Modeling of Automobile Assembly Line Performance Using ARENA Simulation Software

Due to the intense competition in today’s business environment, companies must continuously analyze and enhance their existing manufacturing systems. Discrete event simulation, which is especially helpful for simulating queuing systems, involves describing the system as it evolves. The building and study of a simulation model of an existing production line are the main topics of this paper. The findings demonstrate that the current system’s throughput is low due to bottlenecks, extended processing times at workstations, and inefficient resource utilization, all contribute to productivity losses in automotive assembly lines. This paper aims to evaluate the efficiency of the production line performance of automobile assembly lines using Arena modeling and simulation. Data provided by the company’s management is utilized to calculate the processing time and standard time for each step in the production line. Additional information is gathered through direct observation of the assembly line. A car assembly manufacturing line was selected as a case study, and Arena 16.0 software was employed for basic modeling and analysis to achieve these objectives.


INTRODUCTION
In today's rapidly and dynamically changing economic conditions, manufacturing industries face considerable difficulties as manufacturing technology continues to advance in the age of globalization.For instance, they are ensuring customer satisfaction, predicting product demand for the market, increasing factory efficiency, fierce market competition, technological advancements, etc. Manufacturing company managers and engineers are actively looking for ways to deal with significant issues in production lines.These difficulties include problems with inefficiency, long waits, and processing times at workstations, all of which harm production and raise costs.The simulation technique known as discrete-event system simulation is used to study, analyze, and optimize manufacturing systems. (1)The objective of this paper is to give the industry a comprehensive set of tools, techniques, and procedures to allow examination of current worker and machine performance systems using a consequent simulation system model that could be used before an existing system is altered or a new system is built, to reduce the likelihood of failure to meet specifications, to eliminate unforeseen bottlenecks, to prevent under-or over-utilization of resources, and to optimize Simulated events might be discrete or continuous.The system's state is constantly changing throughout time under a constant model.However, in a discrete model, changes can only occur at specific moments.In this study, the performance of workers and machinery at The General Company for the Automotive Industry is modeled using discrete event simulation.An essential step in ensuring that the firm's operations are running smoothly is to simulate the firm's activities using a discrete event simulations program, such as the Arena software, analyze the results of the simulation, and continually seek out and implement solutions to optimize the experimental model and to ensure efficient use of the available resources. (2)

Related Works
Production can be defined as the systematic transformation of one material form into another through chemical or mechanical processes to generate or improve the product's utility for the end user.Therefore, production constitutes a process that adds value. (3)At every phase of processing, there is an incremental increase in value.Discrete-event simulation may be helpful in various disciplines because it enables projects to be studied and their feasibility to be determined without making significant investments.Identifying waste, improving the layout, and working on the line balance for optimizing resources helped improve the productivity of the assembly line. (4)There is a lot of literature on applying simulation to manufacturing systems.Han et al. have used DES models to show the advantages of using VSM as a lean technique.Use simulation to address the issue of quality inspection. (5)A computer simulation analysis was conducted to model production process efficiency, perform product life analyses of the production line, and address the allocation of intermediate buffers. (6)A TV assembly/production line's performance was measured to meet the study's objective.A detailed analysis revealed that the production line had bottlenecks, which were queuing the stations. (4)The research demonstrated the integration of critical lean tools, such as Value Stream Mapping (VSM), with the simulation of a batch production system's current and future states in the leather industry. (7)The study focuses on Simulating the automobile repair shop firm's activities using Arena software to analyze the result. (8)The repair shop's capacity is primarily influenced by the possibility of an event that designates a specific number of vehicles to each intervention category (9) focused on line balancing in a textile business using simulation and optimization approaches.The study aimed to streamline material flows within the company, maximize labor capacity utilization, minimize cycle times and material requirements for processes, and eliminate bottlenecks for efficient capacity utilization. (10)

METHODOLOGY
The research's methodology explained in Figure 1 illustrations.The details of each section of the methodology flow chart are provided in the following paragraphs.
The State Company for automobile assembly manufacturers of SAIPA saloon cars, Selecting the company and production line which correlates with the objective of the study is one of the important aspects in conducting the study.

Data Collection
For simulation and verification, real-world operational data are required.The information was gathered by keeping an eye on the line.Entities per arrival, line configuration, and machine process types comprise the data collected.The company's management provided the processes and standards for each line's operations.The model built into ARENA software is discussed in regular meetings with the center's engineer.

Building of Model & Simulation by ARENA
A comprehensive simulation model has been developed using ARENA simulation software version 14, which is a powerful tool for modeling and analyzing systems.This model represents the assembly line for SAIPA saloon cars.The production operates on a single shift per day, with a total available time of 7,5 hours per shift.This includes a 30-minute lunch break.

Verification and Validation of Model
Simulation models are used to represent real-world systems, but they can never be exact duplicates of those systems.These models provide a simplified version of the system that can be used to analyze and predict its behavior under different conditions.It is important to keep in mind that the accuracy of the model depends on the quality and completeness of the data used to create it.It is essential to thoroughly verify and validate a model for its intended use.Verification ensures correct implementation of a model's design.Validation involves evaluating how closely a simulation model and its associated data resemble the actual real-world system.This comparison involves analyzing and comparing the simulation model outputs with the performance of the existing system.According to the simulation model, the daily production output for five replications averaged 28 cars which aligns with the system's actual daily production of 28 vehicles.This indicates that the simulation accurately reflects the production line.

RESULTS AND DISCUSSION
In this section, we will present and discuss the results of this study, providing a detailed analysis of the findings and their implications.
Designing an effective system requires understanding, data collection, and rigorous verification.
1.The downtime of all machines could be higher on average.2. After analyzing the time data, it is clear that jobs should be distributed evenly.
3. The labor costs associated with the production line exceed the planned budget.There are three main effected parameters on production assembly line: • Workers utilization: The number of workers is more than the planned number.At the same time, most of the individuals in the organization work with little efficiency.

CONCLUSIONS
The existing assembly production line was analyzed, and existing setup bottlenecks were identified in the process.A simulation-based model was developed using the Arena 16 simulation package, and generated results were tested with production line manufacturing mentioned in the case study.All the values used in the comparison were generated by simulation in Arena 16 at five replication lengths for six-month run periods.A discrete-event system simulation is a powerful tool for modeling and analyzing manufacturing systems.It has a good record of applications in the industrial sector.This study illustrates the benefits of the time/cost tradeoff, including the potential advantage of achieving good service levels and helping companies avoid unnecessary investments.The presented work scope can be to improve the cost associated with labor and maintenance costs.

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Washing the body and prepare to assemble -Connecting the fuel tank T0 Connecting the insulation under the dashboard -Connecting the door lock T01 Prepare the main and subsidiary wires -link the hand brake T02 Connecting the brake separator to the pipes -Connecting (wiper motor) with the arms T03 Connecting steering column -Connecting pedals( klieg, fuel, brakes) Bonding of ground insulation and floor covering -Connecting the four door locks T08 10 -Connecting the rear bumper and taillights -Connecting the box lock T09 11 -Connecting the cooling system with the fan -Dashboard placement and instrument cluster side brake to the brick tubes -Installing the double in (Shift Drive) -Installing the engine M01 17 -Connecting the fuel hose -Attaching the exhaust insulator M02 18 -Installing the four wheels -Connecting the front brakes M03 19 -Connecting air ducts and air filter and power tubes -Connecting the radiator F01 20 -Connecting headlights -Connecting the fuse box -Installing water sprinklers for the windshield F02 Connecting the front and back seats -Preparing the battery -Installing the front and back windows F03 22 -Filling the radiator with water -Filling the car engine with oils -Filling refrigerant gas into the cooling system -Filling the tank with fuel -Filling the steering wheel oil F04 23 -Definition and programming of the control panel -Vehicle inspection point by quality control F05 Salud, Ciencia y Tecnología -Serie de Conferencias.2024; 3:828 4

Figure 3 .
Figure 3. Simulation Model of Production Line

Figure 4 .
Figure 4.The output of existing production lines for worker • Work-In-Process (WIP): The current quantity of work in process exceeds the planned amount.Simultaneously, aligning work-in-process (WIP) with an enhanced throughput can be realized through efficient inventory management and process optimization.

Table 1 .
Step by Step Line SAIPA Assembled

Table 2 .
Average times and number of worker in stations

Table 3 .
The relation between number in and out of cars in line

Table 4 .
The calculation of salary costs

Table 5 .
Number of workers with their efficiency percentage

Table 5 .
Number of workers with their efficiency percentage

Table 6 .
The Station name queue and average waiting time