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Method for Predicting Raw Material Prices for Product Production over Long Periods

Title
Method for Predicting Raw Material Prices for Product Production over Long Periods
Authors
LEE, CHI JOOWon, Jong-SungLEE, EUL BUM
POSTECH Authors
LEE, EUL BUM
Date Issued
Jan-2019
Publisher
ASCE-AMER SOC CIVIL ENGINEERS
Abstract
A construction company may invest capital and participate in a special purpose company (SPC) for financing a plant project if high profitability is expected from product production after completion. Thus, the construction company should decide whether to invest on the basis of production costs as well as construction costs. The impact of production costs on profitability is especially large, because products are produced over a long period. This study proposes a method for predicting raw material prices with the aim of contributing to more accurate predictions of profitability. The prediction method is a multivariate time series analysis and the prediction target in this study is the price of iron ore, which is the largest contributor to the price of raw materials for steel products. Following established practices and previous studies, the accuracy of the prediction results was compared with past average values over a specified period. The proposed method was found to be more than 2.3 times more accurate than past average values. The proposed method was applied to predicting the price of iron ore in this study, but for the improvement of prediction accuracy the method may apply to other raw material prices that do not use a statistical method for prediction.
A construction company may invest capital and participate in a special purpose company (SPC) for financing a plant project if high profitability is expected from product production after completion. Thus, the construction company should decide whether to invest on the basis of production costs as well as construction costs. The impact of production costs on profitability is especially large, because products are produced over a long period. This study proposes a method for predicting raw material prices with the aim of contributing to more accurate predictions of profitability. The prediction method is a multivariate time series analysis and the prediction target in this study is the price of iron ore, which is the largest contributor to the price of raw materials for steel products. Following established practices and previous studies, the accuracy of the prediction results was compared with past average values over a specified period. The proposed method was found to be more than 2.3 times more accurate than past average values. The proposed method was applied to predicting the price of iron ore in this study, but for the improvement of prediction accuracy the method may apply to other raw material prices that do not use a statistical method for prediction.
A construction company may invest capital and participate in a special purpose company (SPC) for financing a plant project if high profitability is expected from product production after completion. Thus, the construction company should decide whether to invest on the basis of production costs as well as construction costs. The impact of production costs on profitability is especially large, because products are produced over a long period. This study proposes a method for predicting raw material prices with the aim of contributing to more accurate predictions of profitability. The prediction method is a multivariate time series analysis and the prediction target in this study is the price of iron ore, which is the largest contributor to the price of raw materials for steel products. Following established practices and previous studies, the accuracy of the prediction results was compared with past average values over a specified period. The proposed method was found to be more than 2.3 times more accurate than past average values. The proposed method was applied to predicting the price of iron ore in this study, but for the improvement of prediction accuracy the method may apply to other raw material prices that do not use a statistical method for prediction.
Keywords
CONSTRUCTION COST INDEX; IRON-ORE PRICE; TIME-SERIES; EXCHANGE-RATES; OIL PRICE; MODELS; CHINA; COINTEGRATION; MARKET; VECM
URI
http://oasis.postech.ac.kr/handle/2014.oak/94255
DOI
10.1061/(ASCE)CO.1943-7862.0001586
ISSN
0733-9364
Article Type
Article
Citation
JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT, vol. 145, no. 1, 2019-01
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 LEE, EUL BUM
Graduate Institute of Ferrous Technology
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