Research ArticleOpen AccessGoogle Scholar indexed
The Application of Time Series Modelling and Monte Carlo Simulation: Forecasting Volatile Inventory Requirements
Faculty of Design Media and Management, Buckinghamshire New University, High Wycombe, UK
Faculty of Design Media and Management, Buckinghamshire New University, High Wycombe, UK
Faculty of Design Media and Management, Buckinghamshire New University, High Wycombe, UK
- 1 Faculty of Design Media and Management, Buckinghamshire New University, High Wycombe, UK
- 2 Faculty of Design Media and Management, Buckinghamshire New University, High Wycombe, UK
- 3 Faculty of Design Media and Management, Buckinghamshire New University, High Wycombe, UK
Applied Mathematics·Volume 05 (2014)·Pages 1152–1168·Published 24 April 2014·DOI10.4236/am.2014.58108
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Abstract
During the assembly of internal combustion engines, the specific size of crankshaft shell bearing is not known until the crankshaft is fitted to the engine block. Though the build requirements for the engine are consistent, the consumption profile of the different size shell bearings can follow a highly volatile trajectory due to minor variation in the dimensions of the crankshaft and engine block. The paper assesses the suitability of time series models including ARIMA and exponential smoothing as an appropriate method to forecast future requirements. Additionally, a Monte Carlo method is applied through building a VBA simulation tool in Microsoft Excel and comparing the output to the time series forecasts.
KeywordsForecastingTime Series AnalysisMonte Carlo Simulation
- Hillier, V.A.W. and Coombes, P. (2004) Fundamentals of Motor Vehicle Technology Book 1. 5th Edition, Nelson Thornes Ltd, Cheltenham.
- Woodward, W.A., Gray, H.L. and Elliot, A.C. (2012) Applied Time Series Analysis. CRC Press, Boca Baton.
- Cryer, J.D. and Chan, K.S. (2008) Time Series Analysis: With Applications in R. Springer. Science + Business Media, London.
- Brockwell, P.J. and Davis, R.A. (2010) Introduction to Time Series and Forecasting. 2nd Edition, Springer Texts in Statistics, New York.
- Pena, D., Tiao, G.C. and Tsay, R.S. (2000) A Course in Time Series Analysis. Wiley-Interscience, New York.
- Chatfield, C. (2006) The Analysis of Time Series, an Introduction. 6th Edition, Chapman and Hall, London.
- Montgomery, D.C., Jennings, C.L. and Kulachi, M. (2008) Introduction to Time Series Analysis and Forecasting. WileyBlackwell, New Jersey.
- Bisgard, S. and Kulachi, M. (2011) Time Series Analysis and Forecasting by Example. Wiley & Sons Inc., New York. http://dx.doi.org/10.1002/9781118056943
- Box, G.E.P., Jenkins, G.M. and Reinsel, G.C. (2008) Time Series Analysis. Forecasting and Control. 4th Edition, Wiley & Sons Inc., New Jersey.
- Hamilton, J.D. (1994) Time Series Analysis. Princeton University Press, Princeton.
- Kirchgassner, D., Wolters, J. and Hassler, U. (2013) Introduction to Modern Time Series Analysis. 2nd Edition, Springer Heidelberg, New York. http://dx.doi.org/10.1007/978-3-642-33436-8
- Kendall, M. (1976) Time Series. 2nd Edition, Charles Griffin and Co Ltd., London and High Wycombe.
- Brown, R.G. (1956) Exponential Smoothing for Predicting Demand. http://legacy.library.ucsf.edu/tid/dae94e00
- Robinson, S. (2004) Simulation: The Practice of Model Development and Use. John Wiley and Sons, Chichester.
- Banks, J., Carson, J.S. and Nelson, B.L. (1996) Discrete-Event System Simulation. 2nd Edition, Prentice-Hall, Upper Saddle River.
- Singh, V.P. (2009) System Modelling and Simulation. New Age International Publishers, New Delhi.
- Sokolowski, J.A. (2010) Monte Carlo Simulation. In: Sokolowski, J.A. and Banks, C.M., Eds., Modelling and Simulation Fundamentals: Theoretical Underpinnings and Practical Domains, Wiley & Sons Inc., New Jersey, 131-145. http://dx.doi.org/10.1002/9780470590621.ch5