Applications of Discrete Event Simulation in Automotive Systems

This report provides a comprehensive analysis of discrete event simulation (DES) techniques and their applications within the automotive manufacturing industry. It presents three case studies illustrating the use of DES to address various challenges. The first case focuses on optimizing powertrain manufacturing, specifically detecting bottlenecks and improving material handling. The second case explores the application of DES in optimizing the layout of a manufacturing facility, aiming to minimize build-up areas and create space for new projects. The final case study investigates the use of DES to test the production capacity of a flexible manufacturing system (FMS) in an automotive context. The report highlights the benefits of DES, including its ability to optimize system throughput, allocate manpower efficiently, and compare operational options. The study utilizes simulation software like WITNESS to model and analyze different scenarios, emphasizing the importance of DES in addressing complexities like those arising from flexible manufacturing and unpredictable real-world variations in the automotive industry. The report includes references to relevant research and provides valuable insights into how DES can be a powerful tool for strategic decision-making and process optimization in automotive manufacturing.

Running head: Discrete Event Simulation Techniques
Strategic Use of Discrete –Event Simulation Techniques
Name
Institution

DISCRETE EVENT SIMULATION
Case 1: APPLICATIONS OF DISCRETE EVENT SIMULATION IN THE DESIGN OF
AUTOMOTIVE POWERTRAIN MANUFACTURING SYSTEMS
Overview
Manufacturing and assembly of automotive is a complex manufacturing process that
involves the combination of different components. The powertrain is the most critical component
in every automotive (Jayaraman and Gunal, 1997). It is, therefore, important to manufacture
powertrains of high quality for the success of any automotive products. Power train
manufacturers/assemblers must, therefore, optimize the process to realize the best quality.
Objectives and Challenges
1. To detect bottlenecks in the production process (Jayaraman & Gunal, 1997).
2. To optimize material handling process
3. To optimize systems design
4. To design and analyze logistics systems
5. To determine throughput
6. To allocate optimal manpower
7. To compare operational options.
Challenges
The rate of defective products could not be determined with certainty (Jayaraman &
Gunal, 1997).
Solutions

DISCRETE EVENT SIMULATION
A testing model was developed through the application of WITNESS simulation
language, and the performance of the established system was evaluated by measuring the number
of jobs completed per unit time (Jayaraman & Gunal, 1997). The model used approximate
measures of reject rates to generate random rejects for analysis. The set up included four
scenarios.
Scenario 1- holding engines at one point until a test space availed.
Scenario 2- circulating engines in a loop till a test space avails
Scenario 3- a buffer was used for each testing area
Scenario 4- The untested engine circulates and enters a testing pint of one is available
(Jayaraman & Gunal, 1997).
Result and conclusion.
The model for each scenario was a modification of the model for the first scenario.
Through the experimentation, it was determined that scenario 2 was the best of them all. Discrete
event simulation is very useful in optimizing the number and configuration of testing stands to
realize optimal results.

DISCRETE EVENT SIMULATION
Case 2: OPTIMIZATION OF LAYOUT USING DISCRETE EVENT SIMULATION
Overview
BOS automotive products limited is a company that specializes in the manufacture and
sale of accessories for automotive. Particularly the company makes storage systems such as bags,
sun visors, blinds, and safety nets. Before the implementation of Discrete event simulation, the
company had excess build-up area in its production hall loader. It did not have space for new
projects within the same hall (Kurkin & Šimon, 2011).
Objectives and Challenges
1. To minimize build-up area in the production hall
2. To avail space for new projects within the layout of the facility
Challenges
1. Determining which activities could accommodate a reduction in space allocation (Kurkin &
Šimon, 2011).
2. Determining the most strategic place in the layout for new projects (Kurkin & Šimon, 2011).
Solutions
The DES-based solution was designed through a reorganization of the layout to accommodate
the proposed flow of materials (Kurkin & Šimon, 2011). This involved determination of the
shortest paths and those paths that didn’t cut across operations while leaving enough space for
accessing machines for both operations and repairs. The simulation was designed using variants.
Variant A eliminates noise in operation by turning the machine into the route; variant B regulates
the assembly workplaces, variant C creates an extension for the VAG line, variant D changes the
sewing part while variant E changes the arrangement of parts (Kurkin & Šimon, 2011).

DISCRETE EVENT SIMULATION
Result and conclusion.
A multi-criteria table was created to evaluate the optimal position of the process—the project
aimed to generate space for additional projects by combining some production lines. Variant C
was the best for the shift, confirmed through the creation of a simulation model.

DISCRETE EVENT SIMULATION
Case 3: TESTING A FLEXIBLE MANUFACTURING SYSTEM FACILITY PRODUCTION
CAPACITY THROUGH DISCRETE EVENT SIMULATION: AUTOMOTIVE CASE STUDY
Overview
Automotive manufacturing has evolved to become one of the most challenging industries today.
The complexity thereof is created by the increasing trends in the product range, diversity, the
need for smaller lots sizes, and demand for shorter lead times. The use of autonomous
technology equips manufacturing processes with more capacity to deliver and to utilize resources
more efficiently (Rybicka & Enticott, 2016). Flexible manufacturing systems are computer
control sets ups used to give responsively dynamic production systems. With the increased
demand for custom products, the automotive industry pursues the strategy to become more
adaptive to market needs (Rybicka & Enticott, 2016).
Objectives and Challenges
1. To DES as an analytical tool to address the levels of complexity that need addressing to
optimize the FMS set-ups (Rybicka & Enticott, 2016).
2. To measure interactions of system components with one another over specified periods.
3. Evaluate real-world situations for tactical and strategic decision making.
Challenge
1. Unpredictable real word variations (Rybicka & Enticott, 2016).
Solutions
The WITNESS model assumes the total availability of labor and zero transportations
times. The system created variables based on sequence parts, numbers of pallets, machine

DISCRETE EVENT SIMULATION
breakdowns against machine utilization, and throughput to evaluate different values and
sequences to obtain the optimal sequence (Rybicka & Enticott, 2016).
Result and conclusion.
Sequence parameters showed that the extension of manual processes influenced the
performance of FMS. The operating sequence impacts FMS optimality as it influences the feed
rate. The number of pallets must be optimized since its stock-out can starve the FMS, and
overfeeding creates bottlenecks.

DISCRETE EVENT SIMULATION
References
Jayaraman, A. & Gunal, A. K. (1997). APPLICATIONS OF DISCRETE EVENT SIMULATION
IN THE DESIGN OF AUTOMOTIVE POWERTRAIN MANUFACTURING SYSTEMS. Dearborn,
Michigan, s.n., pp. 758-766.
Kurkin, O. & Šimon, M. (2011). Optimization of Layout Using Discrete Event Simulation.
IBIMA Business Review, pp. 1-10.
Rybicka, J. & Enticott, A. T. S. (2016). Testing a Flexible Manufacturing System Facility
Production Capacity through Discrete Event Simulation: Automotive Case Study. International
Journal of Mechanical, Aerospace, Industrial, Mechatronic, and Manufacturing Engineering,
10(4), pp. 668-675.