Please use this identifier to cite or link to this item: https://ah.nccu.edu.tw/handle/140.119/138427


Title: An integrated forecasting model for the coffee bean supply chain
Authors: Le, Thi-Nham
黎氏仁
Wang, Chia-Nan
Yu, Min-Chun
Ho, Nguyen-Nhu-Y
Contributors: 東南亞語言與文化學士學位學程
Keywords: Optimization;Forecasting Model;Supply Chain Management;Coffee Bean
Date: 2021-04
Issue Date: 2022-01-04 14:45:02 (UTC+8)
Abstract: Coffee is the most traded commodity after petroleum. The Vietnamese coffee bean industry has raised concerns lately over an inefficient coffee value chain; bets on coffee price uncertainty are increasing worldwide in the current. Accurate optimization of coffee bean prices helps manufacturers to control an unpredictable market and upgrade cooperativeness in sustainable agriculture. The authors proposed a forecasting method to deal with demand volatility and uncertainty in volumes and coffee bean prices. In this paper, we applied the forecasting nonlinear grey Bernoulli model (NGBM) (1,1). NGBM (1,1), which is based on the parameter optimization algorithm, can increase the precision of predictions. NGBM (1,1) was integrated with Fourier residual modification model to forecast coffee bean price, which was a crucial factor in the Vietnamese coee bean supply chain. The price of coffee beans was calculated using a differential equation in an uncertain system, along with actual data collected over the past six years. The results of this study demonstrate that an integrated forecasting model is an effective forecasting method. This research can help companies to control risks that come with uncertain coffee prices and reduce risks in the sustainable agriculture supply chain.
Relation: Applied Economics, Vol.53, No.28, pp.3321-3333
Data Type: article
DOI 連結: https://doi.org/10.1080/00036846.2021.1887447
Appears in Collections:[東南亞語言與文化學士學位學程] 期刊論文

Files in This Item:

File Description SizeFormat
3.pdf869KbAdobe PDF36View/Open


All items in 學術集成 are protected by copyright, with all rights reserved.


社群 sharing