TÉCNICAS AVANZADAS DE GESTIÓN DE STOCKS. APLICACIÓN A LA GESTIÓN DE ALMACENES EN FARMACIA HOSPITALARIA.

Abstract

The objective of this work is to reduce the existing uncertainty in stocks management, with the aim of improve the efficiency of stcks services, in particular applied to the pharmacy management in hospitals. Advamced techniques of estimation are applied to the problem of stocks management in hospital's pharmacies. An approach oriented to determine de dynamics of this kind of systems will be applied, using data mining techniques, making the most of the known of the dynamics to optimize the performance of the stocks service management, in contrast with the classical approaches to this kind of problems, usually estatics and based on the utilization of operative investigation techniques. The objective is to demonstrate the viability of using time series and causal models analysis techniques to estimate de demand. Data mining techniques are used (machine learning, knowledge discovery, ...) to compare and extend the previous results. Among this techniques can be cited decision trees, multivariate regression , neural networks, Markov models , Support Vector Machines (SVM ) , etc.