Flexibility assessment and management in supply chain : a new framework and applications
This thesis focuses on flexibility issues in supply chain. These issues are becoming more and more important for firms because of the increasingly changing business environment and customer behaviors. Although some of these issues have been tackled in academic research in recent years, but studies have mainly concentrated in conceptual levels and there is little consensus even on the definition of flexibility. This thesis aims at defining a new framework for the supply chain flexibility, proposing quantitative measures of the flexibility and optimizing the use of flexibility, especially in an integrated production and transportation planning context. The new framework of supply chain flexibility is based on classification of different flexibility aspects in a supply chain into three main categories - manufacturing flexibility,logistic chain flexibility and system flexibility. These flexibility types are further distinguished into major flexibility dimension and other flexibility dimension.In order to measure supply chain flexibility from a quantitative point of view, Mechanical Analogy method is particularly discussed. A procedure is established to enlarge and carry out this method in supply chain, provided with a case study to evaluate the flexibility of Louis Vuitton stores.One of the most important issues is to optimally make use of the available flexibility. We investigate an Integrated Production and Transportation Planning problem with given flexibility tolerances, where the production and transportation activities are intimately linked to each other and must be scheduled in a synchronized way. Particularly, heterogeneous vehicles are taken into account. Two mixed integer linear programming models are constructed.Three algorithms are developed and compared with linear relaxation bounds for large sized real life instances and with optimal solutions for small sized instances. These comparisons show the effectiveness of our heuristics in solving real life problems