Optimal Supply Chain Strategy through Stochastic Programming

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

Optimal postponement in supply chain network design under uncertainty: an application for additive manufacturing

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.This work was developed under an Accenture Open Innovation University [grant number I-01326] and was also partially supported by grant RTI2018-097580-B-I00 of the Ministry of Economy and Competitiveness of Spain.Peer ReviewedPostprint (published version

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

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.Postprint (published version

Modelos y algoritmos de distribución de automóviles en el complejo industrial SEAT Martorell

SEA

Speculation – postponement strategies in supply chain network design problems: a modelling framework for new supply chain strategies through stochastic optimization

(English) Speculation and Postponement are opposite supply chain strategies intended to either advance or postpone the necessary production processes to transform raw materials into finished goods. A Customer Order Decoupling Point, or CODP, is a logistic point of the chain where the speculative production is stored until the placement of demand orders, so the CODP positioning characterizes the associated strategy of the supply chain. Two optimization models are presented to decide the optimal Supply Chain Network Design and its Speculation – Postponement strategy through a two-stage stochastic optimization approach: a first one called (OSCS) that assigns strategic and speculation decisions to first-stage variables, and postponement and recourse values to second-stage variables; and a second one called (OSCST) with the same strategic decisions as (OSCS) where speculation and postponement concepts are explicitly excluded of the modelling, so all production flow decisions are left to second-stage variables. The analysis of a study case of (OSCS) shows clear combined Speculation – Postponement strategies for different products joint along with tactical criterions to fulfill demand orders in decreasing order of inventory production added value, as well as significant strategic differences between the original problem and its deterministic equivalent. The solutions of the study cases of (OSCST) keep the observed tactical criterions and show Speculation – Postponement strategies that lay outside of the classical scope. Finally, an extension of the Speculation – Postponement strategy framework is proposed joint along with two measures, the Absolute and Relative Degree of Speculation, to quantify the Speculation – Postponement strategy of any supply chain problem with inventory levels. This PhD thesis is the result of a collaboration between GNOM-UPC, Accenture Technology Labs, Accenture Analytics Innovation Center and CIM-UPC.(Español) Speculation y Postponement son estrategias opuestas de cadena de suministro dirigidas a avanzar o posponer los procesos de producción que transforman materias primas en productos acabados. Un Punto de Desacople de Órdenes de Consumidor, o CODP, es un punto logístico de la cadena donde la producción especulativa es almacenada hasta la llegada de órdenes de demanda, de manera que el posicionamiento de CODPs caracteriza la estrategia asociada de la cadena de suministro. Dos modelos de optimización se presentan para decidir el Diseño en Red de Cadena de Suministro óptimo y su estrategia Speculation – Postponement asociada a través de un enfoque de optimización estocástica de dos etapas: el primero llamado (OSCS) que asigna decisiones estratégicas y de speculation a variables de primera etapa, y decisiones de postponement y valores de recurso a variables de segunda etapa; y un segundo llamado (OSCST) con las mismas decisiones estratégicas que (OSCS) donde los conceptos speculation y postponement son explícitamente excluidos de la modelización, de manera que todas las decisiones de flujos de producción quedan asignados a variables de segunda etapa. El análisis del caso de estudio de (OSCS) muestra estrategias combinadas de Speculation – Postponement claras para diferentes productos junto a criterios tácticos para satisfacer las órdenes de demanda en orden decreciente de valor añadido de la producción almacenada, así como diferencias significantes entre el problema original y su equivalente determinista. Las soluciones de los casos de estudio de (OSCST) mantienen los criterios tácticos observados y muestran estrategias Speculation – Postponement que quedan fuera del alcance clásico. Finalmente, se propone una extensión del marco de estrategias Speculation – Postponement junto a dos medidas, el Grado Absoluto y Relativo de Speculation, para cuantificar la estrategia Speculation – Postponement de todo problema de cadena de suministro con niveles de inventario. Esta tesis doctoral es el resultado de una colaboración entre GNOM-UPC, Accenture Technology Labs, Accenture Analytics Innovation Center y CIM-UPC.DOCTORAT EN ESTADÍSTICA I INVESTIGACIÓ OPERATIVA (Pla 2012

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

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