand post in HVRP. We propose HVRPCR algorithm using the heuristic in order to solve speedily because VRP is NP-Hard and need many solution time. The proposed model is simulated with changing demand post location, demand weight, loading and possible visit number limitation. Results of the computational experiment are provided along with some analysis like travel cost reduction rate.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.38 no.1
• /
• pp.110-117
• /
• 2015

Spare part management is very important to products that have large number of parts and long lifecycle such as automobile and aircraft. Supply chain must support immediate procurement for repair. However, it is not easy to handle spare parts efficiently due to huge stock keeping units. Qualified forecasting is the basis for the supply chain to achieve the goal. In this paper, we propose an agent based modeling approach that can deal with various factors simultaneously without mathematical modeling. Simulation results show that the proposed method is reasonable to describe demand generation process, and consequently, to forecast demand of spare parts in long-term perspective.

• Journal of Applied Reliability
• /
• v.3 no.1
• /
• pp.13-40
• /
• 2003

The seasonal adjustment is an essential process in analyzing the time series of economy and business. One of the powerful adjustment methods is X11-ARIMA Model which is popularly used in Korea. This method was delivered from Canada. However, this model has been developed to be appropriate for Canadian and American environment. Therefore, we need to review whether the X11-ARIMA Model could be used properly in Korea. In this study, we have applied the method to the annual sales of refrigerator sales in A electronic company. We appreciated the adjustment by result analyzing the time series components such as seasonal component, trend-cycle component, and irregular component, with the proposed method. Additionally, in order to improve the result of seasonal adjusted time series, we suggest the demand forecasting method base on autocorrelation and seasonality with the X11-ARIMA PROC.

• Journal of Korean Institute of Industrial Engineers
• /
• v.24 no.4
• /
• pp.559-569
• /
• 1998

Cellular manufacturing system (CMS) has been recognized as an alternative to improve manufacturing productivity in conventional batch-type manufacturing systems through reducing set-up times, work-in-process inventories and throughput times by means of group technology. Most of the studies on the design of CMS assumed that each part has a unique process plan, and that its demand is known as a deterministic value despite of the probabilistic nature of the real world problems. This study suggests an approach for designing CMS, considering both alternative process plans and uncertain demand. A mathematical model is presented to show how to minimize the expected amortized and operating costs satisfying these two relaxations. Four heuristic algorithms are developed based on tabu search which is well suited for getting an optimal or near-optimal solution. Example problems are carried out to illustrate the heuristic algorithms and each of them is compared with the deterministic counterpart.

• IE interfaces
• /
• v.22 no.2
• /
• pp.174-184
• /
• 2009

As R&D environment was changed, the research through Cyberinfrastructure emerged as a new paradigm. Advanced countries such as U.S. and U.K. invested heavily in the system for advancing R&D by building national Cyberinfrastructure. It is required to analyze how to build and utilize Cyberinfrastructure in science and technology. This paper aims to investigate and analyze the utilization status and demand of Cyberinfrastructure and to propose directions for operating it in Korea. This study is expected to be a principle foundation for decision making about developing it.

• Journal of Korean Institute of Industrial Engineers
• /
• v.40 no.3
• /
• pp.291-298
• /
• 2014

Demand forecasting plays a key role in overall business activities such as production planning, distribution management, and inventory management. Especially, for a fast-changing environment of the clothing industry, logical forecasting techniques are required. In this study, we propose a procedure to predict product life cycle using data mining algorithms. The proposed procedure involves three steps : extracting key variables from profiles, clustering, and classification. The effectiveness and applicability of the proposed procedure were demonstrated through a real data from a leading clothing company in Korea.

• Industrial Engineering and Management Systems
• /
• v.13 no.3
• /
• pp.304-318
• /
• 2014

It is necessary for a retailer to improve responsiveness to uncertain customer demand in product sales. In order to solve this problem, this paper discusses an optimal operation for a 2-stage-ordering-production system consisting of a retailer and a manufacturer. First, based on the demand information estimated at first order time $t_1$, the retailer determines the optimal initial order quantity $Q^*_1$, the optimal advertising cost $a^*_1$ and the optimal retail price $p^*_1$ of a single product at $t_1$, and then the manufacturer produces $Q^*_1$. Next, the retailer updates the demand information at second order time $t_2$. If the retailer finds that $Q^*_1$ dissatisfies the demand indicated by the demand information updated at $t_2$, the retailer determines the optimal second order quantity $Q^*_2$ under $Q^*_1$ and adjusts optimally the advertising cost and the retail price to $a^*_2$ and $p^*_2$ at $t_2$. Here, decision-making approaches for two situations are made-a decentralized supply chain (DSC) whose objective is to maximize the retailer's profit and an integrated supply chain (ISC) whose objective is to maximize the whole system's profit. In the numerical analysis, the results of the optimal decisions under DSC are compared with those under ISC. In addition, supply chain coordination is discussed to adjust the unit wholesale price at each order time as Nash Bargaining solutions.