Previsión del suministro usando modelos estadísticos de supervivencia

  1. Díaz Martínez, Zuleyka 1
  2. Fernández Menéndez, José 1
  3. Minguela Rata, Beatriz 1
  1. 1 UCM
Journal:
Anales de ASEPUMA

ISSN: 2171-892X

Year of publication: 2020

Issue: 28

Type: Article

DIALNET GOOGLE SCHOLAR lock_openOpen access editor

More publications in: Anales de ASEPUMA

Abstract

Demand forecasting in organizations is a very noteworthy issue, and many statistical models have been developed to estimate the probability distribution of its values. However, the forecast of the amounts of goods that can be gotten from the suppliers has not received almost any attention. Usually, firms make a mid term forecast of the demand in order to bargain with suppliers the details of the procurement. The unavoidable variations that spring over in practice regarding to the agreed conditions, are managed on the fly, and typically they are not relevant enough in order to justify their statistical modeling. But that’s not always the case. For example, there are legal and contractual requirements for pharmaceutical distributors that force them to provide a given service level to their customers. In this situation, there is often some degree of supply rationing from the pharma laboratories, and this makes it advisable to model both demand and supply in order to guarantee that the medication shortage probability is kept under an acceptable level. in this work we present some survival, or reliability, statistical models, whose distinctive feature is the presence of censored observations, to properly model the supply from the providers.

Bibliographic References

  • Axsäter, S. (2006). Inventory Control. Springer. Box, G. E. P. & Jenkins, G. M. (1970). Time Series Analysis, Forecasting, and Control. Holden-Day.
  • Brown, R. G. (1959). Statistical Forecasting for Inventory Control. McGraw-Hill. Brown, R. G. (1963). Smoothing, Forecasting and Prediction of Discrete Time Series. Prentice-Hall.
  • Byrd, R. H., Lu, P., Nocedal, J. & Zhu, C. (1995). A Limited Memory Algorithm for Bound Constrained Optimization. SIAM Journal on Scientific Computing, 16 (5), 1190-1208.
  • Cox, D. R. & Oakes, D. (1984). Analysis of Survival Data. Chapman & Hall/CRC. Heizer, J. & Render, B. (2007). Dirección de la Producción y de Operaciones (8ª ed). Pearson Education.
  • Hill, A. V. (2012). The Encyclopedia of Operations Management. Pearson Education.
  • Holt, C. C. (2004). Forecasting seasonals and trends by exponentially weighted moving averages. International Journal of Forecasting, 20 (1), 5-10.
  • Hopp, W. J. & Spearman, M. L. (2011). Factory Physics (3ª ed). Waveland Press. Hyndman, R. J. & Athanasopoulos, G. (2014). Forecasting. Principles and Practice. OTEXTS.COM
  • Hyndman, R. J. & Khandakar, Y. (2008). Automatic time series forecasting: the forecast package for R. Journal of Statistical Software, 26 (3), 1-22.
  • Hyndman, R. J., Athanasopoulos, G., Bergmeir, C., Caceres, G., Chhay, L., O’HaraWild, M., Petropoulos, F., Razbash, S., Wang, E. & Yasmeen, F. (2020). forecast: Forecasting functions for time series and linear models (R package version 8.11).
  • Hyndman, R. J., Koehler, A. B., Ord, J. K. & Snyder, R. D. (2008). Forecasting with Exponential Smoothing. The State Space Approach. Springer.
  • Hyndman, R. J., Koehler, A. B., Snyder, R. D. & Grose, S. (2002). A state space framework for automatic forecasting using exponential smoothing methods. International Journal of Forecasting, 18 (3), 439-454.
  • Kalbfleisch, J. D. & Prentice, R. L. (2002). The Statistical Analysis of Failure Time Data (2nd ed). Wiley-Interscience.
  • Lawless, J. F. (2003). Statistical Models and Methods for Lifetime Data (2nd ed). Wley-Interscience.
  • Moore, D. F. (2016). Applied Survival Analysis Using R. Springer.
  • Mullen, K., Ardia, D., Gil, D., Windover, D. & Cline, J. (2011). Deoptim: An R Package for Global Optimization by Differential Evolution. Journal of Statistical Software, 40 (6), 1-26.
  • R Core Team. (2020). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. Vienna, Austria. https://www.Rproject.org/ Silver, E. A., Pyke, D. F. & Thomas, D. J. (2017). Inventory and Production Management in Supply Chains (4ª ed). CRC Press.
  • Therneau, T. M. & Grambsch, P. M. (2000). Modeling Survival Data: Extending the Cox Model. Springer.
  • Therneau, T. M. (2020). A Package for Survival Analysis in R [R package version 3.1-11].
  • Thomopoulos, N. T. (2015). Demand Forecasting for Inventory Control. Springer.
  • Thomopoulos, N. T. (2016). Elements of Manufacturing, Distribution and Logistics. Springer. Winters, P. R. (1960). Forecasting Sales by Exponentially Weighted Moving Averages. Management Science, 6 (3), 324-342.
  • Xiang, Y., Gubian, S., Suomela, B. & Hoeng, J. (2013). Generalized Simulated Annealing for Efficient Global Optimization: the GenSA Package for R. The R Journal, 5 (1).
Data source: Dialnet