Methodology for inventory planning of returnable transport items in a beverage distribution network

Autor: Ardila Gamboa, César David
Přispěvatelé: Ballesteros Riveros, Frank Alexander, Javier Arturo Orjuela Castro, Carlos Alberto González Camargo
Jazyk: Spanish; Castilian
Rok vydání: 2018
Předmět:
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Popis: Introducción: La logística inversa es una serie de procesos donde productos, materiales y otros recursos se recuperan de los clientes y vuelven a la empresa. Los ítems de transporte retornables son ampliamente utilizados para soportar las operaciones de logística inversa en cadenas de abastecimiento de ciclo cerrado en empresas y organizaciones y proyectar y planear el comportamiento de estos inventarios facilita su control y mejora su desempeño. El análisis envolvente de datos se aplica para medir la eficiencia relativa de escenarios de planeación. Objetivo: Aplicar una metodología de planeación de ítems de transporte retornables, con un enfoque de simulación con dinámica de sistemas, para la toma de decisiones basada en proyecciones del comportamiento del sistema productivo, permitiendo la continuidad de la operación en empresas productoras y distribuidoras de bebidas. Método: A partir de una revisión de la literatura y el marco contextual de una empresa productora y distribuidora de bebidas, se analizan las variables involucradas para diseñar, estructurar y aplicar una metodología de planeación de inventarios para ítems de transporte retornables, con la novedad de contemplar dos niveles: botellas y cajas. Se recopilan datos para las variables de entrada del modelo de dinámica de sistemas basado en el software Vensim, se proyecta el comportamiento del sistema y calculan 8 indicadores de desempeño en 24 escenarios. Se evalúa la efectividad de la metodología a través de la comparación de los indicadores de desempeño mediante la técnica de análisis envolvente de datos. Resultados: Se definen las variables de la planeación de inventarios para los ítems de transporte retornables, se recopilan los datos y se integran al modelo de dinámica de sistemas y se calculan indicadores para cada escenario. El modelo de análisis envolvente de datos se ejecuta y se determinan las eficiencias relativas para cada escenario. Se realiza un análisis de frontera y se analizan las variables a través de un análisis de sensibilidad. Se identifican 5 variables clave que incrementan el desempeño de los escenarios hasta en un 10% de eficiencia relativa. Conclusiones: La planeación de ítems de trasporte retornables y la logística inversa ofrece numerosas ventajas en empresas de diferentes sectores. El enfoque en dinámica de sistemas facilita el diseño, integrando 84 variables en un modelo de planeación. El modelo permite simular el comportamiento del sistema en el tiempo, obteniendo indicadores de desempeño que evalúan el sistema en un horizonte de tiempo. El modelo de análisis envolvente de datos identifica qué escenarios tienen un rendimiento relativo superior, y facilita a los gerentes de logística tomar decisiones informadas sobre las variables del sistema. TABLA DE CONTENIDO VI LISTA DE TABLAS IX LISTA DE FIGURAS 1 RESUMEN 2 ABSTRACT 3 CAPÍTULO 1 INTRODUCCIÓN 4 1.1. Objetivos 5 1.1.1. Objetivo General 5 1.1.2. Objetivos Específicos 5 1.2. Justificación 6 1.3. Planteamiento del Problema 7 1.3.1. Análisis Causa - Efecto 8 1.3.2. Análisis DOFA 9 1.4. Hipótesis y preguntas de investigación 11 1.5. Alcance y delimitaciones 12 1.6. Impacto del proyecto 12 CAPÍTULO 2 METODOLOGÍA 13 2.1. Diseño metodológico 13 2.1.1. Tipo de estudio 13 2.1.2. Participantes de la investigación 14 2.1.3. Herramientas, aparatos, materiales o instrumentos 14 2.1.4. Método de análisis e interpretación de los datos 15 2.1.5. Etapas del proyecto 15 2.2. Metodología de desarrollo del proyecto 16 2.2.1. Variables del modelo de planeación 16 2.2.2. Resultados esperados 16 2.2.3. Cronograma del proyecto 17 CAPÍTULO 3 ANTECEDENTES Y ESTADO DEL ARTE 18 3.1. Conceptos consultados 18 3.2. Administración de la cadena de abastecimiento (SCM) 20 3.2.1. Procesos logísticos en la cadena de abastecimiento 21 3.2.2. Factores críticos de éxito de la administración de la cadena de abastecimiento 25 3.2.3. Evolución a la administración de cadenas de abastecimiento sostenibles 27 3.2.4. Industrias y sostenibilidad 30 3.3. Logística inversa en cadenas de abastecimiento de bucle o ciclo cerrado (CLSC) 31 3.3.1. El retorno como principal entrada de las CLSC 32 3.3.2. La recuperación como núcleo de las CLSC 34 3.3.3. Factores críticos de éxito de una CLSC 37 3.4. Ítems de transporte retornables (RTI) 37 3.4.1. Administración de los RTI 38 3.4.2. Administración del ciclo de vida de los RTI 39 3.4.3. Diseño de productos basados en RTI 40 3.5. Modelos para la planeación de inventarios de cadenas de abastecimiento con logística inversa. 41 3.5.1. Historia de los modelos matemáticos para la planeación 42 3.5.2. Modelos según su objetivo 42 3.5.3. Modelos según su metodología 43 3.5.4. Análisis de la literatura según sus variables 46 CAPÍTULO 4 MARCO DE REFERENCIA 52 4.1. Antecedentes del proyecto 52 4.1.1. Modelo de planeación de envases retornables 52 4.1.2. Modelo operativo de botellas retornables (por CD) 53 4.1.3. Modelo operativo de requerimientos de cajas plásticas 53 4.2. Marco contextual 53 4.2.1. Características del producto, el embalaje retornable y el consumidor 53 4.2.2. Proceso de planeación 55 4.2.3. Características del diseño de la red 60 4.2.4. Procesos de Logística Inversa 61 4.2.5. Plan de abastecimiento 62 CAPÍTULO 5 RESULTADOS 64 5.1. Entregables del marco teórico y contextual 64 5.2. Documentación del diseño de la metodología y herramienta de planeación funcional 64 5.2.1. Parámetros del modelo 69 5.2.2. Variables Intervinientes 76 5.2.3. Variables Dependientes 81 5.2.4. Indicadores de desempeño 85 5.2.5. Diseño del modelo DEA 87 5.3. Diseño de la Metodología de Planeación 88 5.3.1. Identificación de las variables relevantes del sistema 89 5.3.2. Recolección de datos históricos y proyectados para las variables de entrada 89 5.3.3. Integración de los datos recopilados a la metodología 90 5.3.4. Recopilación de resultados 91 5.3.4.1. Revisión de la coherencia de los resultados 91 5.3.5. Prueba y Error para identificación de variables en búsqueda de mejoramiento 92 5.3.6. Aplicación del modelo DEA para comparar resultados 92 5.3.7. Análisis de Sensibilidad sobre las variables modificadas 92 5.3.8. Conclusiones de la aplicación de la metodología 93 5.4. Protocolo con el paso a paso de la aplicación de la metodología 93 5.4.1. Recolección de datos 94 5.4.2. Integración de los datos recopilados a la metodología 96 5.4.3. Recopilación de resultados 98 5.5. Metodología aplicada, resultados del modelo 99 5.5.1. Aplicación del modelo DEA 100 5.5.2. Análisis de Sensibilidad y de frontera eficiente 103 5.5.3. Conclusiones de la aplicación del modelo DEA 104 5.6. Propuesta de implementación 105 5.6.1. Objetivo general de la implementación 106 5.6.2. Objetivos específicos de la implementación 106 5.6.3. Aceptación de la propuesta 106 CAPÍTULO 6 CONCLUSIONES Y RECOMENDACIONES 108 BIBLIOGRAFÍA 112 APÉNDICE 1 120 ANEXO 1 121 ANEXO 2 122 Introduction: Reverse logistics is a series of processes through which products, materials and other resources are recovered from customers and returned to the company. Returnable transport items are widely used to support reverse logistics operations conforming closed-loop supply chains in companies and organizations, and projecting and planning of the behavior of these inventories improves their control and performance. Data envelopment analysis is applied to measure the relative efficiency of planning scenarios. Objective: To apply a planning methodology for returnable transport items, using a simulation approach with systems dynamics, for decision making based on projections of the behavior of the productive system, allowing the continuity of the operation in companies producing and distributing beverages. Method: From a literature review and the context of a company that produces and distributes beverages, variables are identified and used for the design, structure and application of a methodology for inventory planning for returnable transport items in two levels (bottles and crates). Data is collected for the input variables of the system dynamics model based on the software Vensim, the system behavior is projected and 8 performance indicators are calculated through 24 scenarios. The effectiveness of the methodology is evaluated through the comparison of the performance indicators through the technique of data envelopment analysis. Results: The inventory planning variables are defined for returnable transport items, the data are collected and integrated into the system dynamics model and indicators are calculated for each scenario. The data envelopment analysis model is executed and the relative efficiencies are determined for each scenario. A frontier analysis is carried out and the variables are analyzed through a sensitivity analysis. Five key variables are identified that increase the performance of the scenarios up to 10% of relative efficiency. Conclusions: The planning of returnable transport items and reverse logistics offers numerous advantages in companies from different sectors. The focus on systems dynamics facilitates the design of the planning model, integrating 84 variables. The model allows to simulate the behavior of the system over time, obtaining performance indicators that evaluate the system in a time horizon. The data envelopment analysis model identifies which scenarios have a higher relative performance, and makes it easier for logistics managers to make informed decisions about the system’s variables.
Databáze: OpenAIRE
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