@article { ,
	title = {Optimal Supply Chain Strategy through Stochastic Programming},
	editor = {F.-Javier Heredia},
	year = {2016},
	month = {27/07/2016},
	type = {MSc Thesis},
	address = {Faculty of Mathematics and Statistics},
	abstract = {In this project, a new two-stage stochastic programming decision model has been developed to assess: (a) the convenience of introducing 3D printing into any generic manufacturing process, both single and multi-product; and (b) the optimal degree of postponement known as the customer order decoupling point (CODP) while also assuming uncertainty in demand for multiple markets. To this end, we propose the formulation of a generic supply chain through an oriented graph that represents all the deployable alternative technologies. These are defined through a set of operations for manufacturing, assembly and distribution, each of which is characterized by a lead time and cost parameters. Based on this graph, we develop a mixed integer two-stage stochastic program that finds the optimal manufacturing technology to meet the demand of each market, the optimal production quantity for each operation, and the optimal CODP for each technology. The results obtained from several case studies in real manufacturing companies are presented and analyzed. The work presented in this master?s thesis is part of an ongoing research project between UPC and Accenture.},
	keywords = {teaching; supply chain; 3D printing; Postponment; stochastic programming; Accenture; MSc Thesis},
	URL = {http://hdl.handle.net/2117/88818},
	author = {Daniel Ramon Lumbierres}
}

