Visualisation and statistical modelling techniques for the management of inventory stock levels
Winston LeMay Sweatman, James M. McGree, Corrie Jacobien Carstens, Kylie J. Foster, Shen Liu, Nicholas John Tierney, E. C. Tredenick, Ayham Zaitouny · ANZIAM Journal · 2016
This paper describes the investigations conducted in a Mathematics-in-Industry Study Group project from the Australian meeting at Queensland University of Technology in 2015. This concerned the management of stock levels of raw materials used to construct aortic stents. The approaches used included network visualisation, classification and regression trees, and time series modelling. This work will be of general interest to those who are managing stock levels in a highly volatile context. The methods applied show that there is potential value in taking a statistical approach to understand and make decisions within such volatility. The work provides a basis for developing more advanced statistical approaches for specific inventory problems. References Breiman, L. (1996) Bagging Predictors. Machine Learning, 24, 2, 123–140. doi:10.1023/A:1018054314350 Breiman, L. (2001) Statistical Modeling: The Two Cultures Statistical Science, 16, 3, 199–231. https://projecteuclid.org/download/pdf_1/euclid.ss/1009213726 Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J. (1984) Classification and Regression Trees Wadsworth, Belmont, Ca. ISBN-13: 978-0412048418 ISBN-10: 0412048418 Csardi, G., Nepusz, T. (2006) The igraph software package for complex network research. InterJournal: Complex Systems, 1695, 5, 1–9. http://www.interjournal.org/manuscript_abstract.php?361100992 Fruchterman, T. M. J., Reingold, E. M. (1991) Graph drawing by force-directed placement. Software: Practice and Experience, 21, 11, 1129–1164. doi:10.1002/spe.4380211102 Hastie, T., Tibshirani, R., Friedman, J. H. (2009) The elements of statistical learning : Data mining, inference, and prediction, 2nd edition New York: Springer Verlag. ISBN 978-0-387-84858-7 (eBook), ISBN 978-0-387-84857-0 (Hardcover) doi:10.1007/978-0-387-84858-7 Kim, J. H., Wong, K., Athanasopoulos, G., Liu, S. (2011) Beyond point forecasting: Evaluation of alternative prediction intervals for tourist arrivals. International Journal of Forecasting, 27, 887–901. doi:10.1016/j.ijforecast.2010.02.014 Liu, S., Maharaj, E. A. (2013). A hypothesis test using bias-adjusted AR estimators for classifying time series in small samples. Computational Statistics and Data Analysis, 60, 32–49. doi:10.1016/j.csda.2012.11.014 Liu, S., Maharaj, E. A., Inder, B. (2014) Polarization of forecast densities: A new approach to time series classification. Computational Statistics and Data Analysis, 70, 345–361. doi:10.1016/j.csda.2013.10.008 Liu, S., McGree, J., Ge, Z., Xie, Y. (2015) Computational and Statistical Methods for Analysing Big Data with Applications. Elsevier, London. ISBN: 978-0-12-803732-4 Reingold, E. M., Tilford, J. S. (1981) Tidier drawings of trees. IEEE Transactions on Software Engineering, 7, 2, 223–228. doi:10.1109/TSE.1981.234519