How to cite this paper
Wofuru-Nyenke, O & Briggs, T. (2022). Predicting demand in a bottled water supply chain using classical time series forecasting models.Journal of Future Sustainability, 2(2), 65-80.
Refrences
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Brownlee, J. (2018). Deep Learning for Time Series Forecasting: Predict the Future with MLPs, CNNs and LSTMs in Python. 1st ed., Independently Published.
Chopra, S. & Meindl, P. (2016). Supply Chain Management: Strategy, Planning, and Operation. 6th ed., London, LDN: Pearson Education Ltd.
Ekhosuehi, N., Dickson, E.A.O., & Omorogbe, A. (2016). On forecast performance using a class of weighted moving average processes for time series. Journal of Natural Sciences Research, 6(13), 2224 – 3186.
George, M.L., Rowlands, D., Price, M. & Maxey, J. (2005). The Lean Six Sigma Pocket Toolbook: A Quick Reference Guide to Nearly 100 Tools for Improving Process Quality, Speed, and Complexity. New York, NY: McGraw-Hill.
Grubb, H., & Mason, A. (2001). Long lead-time forecasting of UK air passengers by Holt–Winters methods with damped trend. International Journal of Forecasting, 17(1), 71 – 82.
Lee, W., Chen, C., Chen, K., Chen, T., & Liu, C. (2012). A Comparative Study on The Forecast of Fresh Food Sales Us-ing Logistic Regression, Moving Average and BPNN Methods. Journal of Marine Science and Technology, 20(2), 142 – 152.
Mateia, N.A. (2013). Simple Moving Average vs Linear Regression Forecast. Journal Annals Economy Series, 16, 83 – 88.
Montgomery, D.C., Runger, G.C. & Hubele, N.F. (2011). Engineering Statistics. 5th ed., New Jersey, NJ: John Wiley & Sons, Inc.
Nakade, K., & Aniyama, Y. (2019). Bullwhip effect of weighted moving average forecast under stochastic lead time. IFAC-PapersOnLine, 52(13), 1277-1282.
Rendon-Sanchez, J.F., & de Menezes, L.M. (2019). Structural combination of seasonal exponential smoothing forecasts applied to load forecasting. European Journal of Operational Research, 275(3), 916 – 924.
Russel, R.S. & Taylor, B.W. (2011). Operations Management. 7th ed., New Jersey, NJ: John Wiley & Sons, Inc.
Segura, J.V., & Vercher, E. (2001). A spreadsheet modeling approach to the Holt–Winters optimal forecasting. Europe-an Journal of Operational Research, 131(2), 375 – 388.
Shih, S.H. & Tsokos, C.P. (2008). A Weighted Moving Average Process for Forecasting. Journal of Modern Applied Statistical Methods, 7(1), 187 – 197.
Wofuru-Nyenke, O.K. (2021). Value Stream Mapping: A Tool for Waste Reduction. International Journal of Innovative Research & Development, 10(6), 13 – 20.
Wofuru-Nyenke, O.K., Briggs, T.A., & Aikhuele, D.O. (2022). Advancements in Sustainable Manufacturing Supply Chain Modelling: a Review. Process Integration and Optimization for Sustainability, 1 – 25.
Wofuru-Nyenke, O.K., Nkoi, B., & Oparadike, F.E. (2019). Waste and Cost Reduction for a Water Bottling Process Us-ing Lean Six Sigma. European Journal of Engineering and Technology Research, 4(12), 71 – 77.
Yu, J., Kim, S.B., Bai, J., & Han, S.W. (2020). Comparative Study on Exponentially Weighted Moving Average Ap-proaches for the Self-Starting Forecasting. Applied Sciences, 10(20), 1 – 18.
Billah, B., King, M.L., Snyder, R.D., & Koehler, A.B. (2006). Exponential smoothing model selection for forecasting. International Journal of Forecasting, 22(2), 239 – 247.
Brownlee, J. (2018). Deep Learning for Time Series Forecasting: Predict the Future with MLPs, CNNs and LSTMs in Python. 1st ed., Independently Published.
Chopra, S. & Meindl, P. (2016). Supply Chain Management: Strategy, Planning, and Operation. 6th ed., London, LDN: Pearson Education Ltd.
Ekhosuehi, N., Dickson, E.A.O., & Omorogbe, A. (2016). On forecast performance using a class of weighted moving average processes for time series. Journal of Natural Sciences Research, 6(13), 2224 – 3186.
George, M.L., Rowlands, D., Price, M. & Maxey, J. (2005). The Lean Six Sigma Pocket Toolbook: A Quick Reference Guide to Nearly 100 Tools for Improving Process Quality, Speed, and Complexity. New York, NY: McGraw-Hill.
Grubb, H., & Mason, A. (2001). Long lead-time forecasting of UK air passengers by Holt–Winters methods with damped trend. International Journal of Forecasting, 17(1), 71 – 82.
Lee, W., Chen, C., Chen, K., Chen, T., & Liu, C. (2012). A Comparative Study on The Forecast of Fresh Food Sales Us-ing Logistic Regression, Moving Average and BPNN Methods. Journal of Marine Science and Technology, 20(2), 142 – 152.
Mateia, N.A. (2013). Simple Moving Average vs Linear Regression Forecast. Journal Annals Economy Series, 16, 83 – 88.
Montgomery, D.C., Runger, G.C. & Hubele, N.F. (2011). Engineering Statistics. 5th ed., New Jersey, NJ: John Wiley & Sons, Inc.
Nakade, K., & Aniyama, Y. (2019). Bullwhip effect of weighted moving average forecast under stochastic lead time. IFAC-PapersOnLine, 52(13), 1277-1282.
Rendon-Sanchez, J.F., & de Menezes, L.M. (2019). Structural combination of seasonal exponential smoothing forecasts applied to load forecasting. European Journal of Operational Research, 275(3), 916 – 924.
Russel, R.S. & Taylor, B.W. (2011). Operations Management. 7th ed., New Jersey, NJ: John Wiley & Sons, Inc.
Segura, J.V., & Vercher, E. (2001). A spreadsheet modeling approach to the Holt–Winters optimal forecasting. Europe-an Journal of Operational Research, 131(2), 375 – 388.
Shih, S.H. & Tsokos, C.P. (2008). A Weighted Moving Average Process for Forecasting. Journal of Modern Applied Statistical Methods, 7(1), 187 – 197.
Wofuru-Nyenke, O.K. (2021). Value Stream Mapping: A Tool for Waste Reduction. International Journal of Innovative Research & Development, 10(6), 13 – 20.
Wofuru-Nyenke, O.K., Briggs, T.A., & Aikhuele, D.O. (2022). Advancements in Sustainable Manufacturing Supply Chain Modelling: a Review. Process Integration and Optimization for Sustainability, 1 – 25.
Wofuru-Nyenke, O.K., Nkoi, B., & Oparadike, F.E. (2019). Waste and Cost Reduction for a Water Bottling Process Us-ing Lean Six Sigma. European Journal of Engineering and Technology Research, 4(12), 71 – 77.
Yu, J., Kim, S.B., Bai, J., & Han, S.W. (2020). Comparative Study on Exponentially Weighted Moving Average Ap-proaches for the Self-Starting Forecasting. Applied Sciences, 10(20), 1 – 18.