  In the current competitive environment, it is crucial for manufacturers to
make the best decisions in the shortest time, in order to optimize the
efficiency and effectiveness of the manufacturing systems. These decisions
reach from the strategic level to tactical and operational production planning
and control. In this context, elaborating intelligent decisions support systems
(DSS) that are capable of integrating a wide variety of models along with data
and knowledge resources has become promising. This paper proposes an
intelligent DSS for quality control planning. The DSS is a recommender system
(RS) that helps the decision maker to select the best control scenario using
two different approaches. The first is a manual choice using a multi-criteria
decision making method. The second is an automatic recommendation based on
case-based reasoning (CBR) technique. Furthermore, the proposed RS makes it
possible to continuously update the control plans in order to be adapted to the
actual process quality situation. In so doing, CBR is used for learning the
required knowledge in order to improve the decision quality. A numerical
application is performed in a real case study in order to illustrate the
feasibility and practicability of the proposed DSS.

Artificial Intelligence;Robotics