This project aims to revisit the Supply Chain (SC) design models while considering an endogenous demand sensitive to the environmental performance, which distinguishes our work from existing models where demand is usually modeled as an exogenous parameter that does not depend on the model decisions. Indeed, on the one hand, the environmental performance has become a factor of competitiveness for companies and an important purchasing criterion for customers whether in the context of B-to-B or B-to-C. On the other hand, the environmental performance depends on the decisions undertaken at the design of SC such as location/allocation of production sites, choice of suppliers, selection of manufacturing technologies and transportation modes, etc. While this project is theoretical research-oriented, we are aware of the necessity of validating our assumptions and models by considering real-worlds situations. Two industrial areas are particularly interesting for us: mechanical manufacturing and food industry. With this objective in mind, we will collaborate with “pôle LUTB”, “pôle Viaméca” and the company Diana Food that accepted to provide us with data and relevant examples. First, we have to determine the attributes that affect demand. For instance, the carbon footprint is often considered, but it is a shortcut that is increasingly challenged by some comprehensive approaches such as the Life Cycle Analysis. Then, we have to build mathematical equations establishing the relationships between the environmental performance of a product and its demand level. The next step is to integrate the demand functions established in the previous phases in SC design models and to solve these models. Two main categories of models will be considered: - mixed integer linear programming that can integrate several decisions simultaneously, but often do not allow characterizing analytically the optimal solution, - analytical models that are more generic but offer the possibility of integrating complex demand functions and obtaining analytically the optimal solution. We will use advanced operations research techniques for modeling and solving. Finally, we will dedicate the last step to the validation and experimentation of the proposed models. In fact, we will use our models to show that ignoring the sensitivity of demand to the environmental performance could lead to inappropriate decisions. We will also try to derive insights such as the trade-off between local and international SC when the customers are sensitive to the environmental performance, the impact of customers’ environmental awareness on logistics decisions, the expected gain of a company that takes into account the sensitivity of customers to environmental performance and adapts its SC to meet customers’ requirements, etc.