Postponment
Thu, 10/24/2024 - 12:05 — admin
| Publication Type | Thesis |
| Year of Publication | 2024 |
| Authors | Daniel Ramón Lumbierres |
| Academic Department | Dept. Statistics and Operations Research |
| Number of Pages | 124 |
| University | Universitat Politècnica de Catalunya |
| City | Barcelona |
| Degree | PhD Thesis |
| Key Words | supply chain; postponement; stochastic programming; research |
| Abstract | Speculation i Postponement son estratègies oposades de cadena de subministrament dirigides a avançar o postposar els processos de producción que transformen matèries primeres en productes acabats. Un Punt de Desacoblament d’Ordres de Consumidor, o CODP, és un punt logístic de la cadena on la producció especulativa és emmagatzemada fins a l’arribada d’ordres de demanda, de manera que el posicionament de CODPs caracteritza l’estratègia associada a la cadena de subministrament. Es presenten dos models d’optimització per decidir el Disseny en Xarxa de Cadena de Subministrament òptim i la seva estrategia Speculation – Postponement associada mitjançant un enfoc d’optimització estocástica en dues etapes: el primer model, anomenat ( |
| URL | Click Here |
| Export | Tagged XML BibTex |
Tue, 07/21/2020 - 16:46 — admin
Wed, 07/11/2018 - 11:30 — admin
| 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. |
| URL | Click Here |
| Export | Tagged XML BibTex |
Thu, 08/31/2017 - 23:00 — admin
| 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. |
| URL | Click Here |
| DOI | https://doi.org/10.1016/j.promfg.2017.09.181 |
| Export | Tagged XML BibTex |
Thu, 07/27/2017 - 13:34 — admin
| 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. |
| URL | Click Here |
| Export | Tagged XML BibTex |
Thu, 06/08/2017 - 17:39 — admin
| 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 |
| Export | Tagged XML BibTex |
Mon, 07/18/2016 - 19:11 — admin
| 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 |
| URL | Click Here |
| Export | Tagged XML BibTex |
Mon, 07/18/2016 - 19:04 — admin
| Publication Type | Conference Paper |
| Year of Publication | 2016 |
| Authors | Daniel Ramon Lumbierres; Asier Muguruza; Robert Gimeno Feu; Ping Guo; Mary Hamilton; Kiron Shastry; Sunny Webb; Joaquim Minguella; F.-Javier Heredia |
| Conference Name | 28th European Conference on Operational Research |
| Series Title | Conference Handbook |
| Pagination | 330 |
| Conference Date | 3-6/07/2016 |
| Conference Location | Poznan, Poland |
| Type of Work | contributed presentation. |
| Key Words | research; supply chain; 3D printing; stochastic programming; postponment; modeling; additive manufacturing |
| Abstract | In 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. |
| URL | Click Here |
| Export | Tagged XML BibTex |
|
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.