Postponment

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.

A multistage stochastic programming model for the strategic supply chain design

Publication TypeConference Paper
Year of Publication2018
AuthorsDaniel Ramón-Lumbierres; F.-Javier Heredia; Robert Gimeno Feu; Julio Consola; Román Buil Giné
Conference Name23th International Symposium on Mathematical Programming
Conference Date01-06/07/2018
Conference LocationBordeaux
EditorMathematical Optimization Society (MOS)
Type of Workcontributed presentation
Key Wordsresearch; supply chain; postponment; multistage stochastic programming
AbstractSupply 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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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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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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Strategical models in supply chain design through mathematical optimization.

Publication TypeFunded research projects
Year of Publication2017
AuthorsF.-Javier Heredia
Type of participationLeader
Duration11/2016-11/2019
Funding organizationAccenture Technology Labs
PartnersAccenture Technology Labs (Silicon Valley), Accenture Analytics Innovation Center (Barcelona)
Full time researchers2
Budget132.532,43€
Project codeI-01507, I-01508
Key Wordsresearch; supply chain; manufacturing; private; project; Accenture
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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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