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Costa, L, Barbosa, M B A, Baldam, R d L and Coelho, T d P (2019) Challenges of Process Modeling in Architecture and Engineering to Execute Projects and Public Works. Journal of Construction Engineering and Management, 145(01).
Han, Y, Feng, Z, Zhang, J, Jin, R and Aboagye-Nimo, E (2019) Employees’ Safety Perceptions of Site Hazard and Accident Scenes. Journal of Construction Engineering and Management, 145(01).
Jeelani, I, Albert, A, Han, K and Azevedo, R (2019) Are Visual Search Patterns Predictive of Hazard Recognition Performance? Empirical Investigation Using Eye-Tracking Technology. Journal of Construction Engineering and Management, 145(01).
Lee, C, Won, J and Lee, E (2019) Method for Predicting Raw Material Prices for Product Production over Long Periods. Journal of Construction Engineering and Management, 145(01).
- Type: Journal Article
- Keywords: Multivariate time series analysis; Variable error correction model; Price of iron ore; Price of oil; Exchange rate; Autoregressive moving average;
- ISBN/ISSN: 0733-9364
- URL: https://doi.org/10.1061/(ASCE)CO.1943-7862.0001586
- 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.
Lee, J and Hyun, H (2019) Multiple Modular Building Construction Project Scheduling Using Genetic Algorithms. Journal of Construction Engineering and Management, 145(01).
Nasirian, A, Arashpour, M and Abbasi, B (2019) Critical Literature Review of Labor Multiskilling in Construction. Journal of Construction Engineering and Management, 145(01).
Ryu, J, Seo, J, Jebelli, H and Lee, S (2019) Automated Action Recognition Using an Accelerometer-Embedded Wristband-Type Activity Tracker. Journal of Construction Engineering and Management, 145(01).
Tang, W, Cui, Q, Zhang, F and Chen, Y (2019) Urban Rail-Transit Project Investment Benefits Based on Compound Real Options and Trapezoid Fuzzy Numbers. Journal of Construction Engineering and Management, 145(01).
Techera, U, Hallowell, M and Littlejohn, R (2019) Worker Fatigue in Electrical-Transmission and Distribution-Line Construction. Journal of Construction Engineering and Management, 145(01).
Zhang, M, Cao, T and Zhao, X (2019) Using Smartphones to Detect and Identify Construction Workers’ Near-Miss Falls Based on ANN. Journal of Construction Engineering and Management, 145(01).
Zhang, S, Liu, X, Gao, Y and Ma, P (2019) Effect of Level of Owner-Provided Design on Contractor’s Design Quality in DB/EPC Projects. Journal of Construction Engineering and Management, 145(01).
Zhang, Y, Luo, H, Skitmore, M, Li, Q and Zhong, B (2019) Optimal Camera Placement for Monitoring Safety in Metro Station Construction Work. Journal of Construction Engineering and Management, 145(01).