Stock control analytics: a data-driven approach to compute the fill rate for the (s, S) system considering undershoots
Inventory policies are traditionally characterized assuming several hypotheses that lead to commit important errors when are used in practical environments. This is the case when the inventory is continuously reviewed by means of the Order-Point, Order-Up-to-Level (s, S) system and undershoots, i.e....
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2020 |
| País: | España |
| Institución: | Universidad de Castilla-La Mancha |
| Repositorio: | RUIdeRA. Repositorio Institucional de la UCLM |
| OAI Identifier: | oai:ruidera.uclm.es:10578/23978 |
| Acceso en línea: | http://hdl.handle.net/10578/23978 |
| Access Level: | acceso abierto |
| Palabra clave: | Inventory Fill Rate Lost sales Undershoots State Dependent Parameter |
| Sumario: | Inventory policies are traditionally characterized assuming several hypotheses that lead to commit important errors when are used in practical environments. This is the case when the inventory is continuously reviewed by means of the Order-Point, Order-Up-to-Level (s, S) system and undershoots, i.e. the difference between the order-point and the inventory position when it is reached, are neglected. This paper analyses conceptually and empirically the bias on the classic fill rate formula when neglecting undershoots. After that, we suggest a non-parametric approach based on a State Dependent Parameter algorithm to propose a new non-linear expression, named analytic fill rate that correct that bias. The proposed approach is developed under a data-driven perspective and is easily implementable in practice. This research is developed in a lost sales context with stochastic and i.i.d. discrete demand |
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