• Title/Summary/Keyword: forecast supply

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A Study on Forecasting Demand and Supply of Marine Officer for Korean Ocean-Going Merchant Vessels (외항 상선 해기사 인력 수요 및 공급 예측에 관한 연구)

  • Sang-hoon Shin;Yong-John Shin
    • Journal of Navigation and Port Research
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    • v.48 no.1
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    • pp.7-16
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    • 2024
  • Although the number of ocean-going merchant ships is increasing, the number of Korean marine officers is decreasing. This manpower shortage problem is becoming more serious. This study objectively measured factors determining the demand and supply of ocean-going merchant ship officers and forecasted the exact manpower demand and supply. Demand was predicted by applying the number of ship officers required for each ship size to the number of ships forecasted. The supply was predicted by segmenting by position and age using the Markov model, reflecting increase/decrease factors such as promotion, turnover, retirement, and new entry by year. The demand for ocean-going merchant ship officers will increase from 11,638 in 2023 to 13,879 in 2030 while the supply will decrease from7,006 in 2023 to 6,426 in 2030, with the shortage expected to exceed 10,000 in 2040. This study can be used as a reference to solve the problem of manpower shortage for ocean-going merchant ship officers by improving the accuracy of predictions through objective data, scientific analysis methods, and logical reasoning.

Supply models for stability of supply-demand in the Korean pork market

  • Chunghyeon, Kim;Hyungwoo, Lee ;Tongjoo, Suh
    • Korean Journal of Agricultural Science
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    • v.49 no.3
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    • pp.679-690
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    • 2022
  • As the supply and demand of pork has become a significant concern in Korea, controlling it has become a critical challenge for the industry. However, compared to the demand for pork, which has relatively stable consumption, it is not easy to maintain a stable supply. As the preparation of measures for a supply-demand crisis response and supply control in the pig industry has emerged as an important task, it has become necessary to establish a stable supply model and create an appropriate manual. In this study, a pork supply prediction model is constructed using reported data from the pig traceability system. Based on the derived results, a method for determining the supply-demand crisis stage using a statistical approach was proposed. From the results of the analysis, working days, African swine fever, heat wave, and Covid-19 were shown to affect the number of pigs graded in the market. A test of the performance of the model showed that both in-sample error rate and out-sample error rate were between 0.3 - 7.6%, indicating a high level of predictive power. Applying the forecast, the distribution of the confidence interval of the predicted value was established, and the supply crisis stage was identified, evaluating supply-demand conditions.

Forecasting uranium prices: Some empirical results

  • Pedregal, Diego J.
    • Nuclear Engineering and Technology
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    • v.52 no.6
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    • pp.1334-1339
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    • 2020
  • This paper presents an empirical and comprehensive forecasting analysis of the uranium price. Prices are generally difficult to forecast, and the uranium price is not an exception because it is affected by many external factors, apart from imbalances between demand and supply. Therefore, a systematic analysis of multiple forecasting methods and combinations of them along repeated forecast origins is a way of discerning which method is most suitable. Results suggest that i) some sophisticated methods do not improve upon the Naïve's (horizontal) forecast and ii) Unobserved Components methods are the most powerful, although the gain in accuracy is not big. These two facts together imply that uranium prices are undoubtedly subject to many uncertainties.

Experimental Study on Cooling Load Forecast Using Neural Networks (신경회로망을 이용한 일일 냉방부하 예측에 관한 실험적 연구)

  • Shin, Kwan-Woo;Lee, Youn-Seop;Kim, Yong-Tae;Choi, Byoung-Youn
    • Proceedings of the KIEE Conference
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    • 2001.11c
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    • pp.61-64
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    • 2001
  • The electric power load during the peak time in summer is strongly affected by cooling load. which decreases the preparation ratio of electricity and brings about the failure in the supply of electricity in the electric power system. The ice-storage system and heat pump system etc are used to settle this problem. In this study, the method of estimating temperature and humidity to forecast the cooling load of ice-storage system is suggested. And also the method of forecasting the cooling load using neural network is suggested. For the simulation, the cooling load is calculated using actual temperature and humidity. The forecast of the temperature, humidity and cooling load are simulated. As a result of the simulation, the forecasted data approached to the actual data.

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The Study on Cooling Load Forecast of an Unit Building using Neural Networks

  • Shin, Kwan-Woo;Lee, Youn-Seop
    • International Journal of Air-Conditioning and Refrigeration
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    • v.11 no.4
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    • pp.170-177
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    • 2003
  • The electric power load during the summer peak time is strongly affected by cooling load, which decreases the preparation ratio of electricity and brings about the failure in the supply of electricity in the electric power system. The ice storage system and heat pump system etc. are used to settle this problem. In this study, the method of estimating temperature and humidity to forecast the cooling load of ice storage system is suggested. The method of forecasting the cooling load using neural network is also suggested. The daily cooling load is mainly dependent on actual temperature and humidity of the day. The simulation is started with forecasting the temperature and humidity of the following day from the past data. The cooling load is then simulated by using the forecasted temperature and humidity data obtained from the simulation. It was observed that the forecasted data were closely approached to the actual data.

The Forecasting Model of the Change in Food Balance and Nutrient Intake under the Economic Growth (경제성장에 따른 식품수급 및 영양소 섭취 변화의 예측 모형)

  • Lee, Jong-Mee
    • Journal of the Korean Society of Food Culture
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    • v.5 no.4
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    • pp.481-485
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    • 1990
  • This study is designed to forecast the characteristics in food consumption patterns under per capita GNP growth. Ordinary least square(OLS)method was employed as analyzing technique. Equation was $Y=a_0+a_1X$, in which X was per capita GNP and Y were Engel coefficient, food supply, energy supply, nutrient intake and ratio of self-supply of food. The result obtained indicates that the intake of nutrient such as protein and fat will be increased, and wheat, corn and legume are expected to be imported wholly due to lower ratio of self-supply, and rice will be over-supplied continually. Therefore, the relevant policy of government must be established in the field of supply and demand of food, and the research of sound national health should be done.

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Resource Demand/Supply and Price Forecasting -A Case of Nickel- (자원 수급 및 가격 예측 -니켈 사례를 중심으로-)

  • Jung, Jae-Heon
    • Korean System Dynamics Review
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    • v.9 no.1
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    • pp.125-141
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    • 2008
  • It is very difficult to predict future demand/supply, price for resources with acceptable accuracy using regression analysis. We try to use system dynamics to forecast the demand/supply and price for nickel. Nickel is very expensive mineral resource used for stainless production or other industrial production like battery, alloy making. Recent nickel price trend showed non-linear pattern and we anticipated the system dynamic method will catch this non-linear pattern better than the regression analysis. Our model has been calibrated for the past 6 year quarterly data (2002-2007) and tested for next 5 year quarterly data(2008-2012). The results were acceptable and showed higher accuracy than the results obtained from the regression analysis. And we ran the simulations for scenarios made by possible future changes in demand or supply related variables. This simulations implied some meaningful price change patterns.

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Application of ANFIS for Prediction of Daily Water Supply (상수도 1일 급수량 예측을 위한 ANFIS적용)

  • Rhee, Kyoung-Hoon;Kang, Il-Hwan;Moon, Byoung-Seok
    • Journal of Korean Society of Water and Wastewater
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    • v.14 no.3
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    • pp.281-290
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    • 2000
  • This study investigates the prediction of daily water supply, which is a necessary for the efficient management of water distribution system. ANFIS, namely artificial intelligence, is a neural network into which fuzzy information is inputted and then processed. In this study, daily water supply was predicted through an application of network-based fuzzy inference system(ANFIS) for daily water supply prediction. This study was investigated methods for predicting water supply based on data about the amount of water which supplied in Kwangju city. For variables choice, four analyses of input data were conducted: correlation analysis, autocorrelation analysis, partial autocorrelation analysis, and cross-correlation analysis. Input variables were (a) the amount of water supply, (b) the mean temperature, and (c) the population of the area supplied with water. Variables were combined in an integrated model. Data of the amount of daily water supply only was modelled and its validity was verified in the case that the meteorological office of weather forecast is not always reliable. Proposed models include accidental cases such as a suspension of water supply. The maximum error rate between the estimation of the model and the actual measurement was 18.46% and the average error was lower than 2.36%. The model is expected to be a real-time estimation of the operational control of water works and water/drain pipes.

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Quantifying the Bullwhip Effect in a Supply Chain Considering Seasonal Demand (공급사슬에서 계절적 수요를 고려한 채찍효과 측도의 개발)

  • Cho, Dong-Won;Lee, Young-Hae
    • Journal of Korean Institute of Industrial Engineers
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    • v.35 no.3
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    • pp.203-212
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    • 2009
  • The bullwhip effect refers to the phenomenon where demand variability is amplified when one moves upward a supply chain. In this paper, we exactly quantify the bullwhip effect for cases of seasonal demand processes in a two-echelon supply chain with a single retailer and a single supplier. In most of the previous research, some measures of performance for the bullwhip effect are developed for cases of non-seasonal demand processes. The retailer performs demand forecast with a multiplicative seasonal mixed model by using the minimum mean square error forecasting technique and employs a base stock policy. With the developed bullwhip effect measure, we investigate the impact of seasonal factor on the bullwhip effect. Then, we prove that seasonal factor plays an important role on the occurrence of the bullwhip effect.

Selection and Analysis of Operating Parameters for Condition Monitoring of Emergency Diesel Generator at Nuclear Power Plant (원자력발전소 비상디젤발전기 상태감시를 위한 운전인자 선정에 관한 연구)

  • Park, J.H.;Choi, K.H.;Lee, S.G.;Park, J.E.
    • Journal of Power System Engineering
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    • v.11 no.3
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    • pp.3-8
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    • 2007
  • The emergency AC power supply system of the nuclear power plant is designed to supply the power to the nuclear reactor at the emergency operating condition. The safety function of the diesel generator at the nuclear power plant is to supply AC electric power to the plant safety system whenever the preferred AC power supply is unavailable. The reliable operation of onsite emergency diesel generator should be ensured by a conditioning monitoring system designed to maintain and monitor and forecast the reliability level of diesel generator. To do this kind of diesel generator condition monitoring we reviewed several operating factors and history of the wolsong unit 3 diesel generator and selected the proper conditioning monitoring operating factors.

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