The Scenario Description 


Mero Showers Ltd is a major Manufacturer and Distributor of Power Showers, Electric Showers, and Mixer Showers. This company has been operating since 1972 and has consolidated its position as one of the major manufacturers of showers for the UK market. Recently, an aggressive marketing campaign from competitors has made the company lose market share. To avoid losing more market share, the Mero showers Ltd has made a significant investment in technology, research and development and new product development. In turn, new variations of the types of showers on offer have been launched to market.

  • The company expect to see returns for their investment and have forecasted the increase in expected orders starting with the prediction of the arrival of different types of shower parts used in the assembly of Power, Electric and Mixer showers (see table 1 below).
  • The probabilistic distribution of inter arrival times for the arriving parts is shown in table 2. Assuming a typical 5 day working week (9:00am-5:00pm) with 1 hour lunch break for all members of staff.
  • After arriving, all parts are unloaded in an unloading bay on a first come first serve basis. One yard operative is tasked with unloading the parts, organising them, quality checking for any defects. The yard operative has been at Mero Showers Ltd for over 5 years and is experienced in the job. The operative can complete the process of unloading, checking and organising ready for assembly in on average 30 minutes. The operative has lunch break from 12:00-1:00pm.
  • Once the arriving parts have been organised and checked, assembly process will be applied in an assembly cell. This assembly cell consists of 4 assembly bays that used to assemble the arrived parts. 4 workers also known as the ‘factory floor’ staff are qualified to assemble any type and variation of showers sold. At each bay a specific type of assembly and quality test are carried out with each ‘factory floor’ member assigned to and trained in a specific assembly operation and specific bay that cannot move between bays. All bays are visited for the assembly to be complete but the sequence of bays visited depends on the variation of the shower being assembled. See table 3 for the showers assembly route/ sequence.
  • The analysis of the data collected showed that at each bay the time taken was on average 35 minutes regardless of the type of shower or shower variation.
  • The 4 workers at the assembly cell have rotating lunch breaks, with workers 1 and 2 breaking up for lunch at 12:00pm-1:00pm, worker 3, 12:30pm-1:30pm, and worker 4, 1:00pm-2:00pm.
  • After the assembly is complete, the completed showers are sent for testing. Although tests are carried out at each stage of the assembly process, the company would like to test again because this is how they have always done things and for their own confidence that their products are suitable for consumers as returns due to technical problems can be very costly. There is only one test bay which is manned by one ‘testing’ operative who has her lunch between 1:30pm-2:30pm. During her lunch break the yard operative covers for her. The average time each test takes between 40 and 60 minutes.
  • Order processing is a key element of order fulfilment. Order processing operations or facilities are commonly called “distribution centres.
  • The main problem is that one of the production job shops based in Midlands faces an order fulfilment problem.
  • The job shop production facility consists of 3 areas, Area 1 for loading of arrived orders, Area 2 for processing and Area 3 for dispatch and delivery. See Figure 1 for the shop floor layout.

Data Collection and Fittings


  • Random arrival of different types of shower parts- probabilistic distribution


Table 1: Probability distribution of arrival of different types of shower parts

Part type number  Shower part types   Probability (%)
1 Power shower parts 33.33
2 Electric shower parts 33.33
3 Mixer shower parts 33.33
  • Distribution of inter arrival times of different types of shower parts


Table 2: Probability distribution of inter arrival times of part types (in minute) 

Inter arrival Times  Probability (%)
7 19
22 32
35 49
  • Sequence of bay visits for assembly of different shower parts


Table 3: Sequence of Bays Visited – The Assemble Cell 

Type of shower parts                        Sequence of visit  
Power shower parts Bay 1 Bay 3   Bay 2 Bay 4
Electric shower parts Bay 4 Bay 1   Bay 2 Bay 3
Mixer shower parts Bay 1 Bay 2   Bay 3 Bay 4
The Coursework Tasks 

For this piece of individual coursework, you are required to apply simulation modelling to deliver the tasks below:


Task 1- After reading the scenario above, provide problem brief, main aim, objectives, tools and techniques, and key performance indicators.

Task 2- Use the tabular form to define and analyse the Mero shower manufacturing problem. This analysis includes decomposing the system being investigated into its main components including entities, attributes, activities, state variables, and events.

Task 3- An appropriate flowchart with detailed explanations.

Task 4- An appropriate Activity Cycle Diagram (ACD) with detailed explanations.

Task 5- Develop a business simulation model for 200 parts to imitate the above manufacturing problem

(“As-Is” situation) in order to increase productivity of the produced shower final products. Five simulation runs are required, at least two experiments (scenarios) to achieve a reasonable: i. Overall simulation time.

ii. Queue size at each. iii. Average waiting time. iv. Resource(s)/ service facility(s) utilisations.

A comparison via Excel diagrams of the “As-Is” scenario with any other improvement scenarios “What-

If” is required.

Task 6- Conclusion and Recommendations for further improvement (bullet points)


Submission Requirements

✓  The Handout Date: Wednesday, 19/05/2021

✓  Online Submission – by 18:00, Monday 5th  July 2021 online submission via Resit 2 AULA. The mandatory submission components are:

o   A detailed report including all the required tasks 1-6, system analysis table, flowchart diagram, Activity Cycle Diagram (ACD), both ‘As-Is’ & ‘What-If’ simulation models/ snapshots & other relevant comparison diagrams (outlines are provided above).

o   A copy of the developed “As-Is” simulation model (.s8 extension)  o A copy of each of the developed “What-If” scenario (.s8 extension)

o   A copy of Excel file including all scenarios allocated in multiple sheet tables (‘As-Is’ & ‘What-If’) and their experiments outputs, analysis plus overall comparison diagrams.

✓  Report Word limitation: 1500 words as an individual report (for the body of the report, excluding Bibliography, References, and Appendices


1.      You are expected to use the CUHarvard referencing format. For support and advice on how this students can contact Centre for Academic Writing (CAW).

2.      Please notify your registry course support team and module leader for disability support.

3.      Any student requiring an extension or deferral should follow the university process as outlined here.

4.      The University cannot take responsibility for any coursework lost or corrupted on disks, laptops or personal computer. Students should therefore regularly back-up any work and are advised to save it on the University system.

5.      If there are technical or performance issues that prevent students submitting coursework through the online coursework submission system on the day of a coursework deadline, an appropriate extension to the coursework submission deadline will be agreed. This extension will normally be 24 hours or the next working day if the deadline falls on a Friday or over the weekend period. This will be communicated via email and as a CUMoodle announcement.

6.      Students are reminded of the requirement to comply at all times with CU’s Academic Conduct policy & procedures. Further details are available via the Student Portal on the CU website and

in the Student Handbook.




– Assessment Criteria 

The following criteria will be interpreted appropriately according to the nature of the assessment and the general framework set by the module aim and learning outcomes.

For a Bare Pass Mark (35%)

•       Work lacks any academic merit as adjudged by the foregoing. For an Excellent Mark (>69%)

•       Show a thorough understanding of the purpose of the activity.

•       Display knowledge of all the relevant principles, theories, and practices and an ability to apply them effectively.

•       Provide evidence of extensive reading beyond that listed, including academic journals.

•       Demonstrate an ability to select critical points, evaluate them and communicate the conclusions effectively.

•       Develop and run models that reflect as realistically and sensibly as possible given situations.

•       Develop and run models that are based on sensible and useful options that go beyond given situations.

•       Provide analysis, discussion, and comment critically on the results produced by models.

•       Provide solutions to business problems that are creative and practicable.

•       Provide sound, supported, discussions of further research that may be needed.

Feedback and Support Method: Individual written feedback to be provided on Moodle/AULA:

A slot of time will be allocated to provide students with a brief on all the assignment elements. Students are welcome to contact the lecturer during his contact hours/ THETA hours for any further assistance. However, there is a clear marking scheme/ direction on the bottom of this coursework directing students on how to prepare and manage their outputs for best achievement.