3D printing

New paper published in International Journal of Production Research.

 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.

Comparison of production strategies and degree of postponement when incorporating additive manufacturing to product supply chains

Publication TypeProceedings Article
Year of Publication2017
AuthorsJ. Minguella-Canela; A. Muguruza; D.R. Lumbierres: F.-Javier Heredia; R. Gimeno; P. Guo; M. Hamilton; K.Shastry, S.Webb
Conference NameManufacturing Engineering Society International Conference 2017, MESIC 201
Series TitleProcedia Manufacturing
Volume13
Pagination754-761
Conference Start Date18/06/2017
PublisherElsevier
Conference LocationVigo, Spain
EditorJorge Salguero, Enrique Ares
ISSN Number2351-9789
Key WordsAdditive Manufacturing; Ultra-postponement; Supply Chain; research; paper
AbstractThe 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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DOIhttps://doi.org/10.1016/j.promfg.2017.09.181
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A new contribution on incorporating additive manufacturing to product supply chains

 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.  

Comparison of production strategies and degree of postponement when incorporating additive manufacturing to product supply chains

Publication TypeConference Paper
Year of Publication2017
AuthorsJ. Minguella-Canela; A. Muguruza; D.R. Lumbierres; F.-Javier Heredia; R. Gimeno; P. Guo; M. Hamilton; K. Shastry; S. Webb
Conference NameManufacturing Engineering Society International Conference 2017, MESIC 2017, 28-30
Conference Date28-30/07/2017
PublisherElsevier
Conference LocationVigo, Spain
Type of WorkContributed presentation
Key Wordsresearch; Additive Manufacturing; Ultra-postponement; Supply Chain; stochastic programming
AbstractThe 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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Optimal Supply Chain Strategy through Stochastic Programming

Publication TypeTesis de Grau i Màster // BSc and MSc Thesis
Year of Publication2016
AuthorsDaniel Ramon Lumbierres
DirectorF.-Javier Heredia
Tipus de tesiMSc Thesis
TitulacióMaster in Statistics and Operations Research
CentreFaculty of Mathematics and Statistics
Data defensa27/07/2016
Nota // mark9.5 Excel·lent MH (A+ with Honors)
Key Wordsteaching; supply chain; 3D printing; Postponment; stochastic programming; Accenture; MSc Thesis
AbstractIn 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 / handlehttp://hdl.handle.net/2117/88818
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Optimal Supply Chain Strategy and Postponement Degree with 3D Printing

Publication TypeConference Paper
Year of Publication2016
AuthorsDaniel Ramon Lumbierres; Asier Muguruza; Robert Gimeno Feu; Ping Guo; Mary Hamilton; Kiron Shastry; Sunny Webb; Joaquim Minguella; F.-Javier Heredia
Conference Name28th European Conference on Operational Research
Series TitleConference Handbook
Pagination330
Conference Date3-6/07/2016
Conference LocationPoznan, Poland
Type of Workcontributed presentation.
Key Wordsresearch; supply chain; 3D printing; stochastic programming; postponment; modeling; additive manufacturing
AbstractIn this contribution we would like to present the results of a research project developed by Accenture and BarcelonaTech aiming at studying the advantages of ultra-postponement with 3D printing using the analytical tools of operational research. In this project a new two-stage stochastic programming decision model has been developed to assess (a) the convenience of the introduction of 3D printing in any generic supply chain and (b) the optimal degree of postponement, the so called Customer Order Decoupling Point (CODP), 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 alternative technologies that can be deployed, defined through a set of operations for manufacturing, assembly and distribution, each one 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 with several case studies from real manufacturing companies are presented and analyzed.
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Digitalizing Supply Chain Strategy with 3D Printing

Publication TypeFunded research projects
Year of Publication2015
AuthorsF.-Javier Heredia; Joaquim Minguella
Type of participationleader
Duration06/2015-07/2016
Funding organizationAccenture Technology Labs
PartnersAccenture Technology Labs (Silicon Valley), Accenture Analytics Innovation Center (Barcelona), Fundació CIM-UPC
Budget25.000$
Project codeI-01326
Key Wordsresearch; supply chain optimization; manufacturing; 3D printing; project; private; competitive; Accenture
AbstractThe aim of this research project is to study the advantages of the ultra-postponement with 3D printing (UP3DP) using the analytical tools of operational research (OR). 3D printing (3DP) is a revolutionary technology that is changing the paradigm of the supply chain management allowing delayed and tailored production under demand. However it is still to determine to which extend producers can take profit of the massive integration of this technology in their supply chain strategy: how to distribute 3DP devices among the different production plants? Which kind of technology is more appropriate? What are the benefits of this integration, both from the point of view of the manufacturer’s profit and client’s experience? We expect to find answers to all these questions with the help of the mathematical optimization models and algorithms of the operational research.
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Accenture Open Innovation university grant

As a part of the Accenture Open Innovation  initiative,  Accenture - Tech Labs has awarded 11 research grants to top universities  around the world to significantly broaden and deepen the relationships between Accenture’s technology research and development (R&D) groups and leading university researchers.

The project "Digitalizing Supply Chain Strategy with 3D Printing", lead by professor F.-Javier Heredia (GNOM-UPC) and professor Joaquim Minguella, (Fundació CIM-UPC), was one of 11 awarded projects. This research project aims at studying the advantages of ultra-postponement with 3D printing by using analytical tools and mathematical optimization models and algorithms, exploring how to transform supply chain management by allowing delayed and tailored production in the location where demand occurs. The project will be developped in collaboration with the Accenture Analytics Innovation Center (Barcelona) and Accenture Technology Labs (Silicon Valley). In the image, the attendants to the first in person meeting, held at the Faculty of Matematics and Statistics  and Fundació CIM, with representants from Accenture Analytics, Accenture Technology Labs, GNOM-UPC and FCIM-UPC.

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