Littlefield Simulation Report

7/25/2019 Littlefield Simulation Report

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LITTLEFIELD SIMULATION REPORT

To be able to give right decision and be successful in the simulation, we tried to

understand the rules in a right way and analyzed yearly forecasts to provide necessary products

to the customers on time (lead time) for maximizing our profit.

We realized that demand was not stable, thus buying new machine and increasing

production capacity was necessary to meet the demand during the peak season. Therefore; we

decided to buy new machines to be ready for the peak season. We learned how to move on

correctly and make appropriate decisions by trying and making mistakes. By saving all sales

numbers, profits and lead times day by day enabled us to make more accurate calculations in

our success story.

Executive Summary

To be successful in the simulation, we tried to develop a strategic plan that will be moreprofitable. Depending on the plan, it was decided to play on $1.500 model. Moreover, we were

aware that it was crucial to invest in machines. It is clearly observed in the demand graph, there

was an increasing trend of demand till around day 180. If we could had supplied the goods

during this period, after the calculations we had made, it was obvious that we would had

covered the initial investment of the machine. In this way, we would had enjoyed the high

profits.

Until Wednesday, most of the time we ranked either in the middle of the list or lower than

the middle position. Sometimes we ranked even under the “donothing” position and dropped to

last position. However, we were investing during that period and everything was done according

to our strategy. In addition, we also followed our competitors day by day. Please kindly refer to

“day over day growth opsmsteam1.xlss” and see the analysis we had done during the game.

We were noting down the each team’s growth in dollar amounts and comparing them

with us before we give our final decision about what to do. The gap was decreasing enormously;

by this comparison, we already knew which team had how many machines, which contract they

are using, how much they are growing and when they bought or sold the machine. Thereby, we

had a broad range of understanding about themselves. For instance, let's assume even we had

consensus that we need to invest in station 3, we observed our rivals (Exhibit 3) prior to making

the investment and execute the decision at the right time. Being proactive rather than reactive

helped us a lot during the simulation especially after we took the leadership position early

Wednesday. Due to unstable demand arrivals, we preferred to analyze our rivals and made our

steps accordingly in align with our own strategy. During the simulation, we suffered from randomdemands a lot! It affected our machine’s queue therefore; costed us as missed opportunities.

On the other hand; focusing on the demand graph in details helped us to observe current

situation and make predictions that led us to give right decisions prior to our leadership position.

Once we took the leadership, we kept eye on growing rates more often. Good news was that we

were always growing more than second team most of the times. After the second half of the

steady demand period, we were quite sure that we would win the game because the gap was

still there and getting slightly greater. Also, our capacity and operational strategy were capable



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of meeting the demand. After that time, we enjoyed the game more. Finally, we were able to win

the game as we expected earlier during the game.

Days between 50-100

We could not make any changes in the simulation for the first 50 days. However;

accumulated data in the first 50 days were very insightful for us. When we analyzed the first 50

days, we figured out that the company could not able to deliver orders in some days. Therefore;

increase in the production capacity was an requirement and according to our calculations we

found out that second contract option (1500 USD per order) was much more profitable than the

first contract (1000 USD per order), if the factory delivers the orders within 6 hours as lead time.

By analyzing the data in the first 50 days, we decided to increase the production

capacity and move to the second contract. Before increasing the production capacity, there

were other actions that could be taken to optimize production line as changing the priority of

machine 2 and changing order lot. We decided to take actions one by one in order to have

enough time to analyze results of our actions.

To sum up, we double the capacity of the 1st machine by increasing machine number in

the first station to decrease the queue in front of first machine. In order to utilize 2 machines in

the first station, we changed lots per order to 2 to be prepared when orders arrive. This time, we

entered the second contract, but we have seen that we were not able to meet the requirements

of contract 2. Based on this fact, we decided to buy the second machine for station 2 and after

that we changed our contract and we chose the second option.

We found out that the FIFO model in machine 2 increased the total time spent in theproduction line. If the step 2 products arrive before to the step 4, step 4 waits and creates a

queue. Therefore; we prioritized the step 4. At that time we have seen an improvement in the

queue in front of the machine 2. From the data given to us, what we understood was step2 was

a very fast step and did not create queue however; tuning machine took a lot of time and it

created a bottleneck. Step 4 took longer time compared to step 2 but it was less than the step 3.

As it can be seen in exhibit 1, in the day 67 we have earned $1,020.13 per job in

average. This was an initial signal that we would not be able to earn $1,500 in the future if the

demand increased. So, we figured out that 2 machines in each station would not be sufficient to

meet future demands. Than we decided to buy 1 more machine to the station 1 and changed

lots per order to 3. In 2 days, we found that lots per order 3 didn’t work since there was 2

machines in station 3. At that point, we have corrected our mistake and took one step back by

changing lots per order to 2.



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Days between 100-200

We figured out that with the “lots per order 2“ method we could not able to utilize the

maximum number of receivers that can be in production line (Work in Process - WIP) which is

100. With this method, the maximum number of receivers that can be in the system is 90. It

means that while the first order is in the factory the maximum that can be accepted could be 30.However; in “lots per order” option there can be 100 receivers in the production line because

while first order of 60 receivers are in the line additional 40 receivers can be processed. It

indicates that full capacity usage of 100 receivers is possible. Depending on that, we decided to

move “lots per order 3” model but as we tried previously we have seen that it didn’t work with

the machines on hand. Therefore; we decided to buy one more machine to the station 3 and

switch to “lots per order 3”. However as it is mentioned in the manual of the simulations at least

one station is independent of lot size. Because of this we couldn’t get as much as efficiency as

we thought by switching to “lots per order 3” model. We’ve figure out this effect later on as we

explained in “Days Between 200-268” part of this report.

After buying the 3rd machine to the station 3, we’ve achieved $1,500 for several months

but on day 122 we’ve seen that we’ve earned $1,300.02. On that day factory completed 14

orders however; in the expected demand curve there were at least 20 more days or equal 14

orders. Because of that we wouldn’t able to meet the order if we go forward with existing

capacity. Thus, we decided to buy the 3rd machine to the station 2 and we achieved having 3

machines per each station.

Days between 200-268

During the peak days, we had 6 days that we achieved less than $1000 in average per

order. We could buy one extra machine to the station 3 to decrease the queue but according toour calculation we figured out that other teams were not able to catch us in the remaining days.

So, we decided not to make any more investment.

On day 217, (very last day to make change) we sold the extra machine in the station 2 to

earn $10.000 extra because according to our analysis that machine couldn’t be utilized between

the days 217 and 268. We could have sold more machines but we didn’t want to take random

order risk. The difference between us and the second team was more than $45.000. It means

that at least extra 4 machines that can be saved (4 X $10.000) so, we wanted to be on the safe

side.



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Appendix:

Exhibit 1

Exhibit 2



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Exhibit 3



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Documents

Littlefield Simulation Report