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Abstract Nowadays, one of the main difficulties of Production Management is to take into account the increasing uncertainty of the customer demand. In an MRP system, this uncertainty is mainly managed at middle term and through successive actualizations of the planning. We suggest in this paper a way to explicitly model the uncertainty and imprecision of the demand allowing to pass through all the MRPII steps (Material Requirement Planning, Load Planning, Scheduling). This method, named Fuzzy-MRP (F-MRP) allows to visualize at each step a much more rich information for the decision makers, taking into account not only the certain data but also a quantification of the various eventualities that may arise. Decisions requiring a long preparation (sub-contracting, order of components, increase of capacity, etc.) can so be considered earlier, on the base of quantified data. [ABSTRACT FROM AUTHOR]

The study of smart sensors and actuators led, during the past few years, to the development of facilities which improve traditional sensors and actuators in a necessary way to automate production systems. In another context, many studies have been carried out aimed at defining a decisional structure for production activity control and the increasing need of reactivity leads to the autonomization of decisional levels close to the operational system. We study in this paper the natural convergence between these two approaches and we propose an integration architecture, dealing with machine tool and machining control, that enables the exploitation of distributed smart sensors and actuators in the decisional system. [ABSTRACT FROM AUTHOR]