Quantile Forecasting in Operational Planning and Inventory Management – an Initial Empirical Verification
DOI:
https://doi.org/10.12775/DEM.2016.001Keywords
LINLIN loss, quantile forecasting, quantile regression, re-order point, theta methodAbstract
In the paper we present our initial results of an empirical verification of different methodologies of quantile forecasting used in operational management to calculate the re-order point or order-up-to level as well as the optimal order quantity according to the newsvendor model. The comparison encompasses 26 procedures including quantile regression, the basic bootstrap method and popular textbook formulas. Our results, obtained on the base of 30 time series concerning such diversified phenomena as supermarket sales, passenger transport and water and gas demand, point to the usefulness of regression medians, regression quantiles, bootstrap methods and the procedures available in the SAP ERP system.References
Applied Forecasting, webpage sponsored by Route Optimization and Heiloo, http://www.appliedforecasting.com/quantiles-forecasts-for-inventory-optimization/ (26.09.2015).
Bruzda, J. (2014), Prognozy kwantylowe w zastosowaniach logistycznych. Wprowadzenie do problematyki (Quantile Forecasts in Logistic Applications – An Introduction), in Chaberek M and Reszka L. (eds.) Zeszyty Naukowe Uniwersytetu Gdańskiego, Ekonomika Transportu i Logistyki (Scientific Papers of the University of Gdańsk, Transport and Logistics Economics), 51, 175–195.
Bruzda, J. (2016), Metody wyznaczania prognoz kwantylowych w logistyce – weryfikacja empiryczna (Methods of Quantile Forecasting in Logistic Applications – an Empirical Verification), Logistyka (Logistics), 5/2016, Logistyka – nauka (Logistic research), 9–13.
Ciesielski, M. (2011), Zarządzanie Łańcuchami Dostaw (Supply Chain Management), Polskie Wydawnictwo Ekonomiczne, Warszawa.
Clements, M. P. (2005), Evaluating Econometric Forecasts of Economic and Financial Vari-ables, Palgrave Macmillan, New York.
Doman, M., Doman, R. (2009), Modelowanie Zmienności i Ryzyka. Metody Ekonometrii Finansowej (Risk and Volatility Modelling. Methods of Financial Econometrics), Wolters Kluwer Business, Kraków.
Forecasting Principles, webpage sponsored by the International Institute of Forecasters, http://www.forecastingprinciples.com/index.php/data (26.09.2015).
Gneiting, T. (2011), Quantiles as Optimal Point Forecasts, International Journal of Forecast-ing, 27, 197–207, DOI: http://dx.doi.org/10.1016/j.ijforecast.2009.12.015.
Granger, C. W. J. (1999), Outline of Forecast Theory Using Generalized Cost Functions, Spanish Economic Review, 1, 161–173, DOI: http://dx.doi.org/10.1007/s101080050007.
Koenker, R. (2005), Quantile Regression, Cambridge University Press, Cambridge.
Makridakis, S, Hibon, M (2000), The M3-competition: results, conclusions and implications, International Journal of Forecasting, 16, 451–476, DOI: http://dx.doi.org/10.1016/S0169-2070(00)00057-1.
R Archive Network, https://cran.r-project.org/web/packages/bayesm/index.html (26.09.2015).
SAP Help Portal, http://help.sap.com (26.09.2015).
Wagner, B. (2010), Purchasing and Forecasting Using SAP ERP, Operations and Supply Chain Management Library, http://scn.sap.com/community/uac/operations-and-scm-library (26.09.2015).
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