The paper entitled Optimal Postponement in Supply Chain Network Design Under Uncertainty: An Application for Additive Manufacturing (preprint ) has been published in the International Journal of Production Research. This paper is the result of projects Strategical Models in Supply Chain Design, and Digitalizing Supply Chain Strategy with 3D Printing a successful collaboration between GNOM with Accenture Technology Labs (Silicon Valley), Accenture Analytics Innovation Center (Barcelona) and the Fundació CIM-UPC. This study 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. Finally, it presents and analyses a case study for introducing additive manufacturing technologies.

Publication Type	Journal Article
Year of Publication	2021
Authors	Daniel Ramón-Lumbierres; F.-Javier Heredia; Joaquim Minguella-Canela; Asier Muguruza-Blanco
Journal Title	International Journal of Production Research
Pages	5198-5215
Journal Date	07/2020
Publisher	Taylor&Francis
ISSN Number	0020-7543
Key Words	manufacturing; postponement; stochastic programming; supply chain network design; 3D printing; additive manufacturing; research; paper
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.
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DOI	10.1080/00207543.2020.1775908
Preprint	http://hdl.handle.net/2117/327874
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Publication Type	Conference Paper
Year of Publication	2018
Authors	Daniel Ramón-Lumbierres; F.-Javier Heredia; Robert Gimeno Feu; Julio Consola; Román Buil Giné
Conference Name	23th International Symposium on Mathematical Programming
Conference Date	01-06/07/2018
Conference Location	Bordeaux
Editor	Mathematical Optimization Society (MOS)
Type of Work	contributed presentation
Key Words	research; supply chain; postponment; multistage stochastic programming
Abstract	Supply chain management has been widely developed through the evolution of manufacturing, distribution, forecasting and customer behavior, encouraging the introduction of postponement strategies in its various forms. At these strategies, semi-finished goods are stored in certain operations of the chain, called decoupling points, waiting for the placement of demand orders, which trigger production flows from decoupling points to the remainder operations. Such a design problem facing the speculation/postponement paradigm must intrinsically include elements that "unveil" demand orders when they are placed, that is, the modelling approach should keep demand orders as random variables until their placement, when they are disclosed. This work proposes a multi-stage stochastic programming model that decides the optimal allocation of decoupling points, as well as a process selection among alternative designs for any general supply chain case, where the stochastic parameters, demands by period and product, are represented through a scenario tree, which is in turn generated using the forecasting. Both a risk-neutral model and a risk-aversion approach with stochastic dominance constraints are presented and solved with multi-stage instances of test cases based on real manufacturing problems defined in collaboration with the Accenture consultancy company.
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Publication Type	Tesis de Grau i Màster // BSc and MSc Thesis
Year of Publication	2018
Authors	Sandra Orozco Martín
Director	Roman Buil; F.-Javier Heredia; Elena Fernández
Tipus de tesi	MSc Thesis
Titulació	Interuniversity Master in Statistics and Operations Research UPC-UB
Centre	Faculty of Mathematics and Statistics
Data defensa	19/01/2018
Nota // mark	10 MH (A+)
Key Words	teaching; VRP; metaheuristics; MSc Thesis
Abstract	In thisThesis, a variant of the Vehicle Routing Problem is considered where products are distributed from a depot to multiple retail stores using capacitated vehicles and not only the transportation cost but also the lost sales resulting from stock-outs at each location are minimized. Given that the real demand of a particular product at some location during a given time period can only be rougthly estimated, a deterministic and dynamic solution is proposed. This solution is divided into two phases: (i) an overnight optimization determines the set of retailers assigned to each vehicle and computes the optimal routes using the available information; (ii) a dynamic model is used separately on each vehicle in order to reoptimize its path according to the new information made available during the execution of the routes. Two exact formulations and two metaheuristics are proposed for the first phase; an exact formulation is implemented for the second phase. Moreover, an end-to-end solution is developed through the implementation of a visualization tool in R Shiny, which uses MySQL databases and shell calls to AMPL to simulate the process of a whole day.
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A new result of the research project Digitalizing Supply Chain Strategy with 3D Printing with Accenture TechLabs and Center for Analytics has been presented in the Manufacturing Engineering Society International Conference 2017 , MESIC 2017. The study compares different possible production strategies for a product (via conventional technologies and Additive Manufacturing) and assesses the degree of postponement that it would be recommended in order to meet a certain demand distribution. The problem solving is calculated by a program containing a stochastic mathematical model which incorporates extensive information on costs and lead times for the required manufacturing operations. This study was published in Procedia Manufacturing.  

Publication Type	Proceedings Article
Year of Publication	2017
Authors	J. Minguella-Canela; A. Muguruza; D.R. Lumbierres: F.-Javier Heredia; R. Gimeno; P. Guo; M. Hamilton; K.Shastry, S.Webb
Conference Name	Manufacturing Engineering Society International Conference 2017, MESIC 201
Series Title	Procedia Manufacturing
Volume	13
Pagination	754-761
Conference Start Date	18/06/2017
Publisher	Elsevier
Conference Location	Vigo, Spain
Editor	Jorge Salguero, Enrique Ares
ISSN Number	2351-9789
Key Words	Additive Manufacturing; Ultra-postponement; Supply Chain; research; paper
Abstract	The best-selling products manufactured nowadays are made in long series along rigid product value chains. Product repetition and continuous/stable manufacturing is seen as a chance for achieving economies of scale. Nevertheless, these speculative strategies fail to meet special customer demands, thus reducing the effective market share of a product in a range. Additive Manufacturing technologies open promising product customization opportunities; however, to achieve it, it is necessary to delay the production operations in order to incorporate the customer’s inputs in the product materialization. The study offered in the present paper compares different possible production strategies for a product (via conventional technologies and Additive Manufacturing) and assesses the degree of postponement that it would be recommended in order to meet a certain demand distribution. The problem solving is calculated by a program containing a stochastic mathematical model which incorporates extensive information on costs and lead times for the required manufacturing operations.
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DOI	https://doi.org/10.1016/j.promfg.2017.09.181
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Publication Type	Conference Paper
Year of Publication	2017
Authors	J. Minguella-Canela; A. Muguruza; D.R. Lumbierres; F.-Javier Heredia; R. Gimeno; P. Guo; M. Hamilton; K. Shastry; S. Webb
Conference Name	Manufacturing Engineering Society International Conference 2017, MESIC 2017, 28-30
Conference Date	28-30/07/2017
Publisher	Elsevier
Conference Location	Vigo, Spain
Type of Work	Contributed presentation
Key Words	research; Additive Manufacturing; Ultra-postponement; Supply Chain; stochastic programming
Abstract	The best-selling products manufactured nowadays are made in long series along rigid product value chains. Product repetition and continuous/stable manufacturing is seen as a chance for achieving economies of scale. Nevertheless, these speculative strategies fail to meet special customer demands, thus reducing the effective market share of a product in a range. Additive Manufacturing technologies open promising product customization opportunities; however, to achieve it, it is necessary to delay the production operations in order to incorporate the customer’s inputs in the product materialization. The study offered in the present paper compares different possible production strategies for a product (via conventional technologies and Additive Manufacturing) and assesses the degree of postponement that it would be recommended in order to meet a certain demand distribution. The problem solving is calculated by a program containing a stochastic mathematical model which incorporates extensive information on costs and lead times for the required manufacturing operations.
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Publication Type	Funded research projects
Year of Publication	2016
Authors	F.-Javier Heredia
Type of participation	Leader
Duration	11/2016-11/2019
Funding organization	Accenture Technology Labs
Partners	Accenture Technology Labs (Silicon Valley), Accenture Analytics Innovation Center (Barcelona)
Full time researchers	2
Budget	132.532,43€
Project code	I-01507, I-01508
Key Words	research; supply chain; manufacturing; private; project; Accenture
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Publication Type	Tesis de Grau i Màster // BSc and MSc Thesis
Year of Publication	2016
Authors	Daniel Ramon Lumbierres
Director	F.-Javier Heredia
Tipus de tesi	MSc Thesis
Titulació	Master in Statistics and Operations Research
Centre	Faculty of Mathematics and Statistics
Data defensa	27/07/2016
Nota // mark	9.5 Excel·lent MH (A+ with Honors)
Key Words	teaching; supply chain; 3D printing; Postponment; stochastic programming; Accenture; MSc Thesis
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.
DOI / handle	http://hdl.handle.net/2117/88818
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