Raúl Pino Díez

Multiagent Model for Supply Chain Management

There are several circumstances which, in the last two decades, have granted the Supply Chain Management (SCM) a strategic role in the search for competitive advantage. Thus, this paper applies multiagent methodology to optimize the management. We represent the supply chain as a Global Multiagent System, composed of four Multiagent Subsystems, which replicate the behavior of the different levels of the supply chain. Thereby, each member has its own decision-making capacity and seeks to optimize the performance of the supply chain. We will tackle the problem from two complementary perspectives: reducing the Bullwhip Effect, which can be considered as one of the main sources of inefficiencies in the SCM, and minimizing management costs, both from a non collaborative approach, where each level seeks the best solution for himself, and from a collaborative approach, where each level negotiates with the rest looking for the best solution for the whole supply chain.

Analysis of expenditure on pharmaceuticals in the Spanish public health system

In Spain, the social security (i.e., the public health system) budget relays on the local administrations of every region. From this budget, a very important amount of money goes to finance, totally or partially and medicines for people. If the administrations do not take this issue seriously, the expenses can be overwhelming. In this paper, a Spanish region called Asturias is analysed in terms of the number of medicines prescribed and charged to the social security system. In particular, two time series are analysed and forecasted: total number of prescriptions to active people and to pensioners. Two well-known methods are used to obtain forecasts: Box–Jenkins (BJ) ARIMA models and Artificial Neural Networks (ANNs).