This paper proposes a framework for choosing network strategies corresponding to the luxury goods market in general, and more specifically, the luxury alcoholic beverage sector. Its focus on the network orchestration models is an extension of the previous work of Dollet et al. (2010) and Dollet and Díaz (2010). The existing literature on luxury items, supply chains and network orchestration are considered, and in-depth case studies of Heineken, Baccardi-Martini, Diageo and Moët Hennessy (MH) are developed. Finally, from these case studies, a framework is induced to help companies determine and implement their supply network orchestration strategy.

The study explores the suitability of the supply chain network orchestration model for given market profiles based on an analysis of the literature on supply chain and network orchestration, and in-depth secondary data analysis of cases from the alcohol industry (Heineken, Baccardi-Martini, Diageo and Moët Hennessy). Although frameworks for finding appropriate supply chains to a given market profile have been proposed earlier, this study contributes to this body of knowledge by being specifically focused on the luxury alcoholic beverage market and by relying on the recent theory of network orchestration. Both academic and practitioner literature provide ample evidence that in matters of supply chain network design, one size does not fit all. Given the recent evolution of the network orchestration model, the outstanding questions addressed in this paper are:

• Is the supply chain network orchestration model a good fit for the premium beer, wine and spirit market?
• What should be the main considerations and issues when designing such a supply chain network?

Supply Chain Management Journal, Retail Analytics Using SAS, Analytical Processes, Retail Industry, Statistical Techniques, Product Inventory, Customer Satisfaction, Market Segmentation, Marketing Programs, Marketing Mix Models, Econometric Models, Inventory Management System, Marketing Campaigns, SAS Data Mining Software.