Previsión del suministro usando modelos estadísticos de supervivencia

• Díaz Martínez, Zuleyka ^[1] ; Fernández Menéndez, José ^[1] ; Minguela Rata, Beatriz ^[1]
  1. [1] UCM
• Localización: Anales de ASEPUMA, ISSN-e 2171-892X, Nº. 28, 2020
• Idioma: español
• Enlaces
  • Texto completo (htm)
• Resumen
  • español

    La previsión de la demanda en las organizaciones tiene un gran interés y abundan los modelos estadísticos para estimar la distribución de probabilidad de sus valores. Por el contrario, la previsión de las cantidades que se podrán obtener de los proveedores no ha recibido apenas atención. Las organizaciones hacen una previsión de la demanda a medio plazo para negociar con los proveedores las condiciones de suministro. Las inevitables variaciones que se produzcan en la práctica con respecto a las condiciones acordadas se gestionan sobre la marcha y no suelen ser suficientemente relevantes como para modelizarlas. Sin embargo, no siempre es así. Por ejemplo, para los distribuidores de productos farmacéuticos existen restricciones legales o contractuales que les obligan a garantizar el suministro a sus clientes y mercados. En este caso es frecuente que se produzca racionamiento de la oferta por parte de los laboratorios suministradores, lo que hace aconsejable modelizar estadísticamente tanto la demanda como la oferta para garantizar que la probabilidad de desabastecimiento no rebase unos límites aceptables. En este trabajo se presentan modelos estadísticos de supervivencia o fiabilidad, cuyo rasgo distintivo es la presencia de observaciones censuradas, para modelizar adecuadamente la oferta por parte de los proveedores.

  • English

    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.

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