Managing Bottlenecks in Manual Automobile Assembly Systems Using Discrete Event Simulation

Dewa, Mendon; Chidzuu, Liberty

doi:10.7166/24-2-567

Cited by 7 publications

(5 citation statements)

References 14 publications

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“…The second data is the operating time in each workstation used to simulate the model. This data is a secondary data collected published by M. dewa et al [10].…”

Section: Model Development In Arenamentioning

confidence: 94%

Performance Improvement of Truck Assembly Line through Modeling and Simulation Using Arena Software

Lorou¹,

Muchiri²,

Kuloba³

et al. 2021

OJOp

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Due to global completion in the automotive industry, productivity improvement is vital in these industries. In automotive assembly lines, productivity loses are attributed to bottlenecks, long processing time in workstations and underutilization of resources. The purpose of this research is to evaluate the performance of the production system, analyze the bottlenecks in different parts of the workstation, their capacity and constraints. Also, develop different scenarios for improvement. To achieve this objective, a simulation model was developed using Arena Software to analyze bottlenecks in each workstation. The analysis was based on the queue in the system to identify the longest queue in workstations. After analysis of the assembly line, the result showed that the longest waiting time observed is cab preparation and drop, bumper, engine subassembly fitment of soundproof with respective numbers 14.4, 13.97, 13.00, 13.70 minutes, with respective average number waiting of 24.87%, 17.57%, 25.91% and 23.39%. Using different scenarios for the improvement of the production line showed that the waiting time observed in-cab preparation and drop, bumper, engine subassembly, fitment of soundproof has greatly reduced for the two workstations added and three operators respectively from 14.4 to 3.97 and 3.1, from 13.97 to 3.77 and 2.48, from 13.00 to 4.21 and 3.83, from 13.70 to 0.00 and 2.37 minutes, with respective average number waiting from 24.87% to 5% and 2%, from 17.57% to 5% and 0.9%, from 25.91% to 5% and 4%, and from 23.39% to 0.00% and 3%. To conclude the research study was able to demonstrate the modelling approach using simulation for analysis of a production line assembly. The contribution of this research was so enormous, this study has improved the output production up to 144%.

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“…The second data is the operating time in each workstation used to simulate the model. This data is a secondary data collected published by M. dewa et al [10].…”

Section: Model Development In Arenamentioning

confidence: 94%

Performance Improvement of Truck Assembly Line through Modeling and Simulation Using Arena Software

Lorou¹,

Muchiri²,

Kuloba³

et al. 2021

OJOp

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“…The improvements made in the production line addressed the issues that were restricting the reduction of the cycle time and causing limitations in the production line. [8] worked on batch modeling lines that were the best fit for moderate demand, although, they were categorized by high WIP inventories, which caused an increase in the product cost and led to a large cycle time of the designed line. Improvements were made to achieve flexibility in the production of the final products when the demand of the clients changes for the different types of the product with different demands.…”

Section: Introductionmentioning

confidence: 99%

Assessment and Enhancementofthe Manufacturing Productivity through Discrete Event Simulation

Alsaadi

2022

IOP Conf. Ser.: Mater. Sci. Eng.

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A balanced and resource-efficient assembly line plays a pivotal role in deciding the profit or loss of an industry. In automotive industries, the productivity of the specific materials is highly dependent on several explanatory variables and the processing operations. Moreover, in this 21st century, simulation is considered a powerful and cost-efficient tool for the designing and analysis of manufacturing operations. Therefore, in this research work, a case-study-based assessment of the assembly line in an automotive component vendor company has been proposed through the study of discrete events. Different processing operations are analyzed in terms of the productivity and effective utilization of available resources. The proposed model is set up by taking actual data using time study techniques from each workstation and process along the production line. All the processes, starting from the arrival of raw materials, forging, to the packing of final products are studied and their data is used to set up a model in software. A detailed statistical distribution of the data urged us to simulate the working area in the virtual environment under the different scenarios to define the bottlenecks. The simulation results have elaborated the realistic sense and meaningful contribution towards the performance enhancement of all the processes involved in the production line. Interestingly, Discrete event simulation has helped us to increase productivity as well as reduce the cost by eradicating the less used resources of the assembly line. Based on the results obtained, it is highly recommended to utilize the proposed methodology in the production system for acquiring better-quality and cost-effective products.

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“…Dentro de las técnicas para determinar, analizar y gestionar los recursos que limitan la capacidad de sistemas discretos y discretos-continuos, Roser [12] resume las principales características de cinco métodos de detección de "cuellos de botella". Ejemplos de técnicas para análisis de "cuellos de botella" y sus resultados, se documentan en: Sengupta [13] identifica y clasifica limitantes de capacidad en un sistema de fabricación; Dewa [14] demuestra que un sistema manual de montaje de automóviles puede ser optimizado por medio de la gestión de los "cuellos de botella"; Williams [15] ofrece un enfoque para entender la relación entre los "cuellos de botella", el retraso acumulado y el flujo de una red ferroviaria, con el fin de apoyar las decisiones de operación e inversión en capacidad; Sharda [16] realiza reducción de "cuellos de botella" en diferentes productos en una planta química, utilizando un enfoque general de análisis en sistemas discreto-continuos.…”

Section: Introductionunclassified

Determinación de restricciones de capacidad de producción en proceso de obtención de cobre

Segovia

Garvs

Pozo

2016

Ingeniare. Rev. chil. ing.

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El trabajo consiste en un estudio de Simulación de Eventos Discretos (DES) para determinar las restricciones de capacidad de producción ("cuello de botella") en el proceso de conminución de mineral de cobre sulfurado en una planta minera en Chile y cómo estas restricciones cambian bajo distintos escenarios de operación. El modelo de simulación del proceso de conminución comprende las etapas de chancado, molienda SAG, primaria, secundaria, pebbles y de espesamiento. El objetivo principal de la investigación es determinar los equipos "cuellos de botella" en el proceso mediante un estudio DES que comprende la modelación de la planta y la experimentación de distintos escenarios de operación utilizando el Sw SIMIO®. La metodología del estudio de simulación sigue las etapas propuestas por Kelton y las recomendaciones presentes en la literatura. Para determinar las restricciones de capacidad ("cuellos de botella" principal, secundarios y cambiantes) se emplea el método de Utilización. Los resultados incluyen el modelo DES validado, alcanzando en la simulación de la variable principal de resultado, un error relativo de 0,66%, un error porcentual absoluto medio de 2,67% y una señal de seguimiento del error dentro de ± 3 DAM. Se determinaron los equipos "cuellos de botella" para la condición histórica de operación de la planta en 2014 y su comportamiento en los distintos escenarios de operación ensayados.

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Managing Bottlenecks in Manual Automobile Assembly Systems Using Discrete Event Simulation

Cited by 7 publications

References 14 publications

Performance Improvement of Truck Assembly Line through Modeling and Simulation Using Arena Software

Performance Improvement of Truck Assembly Line through Modeling and Simulation Using Arena Software

Assessment and Enhancementofthe Manufacturing Productivity through Discrete Event Simulation

Determinación de restricciones de capacidad de producción en proceso de obtención de cobre

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