Inventory cost consequences of variability demand process within a multi-echelon supply chain

The bullwhip effect (Lee et al, 1997a) is a known supply chain phenomenon where small variations in end item demand create oscillations that amplify throughout the chain. Different price elasticity of demand influence different changes of demand when prices of items are changing on the time horizon. The variance of the orders at the end user placed on suppliers or on manufacturer increases with the orders flow upstream in the logistics chain. This creates harmful consequences in inventory levels and all kind of inventory costs that may affect added value of activities along the logistics chain and finally affect Net Present Value of all activities in the chain. Traditional model of dynamic supply chain structures is used for this particular study, based on the seminal work of Forrester Diagrams (Forrester 1961). Simulation platform for supply chain management at stochastic demand developed by Campuzano (2006) has been used. VENSIM Simulation Software was previously used for developing these supply chain dynamic models. In the development platform generalised supply chain models are constructed graphically and also analytically. Our study here is to get a dipper insight into the processes in a logistics chain, measuring the inventory cost consequences due to variability demand amplification

Metodologías de formación de familias considerando la minimización de los tiempos de cambio de partida. Aplicación a la secuenciación en empresas cerámicas.

En este artículo se presentan los resultados finales de un estudio realizado sobre la creación defamilias de piezas en empresas del sector cerámico. En este sector, la elevada personalización de los productos demandados por los clientes, ha llevado a la

La programación de la producción en el sector del automóvil. El problema de la segmentación.

La Gestión de la Cadena de Suministro en el sector del automóvil, así como las relaciones entre ensamblador y proveedores de primera línea han adquirido una gran importancia en los últimos años. Se constata la importancia que los procesos de negocio de s

Consecuencias del efecto Bullwhip según distintas estrategias de gestión de la cadena de suministro: modelado y simulación

[ESP] El efecto Bullwhip es uno de los principales causantes de las inestabilidades en el proceso de gestión de demanda que se producen a lo largo de la cadena de suministro. El presente artículo expone un modelo capaz de recrear diferentes escenarios para la gestión de demanda en una cadena de sumi- nistro determinada, con independencia del número de niveles definidos en la cadena de suministro considerada. El modelo, realizado utilizando la metodología de la dinámica de sis- temas, incorpora las variables necesarias para simular dicho proceso de gestión de demanda, como por ejemplo: niveles de inventario, ordenes de reabastecimiento, fabricación, previsiones u otras. Se muestra la utilidad del modelo propuesto, comparando los resultados que ofrecen dos escenarios diferentes, como son los representados por una cadena tradicional y el de una cadena reducida. [ENG] The Bullwhip effect is one of the main causes of instability in the manage- ment demand process along the Supply Chain. We introduce a model which is able to reproduce different Supply Chain Management Scenarios within a determinate Supply Chain with whichever the levels of this one. The model has been built using Systems Dynamics Methodology and incorporates the main variables which are required for simulating the Man- agement Demand Process (Inventory levels, Replenishment orders, manu- facturing process, forecasting etc.). This paper demonstrates the utility of the proposed model comparing the results offered by two different scenarios namely Traditional supply chain and Reduced supply chain

Fuzzy multi-objective optimisation for master planning in a ceramic supply chain

This is an Accepted Manuscript of an article published in International Journal of Production Research on 2012, available online: http://www.tandfonline.com/10.1080/00207543.2011.588267.In this paper, we consider the master planning problem for a centralised replenishment, production and distribution ceramic tile supply chain. A fuzzy multi-objective linear programming (FMOLP) approach is presented which considers the maximisation of the fuzzy gross margin, the minimisation of the fuzzy idle time and the minimisation of the fuzzy backorder quantities. By using an interactive solution methodology to convert this FMOLP model into an auxiliary crisp single-objective linear model, a preferred compromise solution is obtained. For illustration purposes, an example based on modifications of real-world industrial problems is used.This research has been carried out in the framework of a project funded by the Science and Technology Ministry of the Spanish Government, entitled 'Project of reinforcement of the competitiveness of the Spanish managerial fabric through the logistics as a strategic factor in a global environment' (Ref. PSE-370000-2008-8).Peidro Payá, D.; Mula, J.; Alemany Díaz, MDM.; Lario Esteban, FC. (2012). Fuzzy multi-objective optimisation for master planning in a ceramic supply chain. International Journal of Production Research. 50(11):3011-3020. https://doi.org/10.1080/00207543.2011.588267S301130205011Alemany, M.M.E.et al., 2010. Mathematical programming model for centralized master planning in ceramic tile supply chains.International Journal of Production Research, 48 (17), 5053–5074Beamon, B. M. (1998). Supply chain design and analysis: International Journal of Production Economics, 55(3), 281-294. doi:10.1016/s0925-5273(98)00079-6Chen, C.-L., & Lee, W.-C. (2004). Multi-objective optimization of multi-echelon supply chain networks with uncertain product demands and prices. Computers & Chemical Engineering, 28(6-7), 1131-1144. doi:10.1016/j.compchemeng.2003.09.014Chern, C.-C., & Hsieh, J.-S. (2007). A heuristic algorithm for master planning that satisfies multiple objectives. Computers & Operations Research, 34(11), 3491-3513. doi:10.1016/j.cor.2006.02.022Kreipl, S., & Pinedo, M. (2009). Planning and Scheduling in Supply Chains: An Overview of Issues in Practice. Production and Operations Management, 13(1), 77-92. doi:10.1111/j.1937-5956.2004.tb00146.xLai, Y.-J., & Hwang, C.-L. (1993). Possibilistic linear programming for managing interest rate risk. Fuzzy Sets and Systems, 54(2), 135-146. doi:10.1016/0165-0114(93)90271-iLi, X., Zhang, B., & Li, H. (2006). Computing efficient solutions to fuzzy multiple objective linear programming problems. Fuzzy Sets and Systems, 157(10), 1328-1332. doi:10.1016/j.fss.2005.12.003Mula, J., Peidro, D., Díaz-Madroñero, M., & Vicens, E. (2010). Mathematical programming models for supply chain production and transport planning. European Journal of Operational Research, 204(3), 377-390. doi:10.1016/j.ejor.2009.09.008Mula, J., Peidro, D., and Poler, R., 2010b. The effectiveness of a fuzzy mathematical programming approach for supply chain production planning with fuzzy demand.International Journal of Production Economics, In pressPark *, Y. B. (2005). An integrated approach for production and distribution planning in supply chain management. International Journal of Production Research, 43(6), 1205-1224. doi:10.1080/00207540412331327718Peidro, D., Mula, J., Poler, R., & Lario, F.-C. (2008). Quantitative models for supply chain planning under uncertainty: a review. The International Journal of Advanced Manufacturing Technology, 43(3-4), 400-420. doi:10.1007/s00170-008-1715-yPeidro, D., Mula, J., Poler, R., & Verdegay, J.-L. (2009). Fuzzy optimization for supply chain planning under supply, demand and process uncertainties. Fuzzy Sets and Systems, 160(18), 2640-2657. doi:10.1016/j.fss.2009.02.021Selim, H., Araz, C., & Ozkarahan, I. (2008). Collaborative production–distribution planning in supply chain: A fuzzy goal programming approach. Transportation Research Part E: Logistics and Transportation Review, 44(3), 396-419. doi:10.1016/j.tre.2006.11.001Selim, H., & Ozkarahan, I. (2006). A supply chain distribution network design model: An interactive fuzzy goal programming-based solution approach. The International Journal of Advanced Manufacturing Technology, 36(3-4), 401-418. doi:10.1007/s00170-006-0842-6Torabi, S. A., & Hassini, E. (2008). An interactive possibilistic programming approach for multiple objective supply chain master planning. Fuzzy Sets and Systems, 159(2), 193-214. doi:10.1016/j.fss.2007.08.010Haehling von Lanzenauer, C., & Pilz-Glombik, K. (2002). Coordinating supply chain decisions: an optimization model. OR Spectrum, 24(1), 59-78. doi:10.1007/s291-002-8200-3Zimmermann, H.-J. (1978). Fuzzy programming and linear programming with several objective functions. Fuzzy Sets and Systems, 1(1), 45-55. doi:10.1016/0165-0114(78)90031-

Propuesta de un Procedimiento para la Elaboración de un KPI para la Medición de la Bioseguridad en Procesos de Negocio de la Cadena de Suministro Alimenticia. Aplicación en la Industria Mexicana Alimenticia.

[ESP] El Terrorismo Alimentario ha sido definido por la Organización Mundial de la Salud como “un acto o intento deliberado de contaminación de alimentos para consumo humano con agentes químicos, físicos o microbiológicos con el propósito de causar daño o muerte poblaciones civiles, o para interrumpir la estabilidad social, política o económica” (WHO 2008). La Bioseguridad se refiere a los mecanismos de análisis, control y mejora de la prevención de que ocurran estos ataques, por lo que se refiere entonces a una gestión de riesgos. El descuido de este factor, puede provocar una baja visibilidad de los procesos de negocio y un incremento de la probabilidad de contaminación intencional entre los eslabones de la cadena de suministro alimenticia; y por tanto, generar potencialmente altos costes para sus integrantes que la constituyen (Navarrete, et al. 2010)

A performance measurement system for managing mass production and mass customisation contexts

[ENG] In supply chain/networks (SC/N) operating under a mass customisation (MC) strategy, i.e. producing customised products at a price close to that of mass production, real- time and cooperativeness receive special emphasis in order to improve customer responsiveness. The main difference between MC and Mass Production (MP) lies in their logic of operating. For MP, lower prices lead to greater sales; greater sales in higher volumes, higher volumes in lower costs, and lower cost translate into lower prices. Nevertheless, in MC, customisation leads to more satisfied customers and innovation, which both lead to greater sales and higher profits and understanding of customer needs. Thus, MP is efficiency-driven and based in economies of scale while MC is customer-driven and based on offering higher variety of products at affordable prices. This difference of logic is reflected in the processes involved in each approach. Even though both approaches, MC and MP, rely on the same processes i.e. collaborative order management, collaborative planning and scheduling processes in a SC/N context, the degree of interaction among all three processes depend on the strategy followed (MP or MC) as MC demands that all three processes react and adapt when an new customer order is received. Therefore, as both strategies differ considerably in the way they are implemented, the performance measurement systems (PMS) developed for MP need to be adapted to be used for MC. In fact, this situation is even more complex in the case where the same SC/N operates at both strategies, MP and PC, at the same time for different products. For example, in the current business environment, some enterprises usually operating under a MP approach are deciding to assign part of the available capacity of standard products (MP) to configurable products (MC). Then, PMS should evolve and integrate both approaches together in order to reflect the real situation of the SC/N. In order to deal with the management of both types of products under the same PMS, it is necessary to develop a structure that considers both situations and follows a process-based approximation to manage the processes involved. The purpose of this paper is to introduce a PMS for both contexts, called Mass Production/Mass Customisation-Performance Measurement System (MP/MC-PMS) that fills this research gap by including all three characteristics within its structure in order to provide a tool for managing the performance of MP and MP contexts more efficiently and effectively.This work has been developed within the framework of a research project funded by the Spanish Ministry of Education and Science, reference DPI2008-06788-C02-01, and title “PERMACASI- Mass Customisation through Intelligent Supply Chains, with Products of complex and changing Structure/Bill of Materials and Manufacturing Processes”

An Approach to the Industrial Organization Engineering Background in Spain

[EN] In this article we review the historic background of Spanish Industrial Engineering and briefly compare it with its equivalents in the USA and other countries, indicating similarities and differences. We present the actions taken in Spain that have consolidated Organizational Engineering. First, we describe the early history in the older Schools of Industrial Engineering. We follow reporting the new Industrial Organization speciality in the Degree in Industrial Engineering and the Second Cycle of Industrial Organization Engineering that extends until the end of the last century. We present the actual academic organization to adapt to the European Higher Education Area (EHEA) along with the impact that its adaptation has had on the new Degrees, Master Degrees and Postgraduate Courses. Finally, a short section deals with the Organizational Engineering Development Association (ADINGOR), given its importance for the visibility and consolidation of Spanish Organizational Engineering in Spain and elsewhere.Companys Pascual, R.; Lario Esteban, FC.; Vicens Salort, E.; Poler, R.; Ortiz Bas, Á. (2017). An Approach to the Industrial Organization Engineering Background in Spain. Lecture Notes in Management and Industrial Engineering. 11-23. doi:10.1007/978-3-319-55889-9_2S1123ADINGOR. (2008). Documento de Requisitos para la verificación del título de “Grado en Ingeniería de Organización Industrial”. Boletín de la Asociación para el Desarrollo de la Ingeniería de Organización.ANECA. (2006). Libro Blanco. Título de Grado en Ing: de Organización Industrial.Companys, R. (2001). La Organización Industrial en la ETSII. Sevilla: Congreso de Ingeniería de Organización.Fons Boronat, J. M. (2001). La Ingeniería de Organización: Una visión desde la Administració de Empresas. Sevilla: Congreso de Ingeniería de Organización.Mula, J., Díaz Madroñero, M., & Poler, R. (2012). Configuración del Grado en Ingeniería de Organización Industrial en las universidades españolas. Dirección y Organización, 47, 5–20

Detailed description of the Decision Variables in Mathematical Programming Models in a Collaborative Planning Framework of Supply and Distribution Networks (SDN)

[EN] In this paper is analyzed what components make up a Mathematical Programming Deterministic Model in a Collaborative Planning framework of Supply and Distribution Networks (SDN). Although it is only considered the detailed description of its Decisión Variables, its whole description has allowed a Methodology Definition whose main goal is the analytic modelling of the Collaborative Planning Process in SDN¿s with different degrees of complexity, either the Physical point of view or the Organizational-Decisional and Informational one.[ES] En este artículo se ha analizado de qué componentes constaría un Modelo determinista basado en Programación Matemática en un contexto de Planificación Colaborativa en Redes de Suministro/Distribución (RdS/D). Aunque sólo se ha considerado la descripción detallada de las Variables de Decisión, la descripción completa de todos los componentes del Modelo ha permitido la definición de una Metodología que tiene como objetivo modelar analíticamente el Proceso de Planificación Colaborativa en cualquier RdS/D, pudiendo ser ésta de diferentes grados de complejidad, tanto desde el punto de vista Físico (Recursos/Ítems), como desde el punto de vista Organizacional-Decisional e Informacional.Este trabajo se deriva de la participación de sus autores en un proyecto de investigación Feder-Cicyt titulado RdS-2V.RDSINC «De la Planificación a la Ejecución en la Cadena (Red) de Suministro. Dos visiones diferentes y sus herramientas»Pérez Perales, D.; Lario, FC.; Alemany Díaz, MDM. (2010). Descripción detallada de las variables de decisión en modelos basados en programación matemática en un contexto de planificación colaborativa de una Red de Suministro Distribución (RdS/D). Direccion y Organizacion. (42):7-15. http://hdl.handle.net/10251/108668S7154

Optimal inventory reallocation to customer orders in ceramic tile companies characterized by the lack of homogeneity in the product (LHP)

[ES] La Falta de Homogeneidad en el Producto (FHP), se define como la carencia de la homogeneidad requerida por el cliente en los productos. La FHP aparece en empresas en las que los productos finales obtenidos no son homogéneos, dando lugar a la existencia de diferentes referencias (subtipos) de un mismo producto final. Esta falta de homogeneidad supone un problema cuando el cliente requiere ser servido a través de unidades homogéneas de un mismo producto y sus pedidos se comprometen en base cantidades planificadas, cuyas características de homogeneidad finales se desconocen en el momento de adquirir los compromisos con el cliente. Las constantes discrepancias provocadas por la FHP entre las cantidades planificadas y las realmente obtenidas y disponibles, pueden impedir servir pedidos comprometidos previamente. Para resolver este problema, se propone un modelo de programación matemática que permite reasignar el inventario en empresas caracterizadas por la FHP que fabrican contra almacén (Make to Stock: MTS) que combina varios objetivos. El modelo matemático propuesto se ha validado mediante su aplicación a un caso real de una empresa cerámica. El análisis de los resultados indica la obtención de mejoras considerables en la cantidad de pedidos completados a tiempo y en los ingresos por ventas.[EN] The lack of homogeneity in the product (LHP) is defined as the lack of uniformity required by the customer in the products. The LHP appears in companies where the final products obtained are not homogeneous, leading to the existence of different references (subtypes) of the same product. This lack of homogeneity is a problem when the client needs to be served through homogeneous units of a product and commit orders are based on planned quantities, whose final homogeneity characteristics are unknown at the time of acquiring the customer commitments. The frequent discrepancies caused by the LHP between planned homogeneous amounts and those actually obtained and available, can prevent the delivery of committed orders. To solve this problem, we propose a mathematical programming model for the reallocation of inventory in Make to Stock (MTS) ceramic tile companies characterized by the LHP that combines multiple objectives. The proposed mathematical model has been validated by its application to a real case of a ceramic company. The analysis of the obtained results indicates significant improvements in the number of orders completed on time and in sales revenue achieved.El presente trabajo se ha desarrollado parcialmente, tanto en el marco del proyecto de investigación titulado “Personalización en Masa y Cadenas de Suministro Inteligentes, con Productos y Procesos Complejos” (DPI 2008-06788-C02-01), financiado por el Ministerio de Ciencia e Innovación español, como en el proyecto de investigación titulado “Métodos y modelos para la planificación y gestión de pedidos en cadenas de suministro caracterizadas por la incertidumbre en la producción debido a la Falta de Homogeneidad en el Producto” (DPI2011-23597), financiado por el Ministerio de Economía y Competitividad español y por el Vicerrectorado de Investigación de la Universidad Politécnica de Valencia (PAID-06-11/1840), dentro de los cuales se ha tenido oportunidad de validar el funcionamiento del modelo propuesto aplicándolo a una empresa líder en el sector cerámico.Alemany Díaz, MDM.; Alarcón Valero, F.; Oltra Badenes, RF.; Lario Esteban, FC. (2013). Reasignación óptima del inventario a pedidos en empresas cerámicas caracterizadas por la falta de homogeneidad en el producto (FHP). Boletín de la Sociedad Española de Cerámica y Vidrio. 52(1):31-41. doi:10.3989/cyv.42013S314152