@article { ,
	title = {Optimal Postponement in Supply Chain Network Design Under Uncertainty: An Application for Additive Manufacturing},
	journal = {International Journal of Production Research},
	year = {2021},
	month = {07/2020},
	pages = {5198-5215},
	publisher = {Taylor&Francis},
	abstract = {This study presents a new two-stage stochastic programming decision model for assessing how to introduce some new manufacturing technology into any generic supply and distribution chain. It additionally determines the optimal degree of postponement, as represented by the so-called customer order decoupling point (CODP), while assuming uncertainty in demand for multiple products. To this end, we propose here the formulation of a generic supply chain through an oriented graph that represents all the deployable alternative technologies, which are defined through a set of operations that are characterized by lead times and cost parameters. Based on this graph, we develop a mixed integer two-stage stochastic program that finds the optimal manufacturing technology for meeting each market?s demand, each operation?s optimal production quantity, and each selected technology?s optimal CODP. We also present and analyse a case study for introducing additive manufacturing technologies.},
	keywords = {manufacturing; postponement; stochastic programming; supply chain network design; 3D printing; additive manufacturing; research; paper},
	URL = {https://www.tandfonline.com/doi/full/10.1080/00207543.2020.1775908},
	author = {Daniel Ramón-Lumbierres and F.-Javier Heredia and Joaquim Minguella-Canela and Asier Muguruza-Blanco}
}

