Efforts aimed at combining Operations Research and Constraint
Programming have become increasingly prominent and successful in the
last few years. It is now widely recognized that integrating inference
in the form of constraint propagation and relaxation in the form of
linear programming can yield substantial results. In this paper we argue
the benefits of global constraints as a basis for such an
integration. We demonstrate the advantages of modeling with global
constraints, explain their operational benefits and illustrate this with
a series of case studies.