• Title/Summary/Keyword: Industrial demand

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Heuristic for Distribution Planning in Capacitated Multi-echelon Supply Chains (생산 능력 제한이 있는 다계층 공급사슬의 분배계획을 위한 발견적 기법)

  • Kwon, Ick-Hyun;Shin, Hyun-Joon;Kim, Sung-Shick
    • IE interfaces
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    • v.19 no.2
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    • pp.124-132
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    • 2006
  • The system under study is a single item, multi-echelon distribution system with a capacitated production facility. All the nodes at the downstream ends are demand-sites, i.e., ordered items are delivered to the customers from the node. Also any transshipment depots in the midstream can be demand-sites as well. For a given planning period, at each of demand-site, demand is forecasted and known. Our objective is to minimize the average system cost per period which is the sum of holding and backorder costs in the entire network. Due to the capacity restrictions, it is difficult to establish efficient distribution planning. To overcome such a difficulty and obtain a reasonable and better solution, we convert this problem into a single machine earliness and weighted tardiness scheduling. We propose a simple but cost-effective heuristic for this problem. The experimental results showed that the proposed heuristic obtained much better solutions compared with another approach.

The Characteristics and Perspectives of Industrial Technology Labor-force by Technology Intensities in Korean Manufacturing (기술집약도별 산업기술인력 수급구조의 특징과 정책적 시사점)

  • Hong, Seong-Min;Jang, Seon-Mi
    • Journal of Technology Innovation
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    • v.16 no.2
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    • pp.201-223
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    • 2008
  • This paper studies the supply and demand of Industrial Technology Labor-force(ITL) and analyzes the determinate of ITL shortage in Korean manufacturing. We classified the industry into four categories-high technology industries, medium-high technology industries, medium-low technology industries and low technology industries-based on its R&D intensity like OECD. For the empirical analyses we use a survey data collected from 5,703 enterprises. The key findings are as follows: Firstly, a large majority of ITL is engaged in more technology-intensive industries but the categories that are exposed to more serious labor-force shortage problem are medium-high technology industries and low technology industries. Secondly, in the terms of supply factor, the ITL shortage problems are mainly due to the avoidance of ITL jobs. And the demand point, the reason is that the most of ITL are not researchers but production managers. Thirdly, the cause of imbalance between supply and demand of ITL are different by the technological categories. For example, in the high technology industries, the supply factors, such as average wage and turnover rate played more important role in the imbalance. But in the low technology industries the demand factors, such as per capita sales and the ratio of ITL in all employees were relatively much more important. Based on the findings, we discovered some political meanings such as the necessity to plan various policies to resolve the shortage problem of ITL according to the technological categories, etc.

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Analysis of the Recall Demand Pattern of Imported Cars and Application of ARIMA Demand Forecasting Model (수입자동차 리콜 수요패턴 분석과 ARIMA 수요 예측모형의 적용)

  • Jeong, Sangcheon;Park, Sohyun;Kim, Seungchul
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.43 no.4
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    • pp.93-106
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    • 2020
  • This research explores how imported automobile companies can develop their strategies to improve the outcome of their recalls. For this, the researchers analyzed patterns of recall demand, classified recall types based on the demand patterns and examined response strategies, considering plans on how to procure parts and induce customers to visit workshops, recall execution capacity and costs. As a result, recalls are classified into four types: U-type, reverse U-type, L- type and reverse L-type. Also, as determinants of the types, the following factors are further categorized into four types and 12 sub-types of recalls: the height of maximum demand, which indicates the volatility of recall demand; the number of peaks, which are the patterns of demand variations; and the tail length of the demand curve, which indicates the speed of recalls. The classification resulted in the following: L-type, or customer-driven recall, is the most common type of recalls, taking up 25 out of the total 36 cases, followed by five U-type, four reverse L-type, and two reverse U-type cases. Prior studies show that the types of recalls are determined by factors influencing recall execution rates: severity, the number of cars to be recalled, recall execution rate, government policies, time since model launch, and recall costs, etc. As a component demand forecast model for automobile recalls, this study estimated the ARIMA model. ARIMA models were shown in three models: ARIMA (1,0,0), ARIMA (0,0,1) and ARIMA (0,0,0). These all three ARIMA models appear to be significant for all recall patterns, indicating that the ARIMA model is very valid as a predictive model for car recall patterns. Based on the classification of recall types, we drew some strategic implications for recall response according to types of recalls. The conclusion section of this research suggests the implications for several aspects: how to improve the recall outcome (execution rate), customer satisfaction, brand image, recall costs, and response to the regulatory authority.

Forecasting of Heat Demand in Winter Using Linear Regresson Models for Korea District Heating Corporation (한국지역난방공사의 겨울철 열수요 예측을 위한 선형회귀모형 개발)

  • Baek, Jong-Kwan;Han, Jung-Hee
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.12 no.3
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    • pp.1488-1494
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    • 2011
  • In this paper, we propose an algorithm using linear regression model that forecasts the demand of heated water in winter. To supply heated water to apartments, stores and office buildings, Korea District Heating Corp.(KDHC) operates boilers including electric power generators. In order to operate facilities generating heated water economically, it is essential to forecast daily demand of heated water with accuracy. Analysis of history data of Kangnam Branch of KDHC in 2006 and 2007 reveals that heated water supply on previous day as well as temperature are the most important factors to forecast the daily demand of heated water. When calculated by the proposed regression model, mean absolute percentage error for the demand of heated water in winter of the year 2006 through 2009 does not exceed 3.87%.

A Case study on geriatric dental hygiene and practical education courses based on industry demand (산업체 수요기반의 노인치위생학 및 실습 교육과정 운영 사례 연구)

  • Yong-Keum Choi;Ji-Hye Yun
    • Journal of Korean Dental Hygiene Science
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    • v.6 no.2
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    • pp.141-150
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    • 2023
  • Background: This study was conducted to verify the effectiveness of geriatric dental hygiene education by developing and operating an industrial demand-based curriculum for geriatric dental hygiene. Methods: Wilcoxon signed rank test was performed to verify the before-and-after differences in major competency achievement, geriatric dental hygiene awareness, and class satisfaction according to industrial demandbased field-oriented practical education, and Spearman's correlation analysis was performed to confirm the association between each factor(p<0.05). Results: In the case of major competency achievement, 'communication competence with the older adults' was significantly improved(p=0.031) after conducting industrial demand-based field-oriented practical training. Conclusion: It is believed that the understanding of the older adults and the practical skills for oral care of the older adults can be further developed when the learners are provided with a practical curriculum that can be used in the geriatric industrial field.

Forecasting Daily Demand of Domestic City Gas with Selective Sampling (선별적 샘플링을 이용한 국내 도시가스 일별 수요예측 절차 개발)

  • Lee, Geun-Cheol;Han, Jung-Hee
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.16 no.10
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    • pp.6860-6868
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    • 2015
  • In this study, we consider a problem of forecasting daily city gas demand of Korea. Forecasting daily gas demand is a daily routine for gas provider, and gas demand needs to be forecasted accurately in order to guarantee secure gas supply. In this study, we analyze the time series of city gas demand in several ways. Data analysis shows that primary factors affecting the city gas demand include the demand of previous day, temperature, day of week, and so on. Incorporating these factors, we developed a multiple linear regression model. Also, we devised a sampling procedure that selectively collects the past data considering the characteristics of the city gas demand. Test results on real data exhibit that the MAPE (Mean Absolute Percentage Error) obtained by the proposed method is about 2.22%, which amounts to 7% of the relative improvement ratio when compared with the existing method in the literature.

Minimum LQI based On-demand Routing Protocol for Sensor Networks (Minimum LQI 기반의 On-demand 센서 네트워크 라우팅 프로토콜)

  • Lee, Wan-Jik;Lee, Won-You;Heo, Seok-Yeol
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.10 no.11
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    • pp.3218-3226
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    • 2009
  • A number of on-demand routing protocols for sensor networks have been proposed yet. However, the majority of proposed on-demand routing protocols for sensor networks are not suitable for a relatively poor wireless environment and sensor applications requiring reliable data transmission due to using a hop-count metric for their protocols. In this paper, we proposed a minimum LQI(Link Quality Indicator) based on-demand sensor network routing protocol that is suitable for a relatively poor wireless environment and implemented the proposed routing protocol on a TinyOS. We also compared the implemented protocol with typical hop count based routing protocol by carrying out performance experiments on a multi-hop testbed. The results from these experiments showed that the successful transmission rate of the proposed routing protocol is higher than that of typical hop count based routing protocol over a poor wireless link.

A Producer's Allocation Policy Considering Buyers' Demands in the Supply Chain (공급사슬에서의 구매자의 수요를 고려한 생산자의 제품 할당 정책)

  • Eum, Seung Chul;Lee, Young Hae;Jung, Jung Woo
    • Journal of Korean Institute of Industrial Engineers
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    • v.31 no.3
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    • pp.210-218
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    • 2005
  • In the current global business environment, it is very important how to allocate products from the producer to buyers (or distributors). Sometimes some buyers can order more than pertinent demand due to inappropriate forecasting customers' orders. This is the big obstacle to the efficient allocation of products. If the producer can become aware of buyers' pertinent demand, it is possible to realize the high-level order fulfillment through the effective allocation of products. In this study, a new allocation policy is proposed considering buyers' demands. The backpropagation algorithm, one of algorithms in neural network theory, is used to find pertinent demands from the distributors' orders. In the experiment, an allocation policy considering buyers' demands outperforms previous allocation policies.

Improvement of the Load Forecasting Accuracy by Reflecting the Operation Rates of Industries on the Consecutive Holidays (특수일 조업률 반영을 통한 전력수요예측 정확도 향상)

  • Lim, Nam-Sik;Lee, Sang-Joong
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.65 no.7
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    • pp.1115-1120
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    • 2016
  • This paper presents the daily load forecasting for special days considering the rate of operation of industrial consumers. The authors analyzed the power consumption pattern for both the special and ordinary days according to the contract power classification of industrial consumers, and selected 400~600 specific consumers for which the rates of operation during special days are needed. Load forecasting for 2014 special days considering the rate of operation of industrial consumers showed a noticeable improvement on forecasting error of daily peak demand, which proved the effectiveness of the survey for the rates of operation during special days of industrial consumers.