Assessment 1 Capacity Simulation Reflective Report

799465139700SCHOOL OF BUSINESS

ASSIGNMENT COVER SHEET

STUDENT DETAILS

Student name: KLIMENT TRAJCESKI Student ID number: 19347484

UNIT AND TUTORIAL DETAILS

Unit name: MANAGING OPERATIONS Unit number: 200865

Tutorial group: Tutorial day and time: THURSDAY 8:30 – 10:30 AM

Lecturer or Tutor name: ARMINE WINSLEY

ASSIGNMENT DETAILS

Title: ASSESMENT 1 CAPACITY SIMULATION REFLECTIVE REPORT

Length: 787 WORDS Due date: 15/12/2018 Date submitted: 10/12/2018

Home campus (where you are enrolled): SYDNEY CITY CAMPUS

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Student’s signature: KLIMENT TRAJCESKI

Note: An examiner or lecturer / tutor has the right to not mark this assignment if the above declaration has not been signed.

What strategy did you use in Challenge 1? Please discuss and refer to your results.

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What strategy did you use in Challenge 2? Please discuss and refer to your results. How challenge 2 is different to Challenge 1? Please discuss by referring to your results.

The challenge was to allocate $3 Million among the three workstations. In the first challenge, the carwash is in constant demand and there is fixed performance at each station. There is no variation and the objective is to maximise profits.

The real issue is the budget. We need to optimise the budget allocation by balancing the capacity and increasing the throughput across the workstations. In my first run I tried to allocate the 3 million equally between the three workstations. This created the least cars washed out of the runs due to the hand dry and windows station having the top total time per unit with only 24 cars per hour, so I recognised that it is the bottleneck of the carwash process and more money needs to be invested into that station.

This strategy improved in my second run with me investing more money towards the hand dry and windows station. I invested $200,000 more on top of the original sum into the station and my total cars washed and total profit increased by around 15 cars. This still wasn’t the optimal strategy in this challenge as there was more room for improvement.

In my third run, I invested even more towards the hand and windows station and put in an extra $300,000. All together with the $200,000 from the last run it ended up to an extra $500,000 on top of the original sum. This ended up to be the optimal strategy with the carwash operating at maximum capacity.

The optimal strategy that was used to efficiently maximise profit as shown by the results is:

1- Vacuum and Trash Removal: $ 1,000,000;

2- Machine Wash: $ 500,000;

3- Hand Dry: $1,500,000.

This strategy created no bottlenecks as capacity is greater than demand. The capacity of each work station is equal to be 37.5 cars / hour. As one car completes a cycle at a station, it can travel onto the next station instantly.

Challenge 1 Results:

In challenge 2, there is fluctuation in capacity at each station and it’s not balanced across the three stations, this means that the bottleneck moves around the stations. One stations output can be another’s input.

At the same time, variability leads to longer cycle and lead times. In challenge 2, there is variability which means the cycle time of the process is the station which retains the longest time. This has also influenced throughput time. With variability, queue time can become larger than processing time if utilization approaches full capacity.

The optimal strategy in the second challenge that was used to efficiently maximise profit as shown by the results is:

1- Vacuum and Trash Removal: $ 1,308,000;

2- Machine Wash: $ 562,000;

3- Hand Dry: $1,130,000.

This strategy moves around the bottlenecks due to variability. The capacity of each work station is balanced. Even with this optimal strategy the simulations provide less profit and longer wait times than in challenge 1 with the cycle time being 1.69 min/car.

Challenge 2 is different to challenge 1 in terms of applying the previous strategy as it’s not suitable due to challenge 2 using the principles of variability for both processes and demand. Variability influences process performance. This precisely has led to longer cycle and lead times, decreased profit and a variable bottleneck.

As investment increases in challenge 2, variability decreases and capacity increases. If you use the challenge 1 strategy your profitability goes down from $825 to $655 which is a reduction of 20%.

Challenge 2 Results:

How have the two challenges of simulation confirmed or changed your ideas on capacity management? What really determines process capacity? Please discuss by referring your results and all relevant concepts.

Variation or variability determines process capacity. As stated by Stevenson (2018) Variations can be disruptive to operations and supply chain processes. They may result in additional costs, delays and shortages, poor quality, and inefficient work systems. This is clearly shown in the carwash simulation with the second challenge being variable with both process and demand slowing down which led to a decrease in profits. This can lead to a decrease in competiveness as the carwash fails to take advantage of their strengths and opportunities.

The carwash should adopt a time-based strategy that focuses on the reduction of time. This can lead to lower costs, productivity and quality of work is higher.

The carwashes process capacity can be affected by a range of different factors. Some factors can be organized by management and some can’t. Some examples of these factors that can affect the business are breakdowns and repairs, operator skill and experience, product or material variations and availability of inputs. These factors generate a gap between design capacity and effective capacity.

The carwash must identify and resolve constraints in order to improve productivity. It must determine critical operations and develop methods to establish reasonable goals which for the carwash is to maximise profits and minimise time spent on each service.

Reference List

Stevenson, W 2018, Operations Management, 13th edn, McGraw-Hill Education, New York.