How to cite this paper
Almaraj, I & Ghamdi, M. (2024). Adopting new ARIMA double layering technique in make-to-stock production policy.Decision Science Letters , 13(2), 315-328.
Refrences
Abolghasemi, M., Beh, E., Tarr, G., & Gerlach, R. (2020). Demand forecasting in supply chain: The impact of demand volatility in the presence of promotion. Computers and Industrial Engineering, 142(July 2019), 106380. https://doi.org/10.1016/j.cie.2020.106380
Arora, S., & Majumdar, A. (2022). Machine Learning and Soft Computing Applications in Textile and Clothing Supply Chain: Bibliometric and Network Analyses to Delineate Future Research Agenda. Expert Systems with Applications, 200(June 2021), 117000. https://doi.org/10.1016/j.eswa.2022.117000
Ayyildiz, E., Erdogan, M., & Taskin, A. (2021). Forecasting COVID-19 recovered cases with Artificial Neural Networks to enable designing an effective blood supply chain. Computers in Biology and Medicine, 139(August), 105029. https://doi.org/10.1016/j.compbiomed.2021.105029
Box, G., Jenkins, G., & Reinsel, G. (2008). Time Series Analysis. 4th ed., Wiley.
Dhahri, I., & Chabchoub, H. (2007). Nonlinear goal programming models quantifying the bullwhip effect in supply chain based on ARIMA parameters. European Journal of Operational Research, 177(3), 1800–1810. https://doi.org/10.1016/j.ejor.2005.10.065
Díaz-Robles, L. A., Ortega, J. C., Fu, J. S., Reed, G. D., Chow, J. C., Watson, J. G., & Moncada-Herrera, J. A. (2008). A hybrid ARIMA and artificial neural networks model to forecast particulate matter in urban areas: The case of Temuco, Chile. Atmospheric Environment, 42(35), 8331–8340. https://doi.org/10.1016/j.atmosenv.2008.07.020
Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American statistical association, 74(366a), 427-431. https://doi.org/10.2307/2286348.
Fanoodi, B., Malmir, B., & Jahantigh, F. F. (2019). Reducing demand uncertainty in the platelet supply chain through artificial neural networks and ARIMA models. Computers in Biology and Medicine, 113(January), 103415. https://doi.org/10.1016/j.compbiomed.2019.103415
Gilliland, M. (2010). The Business Forecasting Deal: Exposing Myths, Eliminating Bad Practices, Providing Practical Solutions (Vol. 27). John Wiley & Sons.
Gonçalves, J. N. C., Cortez, P., Carvalho, M. S., & Frazão, N. M. (2021). A multivariate approach for multi-step demand forecasting in assembly industries: Empirical evidence from an automotive supply chain. Decision Support Systems, 142(March 2020). https://doi.org/10.1016/j.dss.2020.113452
Hikichi, S. E., Salgado, E. G., & Beijo, L. A. (2017). Forecasting number of ISO 14001 certifications in the Americas using ARIMA models. Journal of Cleaner Production, 147, 242–253. https://doi.org/10.1016/j.jclepro.2017.01.084
Jaipuria, S., & Mahapatra, S. S. (2014). An improved demand forecasting method to reduce bullwhip effect in supply chains. Expert Systems with Applications, 41(5), 2395–2408. https://doi.org/10.1016/j.eswa.2013.09.038
Karimi, M., Melesse, A. M., Khosravi, K., Mamuye, M., & Zhang, J. (2019). Analysis and prediction of meteorological drought using SPI index and ARIMA model in the Karkheh River Basin, Iran. In Extreme Hydrology and Climate Variability: Monitoring, Modelling, Adaptation and Mitigation. Elsevier Inc. https://doi.org/10.1016/B978-0-12-815998-9.00026-9
Koutroumanidis, T., Ioannou, K., & Arabatzis, G. (2009). Predicting fuelwood prices in Greece with the use of ARIMA models, artificial neural networks and a hybrid ARIMA-ANN model. Energy Policy, 37(9), 3627–3634. https://doi.org/10.1016/j.enpol.2009.04.024
Ramos, P., Santos, N., & Rebelo, R. (2015). Performance of state space and ARIMA models for consumer retail sales forecasting. Robotics and Computer-Integrated Manufacturing, 34, 151–163. https://doi.org/10.1016/j.rcim.2014.12.015
Arora, S., & Majumdar, A. (2022). Machine Learning and Soft Computing Applications in Textile and Clothing Supply Chain: Bibliometric and Network Analyses to Delineate Future Research Agenda. Expert Systems with Applications, 200(June 2021), 117000. https://doi.org/10.1016/j.eswa.2022.117000
Ayyildiz, E., Erdogan, M., & Taskin, A. (2021). Forecasting COVID-19 recovered cases with Artificial Neural Networks to enable designing an effective blood supply chain. Computers in Biology and Medicine, 139(August), 105029. https://doi.org/10.1016/j.compbiomed.2021.105029
Box, G., Jenkins, G., & Reinsel, G. (2008). Time Series Analysis. 4th ed., Wiley.
Dhahri, I., & Chabchoub, H. (2007). Nonlinear goal programming models quantifying the bullwhip effect in supply chain based on ARIMA parameters. European Journal of Operational Research, 177(3), 1800–1810. https://doi.org/10.1016/j.ejor.2005.10.065
Díaz-Robles, L. A., Ortega, J. C., Fu, J. S., Reed, G. D., Chow, J. C., Watson, J. G., & Moncada-Herrera, J. A. (2008). A hybrid ARIMA and artificial neural networks model to forecast particulate matter in urban areas: The case of Temuco, Chile. Atmospheric Environment, 42(35), 8331–8340. https://doi.org/10.1016/j.atmosenv.2008.07.020
Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American statistical association, 74(366a), 427-431. https://doi.org/10.2307/2286348.
Fanoodi, B., Malmir, B., & Jahantigh, F. F. (2019). Reducing demand uncertainty in the platelet supply chain through artificial neural networks and ARIMA models. Computers in Biology and Medicine, 113(January), 103415. https://doi.org/10.1016/j.compbiomed.2019.103415
Gilliland, M. (2010). The Business Forecasting Deal: Exposing Myths, Eliminating Bad Practices, Providing Practical Solutions (Vol. 27). John Wiley & Sons.
Gonçalves, J. N. C., Cortez, P., Carvalho, M. S., & Frazão, N. M. (2021). A multivariate approach for multi-step demand forecasting in assembly industries: Empirical evidence from an automotive supply chain. Decision Support Systems, 142(March 2020). https://doi.org/10.1016/j.dss.2020.113452
Hikichi, S. E., Salgado, E. G., & Beijo, L. A. (2017). Forecasting number of ISO 14001 certifications in the Americas using ARIMA models. Journal of Cleaner Production, 147, 242–253. https://doi.org/10.1016/j.jclepro.2017.01.084
Jaipuria, S., & Mahapatra, S. S. (2014). An improved demand forecasting method to reduce bullwhip effect in supply chains. Expert Systems with Applications, 41(5), 2395–2408. https://doi.org/10.1016/j.eswa.2013.09.038
Karimi, M., Melesse, A. M., Khosravi, K., Mamuye, M., & Zhang, J. (2019). Analysis and prediction of meteorological drought using SPI index and ARIMA model in the Karkheh River Basin, Iran. In Extreme Hydrology and Climate Variability: Monitoring, Modelling, Adaptation and Mitigation. Elsevier Inc. https://doi.org/10.1016/B978-0-12-815998-9.00026-9
Koutroumanidis, T., Ioannou, K., & Arabatzis, G. (2009). Predicting fuelwood prices in Greece with the use of ARIMA models, artificial neural networks and a hybrid ARIMA-ANN model. Energy Policy, 37(9), 3627–3634. https://doi.org/10.1016/j.enpol.2009.04.024
Ramos, P., Santos, N., & Rebelo, R. (2015). Performance of state space and ARIMA models for consumer retail sales forecasting. Robotics and Computer-Integrated Manufacturing, 34, 151–163. https://doi.org/10.1016/j.rcim.2014.12.015