Critically evaluate and implement principles of systems approach and analysis.

College of Engineering, Design and Physical Sciences

MN5617 System Modelling and Simulation

Learning Outcomes

1. Critically evaluate and implement principles of systems approach and analysis.
2. Describe, critically evaluate and appropriately apply manufacturing concepts to real world industrial systems and to design, plan and solve arising problems that day-to-day management of such systems encounter.
3. Develop the required skills for modelling, simulating and critically analysing performance of deterministic and stochastic systems.
4. Acquire the skills to recognise the elements and rules governing supply chains/logistics and reverse logistics for better management and engineering of these systems.
5. Apply key tools and techniques for planning and critically evaluating the design of enterprise systems
6. Modelling of interactions and negotiations between components of enterprise systems
7. Demonstrate integrated modelling of key processes within manufacturing systems
8. Use simulation and optimisation techniques to identify improvements for Enterprise integration
9. Preparation of written reports
10. Critically evaluate a range of complex scenarios and make informed decisions.
11. Exercise a high level of initiative and personal responsibility.

Every year the assignment for the Systems Modelling and Simulation module has a different theme. For example, in the previous years the assignments had healthcare, banking services, airports, and logistics themes. This year our assignment is about Manufacturing company. All this variation allows you to appreciate how this fundamental subject can be applied to different problems which fundamentally have similar performance indicators. Enjoy this year’s assignment!

XYZ Company produces three types of products- product A, product B and product C. Each product requires a certain number of different types of components (C1, C2… C5) to be assembled. Figure 1 shows the products and their respective components.

Figure 1: Component requirements for each product type

Components C1 to C5 a fed to the production line according to exponential distributions with parameters 50, 45, 52, 80, and 49 (all in minutes) respectively. Assembling times are given by TRIA (10,12,15) for A, TRIA (37,48,55) for B and TRIA (18,20,22) for C.

The plant layout consists of 5 stations. Stations 1 through 4 are machining stations and each component has a separate processing sequence through these stations. Operation times at each machine are shown in table 1. After various machining process, all components go to the fifth station which is the assembly station. Components are put together according to product type.

At each of the machine stations, the highest priority is given to the longest components that have been in the system.

Station 5 has two assembly robots that may be selected at random, but one has failure problems. It breaks down periodically and historical data on these breakdowns have shown a mean uptime of exponential, λ=120 minutes. The time to repair also follows an exponential distribution with a mean of 4 minutes. The system operates 8 hours shift in a day. Transfer times between each two station is TRIA (7, 8, 10) minutes.

Due to increase in demand, and the need to reduce production costs, the company is considering ways to effectively reduce Work-In-Process and reduce throughput times whilst maintaining a good level of resource utilisation. To achieve this, management wants to identify the bottleneck processes and increase their capacities if necessary.

As a systems analyst consulted by this company,

1 Develop a simulation model with appropriate animations of the production system. You may use the system layout in figure 2 for your animation.

2 Identify the bottleneck process and propose alternative system improvement initiatives that will improve the performance factors listed. Model your proposed system and compare results for resource utilisation, Work-In- Process, throughput times and number out of each product type with the original system

3 Discuss your improvements with reference to the original system in a written report to the management of the company. The report should present a strong case for your proposal and clearly show the values of the performance factors before and after your improvement (you may want to use Arena’s output analyser for your comparative studies). Validate the results using statistical means (see lecture notes 5 and associated references).

4 In addition, the company is considering installation of a conveyor system to transfer entities between stations. You are required to simulate the conveyor system (non-Accumulating) and find the best settings (e.g. velocity) that retains the appropriate resource utilisation but could reduce overall time entities remain in the system. Distances between Stations are:

Use Run conditions defined as: Warm-up Period = 30 minutes, Replication Length = 5 days and Number of Replication = 10. The system requires initialisation between replications. Decide on whether to initialise Statistics at Run replications and explain your reasoning why you have made the decision.

Figure 2. Layout of the company’s manufacturing system

Table 1. Component routings and Process times (all times are in minutes and are triangularly distributed)

You are asked to upload your submission on Wiseflow. Make sure that you upload the following files: Assignment Report, All Arena Models (extension *.doe files) relevant to your submission, and a the *.out (Output Summary Report files generated after successful run of your models). The Summary Report is automatically generated by Arena in the same directory that you run and compile your model.

Part of the allocated mark for each part is for intuitive modelling approach, design, animation, and presentation of the simulation models and the report. The value and the results need to be interpreted and explained clearly.

Submission deadline:

Submission Method: via the WISEflow platform

Late submission rules: as per the University’s policy Provision of formative assessment: Not applicable. Marking criteria: attached (Annex I) Collusion)

** All submissions will be electronically checked for plagiarism. ** Please be aware of University regulations related to academic misconduct (e.g. plagiarism,

https://www.brunel.ac.uk/about/documents/pdf/Senate-Regulation-6-2018-11-07.pdf

It is the student’s responsibility to be aware of the University regulations related to academic misconduct (e.g. plagiarism, collusion). For further information please refer to: Senate Regulations | Brunel University London

Annex I: Brunel University Generic Masters-level Grade Descriptors

These generic grade descriptors are intended to be used as a tool throughout the assessment process (in assessment design, marking/grading, moderation, feedback and appraisal) for any assessment set at Masters Level in the University. They are designed to show no disciplinary bias and are not intended to act as surrogate award or award classification descriptors. The grade descriptors should be read in conjunction with the learning outcomes associated with the assessment.

Grade A++

Work of exceptionally high quality, commensurate with publication in a highly esteemed peer-reviewed journal. Clearly demonstrates a sophisticated, critical and thorough understanding of the topic. Provides clear evidence of originality and clearly demonstrates the ability to develop an independent, highly systematic and logical or insightful argument or evaluation. Demonstrates exceptional ability in the appropriate use of the relevant literature, theory, methodologies, practices, tools, etc., to analyse and synthesise at Masters Level. Shows exceptional clarity, focus and cogency in communication.

Grade Band A (A+, A, A-)

Clearly demonstrates a sophisticated, critical and thorough understanding of the topic. Provides evidence of originality of thought and clearly demonstrates the ability to develop an independent, highly systematic and logical or insightful argument or evaluation. Demonstrates excellence in the appropriate use of the relevant literature, theory, methodologies, practices, tools, etc., to analyse and synthesise at Masters Level. Develop a simulation model with appropriate animations of the production system. You may use the system layout in figure 2 for your animation. Shows excellent clarity, focus and cogency in communication. Critically evaluate and implement principles of systems approach and analysis.

Grade Band B (B+, B, B-)

Clearly demonstrates a well-developed, critical and comprehensive understanding of the topic. Clearly demonstrates the ability to develop an independent, systematic and logical or insightful argument or evaluation. Demonstrates a high degree of competence in the appropriate use of the relevant literature, theory, methodologies, practices, tools, etc., to analyse and synthesise at Masters Level. Shows a high level of clarity, focus and cogency in communication.

Grade Band C (C+, C, C-)

Demonstrates a critical and substantial understanding of the topic. Demonstrates the ability to develop an independent, systematic and logical or insightful argument or evaluation. Demonstrates a significant degree of competence in the appropriate use of the relevant literature, theory, methodologies, practices, tools, etc., to analyse and synthesise at Masters Level. Provides evidence of clarity, focus and cogency in communication.

Grade Band D (D+, D, D-)

Provides evidence of some critical understanding of the topic. Demonstrates some ability to develop a structured argument or evaluation. Demonstrates an acceptable degree of competence in the appropriate use of the relevant literature, theory, methodologies, practices, tools, etc., to analyse and synthesise, but not at Masters Level. Provides evidence of effective communication.

Grade Band E (E+, E, E-)

Work that demonstrates significant weaknesses, but which provides strong evidence that Grade D is within the reach of the student.

Grade F

Work that is unacceptable.

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All You Need to Know About MN5617 System Modelling and Simulation

The MN5617 System Modelling and Simulation assignment is designed to provide a deep understanding of how systems in various contexts can be represented, analysed, and simulated. The assignment typically involves both theoretical and practical components, requiring students to apply concepts learned throughout the course. It aims to equip learners with the skills necessary to model complex systems, simulate their behaviour under various scenarios, and interpret the results in meaningful ways.

In this assignment, you are expected to create a model of a given system. This could involve the development of a mathematical representation of a process, machine, or even an entire organisational workflow. The system might be dynamic, involving changing conditions over time, or static, representing a more fixed set of relationships. The key is to break down a real-world process or concept into a simplified, yet accurate, model that can be simulated and analysed.

Once the model is developed, the next step is to simulate its behaviour. This often involves using advanced software tools such as MATLAB, Simulink, or AnyLogic. These tools allow you to run simulations based on your model and observe how the system behaves under different conditions. You might simulate how a production line operates, how traffic flows through a city, or how an ecosystem responds to environmental changes.

The simulation phase is crucial as it provides insights into the system`s performance, helping identify inefficiencies, bottlenecks, or areas for improvement. For example, you may simulate a business process and identify potential delays in a customer service workflow. Alternatively, in engineering applications, simulations might reveal the impact of various design changes on a system`s efficiency or safety.

Interpretation of results is a key aspect of the assignment. After running simulations, you must analyse the output data and evaluate what the results mean in the context of the original system. This might involve comparing different simulation scenarios to see how certain variables affect the system, such as how changes in input parameters impact output results.

The final deliverable often includes a comprehensive report detailing your modelling process, the simulation conducted, and an analysis of the findings. You will need to justify the assumptions made during the modelling phase and explain how the results contribute to understanding the system`s behaviour. Additionally, you may need to propose improvements or adjustments to the system based on the simulation outcomes, making it a practical exercise in system optimisation.

The MN5617 assignment requires a blend of technical proficiency, analytical thinking, and creativity. It tests not only your ability to apply modelling techniques but also your capacity to think critically about how systems work and how they can be improved through simulation. This combination of skills is invaluable in many fields, from engineering and business process management to healthcare and environmental modelling.