Establishing a combined forecasting model: a case study on the logistic demand of Nanjing’s green tea industry in China
Abstract
The sales logistics of tea leaves is a process that organically integrates basic logistics activities, including transportation, storage, loading, unloading, carrying, packaging, distribution processing, delivery, and information processing. This process requires quick and accurate forecasting of the logistics demand in the green tea market and the provision of feedback to businesses and farming partners, revealing the need for a simple and accurate forecasting method. Responding to and solving the unclear information and limited data available regarding the green tea market are critical. Therefore, this study established a simple, quick, and accurate model through the use of time series and the technique for ordering preferences by similarity to the ideal solution. Finally, the actual logistics demand in the Nanjing green tea industry was employed to verify the proposed model’s practicality and feasibility, which may provide a critical reference for relevant parties such as businesses and researchers.
First published online 14 December 2020
Keyword : logistics demand, logistics demand forecast, green tea industry, time series forecast model, technique for ordering preferences by similarity to the ideal solution (TOPSIS)
How to Cite
Lin, H.-L., & Lin, C.-T. (2021). Establishing a combined forecasting model: a case study on the logistic demand of Nanjing’s green tea industry in China. Technological and Economic Development of Economy, 27(1), 71-95. https://doi.org/10.3846/tede.2020.14008
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Zhang, C., Huang, L., & Zhao, Z. (2013). Research on combination forecast of port cargo throughput based on time series and causality analysis. Journal of Industrial Engineering and Management, 6(1), 124–134. https://doi.org/10.3926/jiem.687
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Zill, D. G., & Cullen, M. R. (2000). Advance Engineering Mathematics. Jones and Barhett.
Baginski, S. P., & Hassell, J. M. (1997). Determinants of management forecast precision. The Accounting Review, 72(2), 303–312.
Bai, C., & Sarkis, J. (2018). Integrating sustainability into supplier selection: a grey-based TOPSIS analysis. Technological and Economic Development of Economy, 24(6), 2202–2224. https://doi.org/10.3846/tede.2018.5582
Bates, J. M., & Granger, C. W. J. (1969). The combination of forecasts. Journal of the Operational Research Society, 20(4), 451–468. https://doi.org/10.1057/jors.1969.103
Carmona-Benítez, R. B., & Nieto, M. R. (2020). SARIMA damp trend grey forecasting model for airline industry. Journal of Air Transport Management, 82, 101736. https://doi.org/10.1016/j.jairtraman.2019.101736
Chang, S. C., Lai, H. C., & Yu, H. C. (2005). A variable P value rolling Grey forecasting model for Taiwan semiconductor industry production. Technological Forecasting and Social Change, 72, 623–640. https://doi.org/10.1016/j.techfore.2003.09.002
Chao, T., & Peng, J. (2020). An integrated picture fuzzy ANP-TODIM multi-criteria decision-making approach for tourism attraction recommendation. Technological and Economic Development of Economy, 26(2), 331–354. https://doi.org/10.3846/tede.2019.11412
Colino, E. V., Irwin, S. H., Garcia, P., & Etienne, X. (2012). Composite and outlook forecast accuracy. Journal of Agricultural and Resource Economics, 37(2), 228–246.
Deng, J. (1982). Control problems of grey systems. Systems & Control Letters, 1(5), 288–294. https://doi.org/10.1016/S0167-6911(82)80025-X
Fernández-Vázquez, E., & Moreno, B. (2017). Entropy econometrics for combining regional economic forecasts: A data-weighted prior estimator. Journal of Geographical Systems, 19(4), 349–370. https://doi.org/10.1007/s10109-017-0259-9
Gao, Y., Chang, D., Fang, T., & Luo, T. (2018). The correlation between logistics industry and other industries: an evaluation of the empirical evidence from china. The Asian Journal of Shipping and Logistics, 34(1), 27–32. https://doi.org/10.1016/j.ajsl.2018.03.004
Ghosh, I., Jana, R. K., & Sanyal, M. K. (2019). Analysis of temporal pattern, causal interaction and predictive modeling of financial markets using nonlinear dynamics, econometric models and machine learning algorithms. Applied Soft Computing, 82, 105553. https://doi.org/10.1016/j.asoc.2019.105553
Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. Journal of Finance, 48(5), 1779–1801. https://doi.org/10.1111/j.1540-6261.1993.tb05128.x
Guo, Y., Yao, S., Cheng, H., & Zhu, W. (2020). China’s copper futures market efficiency analysis: based on nonlinear granger causality and multifractal methods. Resources Policy, 68, 101716. https://doi.org/10.1016/j.resourpol.2020.101716
Hilary, L. S. (1967). The historical development of the Gauss linear model. Biometrika, 54 (1/2), 1–24. https://doi.org/10.1093/biomet/54.1-2.1
Hsu, C. C., & Chen, C. Y. (2003). Applications of improved grey prediction model for power demand forecasting. Energy Conversion and Management, 44, 2241–2249. https://doi.org/10.1016/S0196-8904(02)00248-0
Hsu, L. C. (2003). Apply the grey prediction model to the global integrated circuit industry. Technological Forecasting and Social Change, 70(6), 563–574. https://doi.org/10.1016/S0040-1625(02)00195-6
Hu, Y. C. (2020). Constructing grey prediction models using grey relational analysis and neural networks for magnesium material demand forecasting. Applied Soft Computing, 93, 106398. https://doi.org/10.1016/j.asoc.2020.106398
Hwang, C., & Yoon, K. (1981). Multiple attribute decision making: Methods and application. Springer Publications. https://doi.org/10.1007/978-3-642-48318-9
Ji, S. W., Li, T. K., Chen, G. P., Su, G., & Zhang, Q. C. (2015). Ann forecast algorithmic model for port logistic demand research. Applied Mechanics and Materials, 744–746, 1869–1872. https://doi.org/10.4028/www.scientific.net/AMM.744-746.1869
Jia, Z. Q., Zhou, Z. F., Zhang, H. J., Li, B., & Zhang, Y. X. (2020). Forecast of coal consumption in Gansu Province based on Grey-Markov chain model. Energy, 199, 117444. https://doi.org/10.1016/j.energy.2020.117444
Jiani, W., & Hans-Dietrich, H. (2018). The freight village as a pathway to sustainable agricultural products logistics in china. Journal of Cleaner Production, 196, 1227–1238. https://doi.org/10.1016/j.jclepro.2018.06.077
Lamsal, K., Jones, P. C., & Thomas, B. W. (2016). Harvest logistics in agricultural systems with multiple, independent producers and no on-farm storage. Computers & Industrial Engineering, 91, 129–138. https://doi.org/10.1016/j.cie.2015.10.018
Lin, H. L., & Cho, C. C. (2020). An ideal model for a merger and acquisition strategy in the information technology industry a case study for investment in the Taiwanese industrial personal computer sector. Journal of Testing and Evaluation, 48(2), 775–793. https://doi.org/10.1520/JTE20170106
Lin, H. L., Cho, C. C., Ma, Y. Y., Hu, Y.-Q., & Yang, G. H. (2019). Optimization plan for excess warehouse storage in e-commerce based plant shops: a case study for Chinese plant industrial. Journal of Business Economics and Management, 20(4), 776–798. https://doi.org/10.3846/jbem.2019.10188
Liu, L., Wang, H., & Xing, S. H. (2019). Optimization of distribution planning for agricultural products in logistics based on degree of maturity. Computers and Electronics in Agriculture, 160, 1–7. https://doi.org/10.1016/j.compag.2019.02.030
Mahmoudi, A., Javed, S. A., Liu, S., & Deng, X. (2020). Model for intelligent decisions: application in project management. Technological and Economic Development of Economy, 26(3), 621–641. https://doi.org/10.3846/tede.2020.11890
National Bureau of Statistics of China. (2018). China statistical information service. http://www.stats.gov.cn/
Wang, Q., Li, S., Li, R., & Ma, M. (2018). Forecasting U.S. shale gas monthly production using a hybrid ARIMA and metabolic nonlinear grey model. Energy, 160, 378–387. https://doi.org/10.1016/j.energy.2018.07.047
Wu, W., Ma, X., Zeng, B., Wang, Y., & Cai, W. (2019). Forecasting short-term renewable energy consumption of China using a novel fractional nonlinear grey Bernoulli model. Renewable Energy, 140, 70–87. https://doi.org/10.1016/j.renene.2019.03.006
Xu, Q., Wang, L., Jiang C., & Zhang, X. (2019). A novel UMIDAS-SVQR model with mixed frequency investor sentiment for predicting stock market volatility. Expert Systems with Applications, 132, 12–27. https://doi.org/10.1016/j.eswa.2019.04.066
Zainuddin, N. H., Lola, M. S., Djauhari, M. A., Yusof, F., Afiq Ramlee, M. N., Deraman, A., Ibrahim, Y., & Abdullah, M. T. (2019). Improvement of time forecasting models using a novel hybridization of bootstrap and double bootstrap artificial neural networks. Applied Soft Computing, 84, 105676. https://doi.org/10.1016/j.asoc.2019.105676
Zemlickienė, V., & Turskis, Z. (2020). Evaluation of the expediency of technology commercialization: a case of information technology and biotechnology. Technological and Economic Development of Economy, 26(1), 271–289. https://doi.org/10.3846/tede.2020.11918
Zhang, C., Huang, L., & Zhao, Z. (2013). Research on combination forecast of port cargo throughput based on time series and causality analysis. Journal of Industrial Engineering and Management, 6(1), 124–134. https://doi.org/10.3926/jiem.687
Zhuang, Z. Y., Su, C. R., & Chang, S. C. (2019). The effectiveness f IF-MADM (Intuitionistic-fuzzy multi attribute decision-making) for group decisions: methods and an empirical assessment for the selection of a senior centre. Technological and Economic Development of Economy, 25(2), 322–364. https://doi.org/10.3846/tede.2019.8399
Zill, D. G., & Cullen, M. R. (2000). Advance Engineering Mathematics. Jones and Barhett.