Supply Chain Game Simulation Report
The Spirit Dragons
Dariusz Swiercz |James Cooper| Moses Reddy Potta |Muhammed Eeman Ali
Summary of Decisions and Results
The goal of the Supply Chain Game Simulation was using the first 730 days of simulation data,
determine what changes needed to be made to the overall system to maximize cash growth by the end
of the simulation on day 1460. To do this, factories, additional capacity, and warehouses could be
purchased to decrease cost per unit, the order quantity and reorder point for each warehouse could be
changed, and the method of transportation, either truck or mail could be changed.
For our team, we first developed what the demand was for the first 730 days for Calopeia and
days 631-730 for the other regions and used that to determine what our capacity needed to be. We
decided that in order to decrease our costs, we would have a warehouse in every region and additional
factories in Sorange and Fardo. Next, we determined that we would reorder to each warehouse in
multiples of 200 and would prioritize having more inventory over optimizing the inventory levels. Once
the factories and capacity were purchased and came online and the raw material order quantity and reorder point were changed, we worked to capture as much demand as possible.
From there, the simulation ran, with the main management coming in prioritizing which region
got orders as which time. Additionally, after day 1430 with the factory going to 0 demand at 1460, we
did our best to minimize inventory remaining at day 1460. At the end of the simulation, our team placed
1st with a total revenue of $21,681,427.44, beating the team in 2nd by roughly $400,000 and far
outpacing the donothing model by roughly $9,500,000. Over the course of the simulation our service
level was just over 90% after day 730, meaning we captured almost all the demand and thus profit
possible.
Development of Demand Forecasts in each Region
The demand forecast was developed by using the historical trend data prior to day 730 of the
simulation. The pattern for Calopeia was seasonal increases and decreases, with the average demand
was 40 drums. For Sorange, the initial demand was low, around 7 drums per day, but it was mentioned
in the game description that the demand will increase linearly. We took this into consideration and
predicted based on a linear increase that Sorange’s demand would increase to an average of 110 by the
end of the simulation. Because of this we had to add capacity in stages as we will discuss later. For both
Fardo and Tyron the average demand value were 16 drums, with no special behavior or fluctuations. For
Entwrop, the demand average was around 12 drums per day, but the order size was 250 drums. We
took this into account by having a larger safety stock due to the higher order quantity, as we will discuss
later. As we will discuss, using these demands, we justified purchases of warehouses, factories, and
capacity in order to capture as much demand as cheaply as possible.
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Analysis and Justification for Factories/Warehouses outside of Calopeia
Below is the profit margin equation:
𝑃𝑀 = 𝑆𝑒𝑙𝑙𝑖𝑛𝑔 𝑃𝑟𝑖𝑐𝑒 − 𝑃𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛 𝐶𝑜𝑠𝑡 − 𝐼𝑛𝑏𝑜𝑢𝑛𝑑 𝑆ℎ𝑖𝑝𝑖𝑛𝑔 − 𝐻𝑜𝑙𝑑𝑖𝑛𝑔 𝐶𝑜𝑠𝑡 − 𝐿𝑜𝑠𝑡 𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡
− 𝑂𝑢𝑡𝑏𝑜𝑢𝑛𝑑 𝑆ℎ𝑖𝑝𝑝𝑖𝑛𝑔
For a scenario where the warehouse and factory are in the same region (excluding Fardo), assuming a
truckload of 200 and the minimum 10 day holding time, the profit margin is:
𝑃𝑀 = $1450 − $1007.50 − $75 − $2.70 − $3.00 − $150 = $211.80 𝑝𝑒𝑟 𝑑𝑟𝑢𝑚
If both the factory and warehouse are in a different region, an additional $75 per drum is
needed, if just the warehouse in is a different region, an additional $50 per drum is needed, and if just
the factory is in a different region, an additional $25 per drum is needed.
Therefore, for each of the mainland regions, it is possible to see whether a factory, warehouse,
or both is justified.
For the maximum number of days a factory or warehouse is available, we discard anything after
day 1430 and knowing it takes 90 days to build a factory and 60 days to build a warehouse, and roughly
14 days to get product to market, we find the following:
Factory Days of Profitable Operation: 1430-730-90-14 = 596 days
Warehouse Days of Profitable Operation: 1430-730-60-10 = 630 days
Entworpe
Long Term Demand: 11.62 units/day
Additional profit captured from having a factory: 11.62*$25*596 = $173,138
Additional profit captured from having a warehouse: 11.62*$50*630 = $366,030
Tyran
Long Term Demand: 15.62 units/day
Additional profit captured from having a factory: 15.62*$25*596 = $232,738
Additional profit captured from having a warehouse: 15.62*$50*630 = $492,030
Based on this information, only adding a warehouse in Entworpe is justified. Sorange and Fardo
are unique situations. For Sorange, the demand starts low but will grow linearly until roughly 110 units
per day by day 1430. Because of this, Sorange will require a warehouse and factory to account for this.
For Fardo, because of the high shipping cost to and from the mainland, a warehouse and factory are
both easily justified here as well.
Analysis and Justification for Capacity Additions
Factory Capacity at Calopeia:
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The Calopeia factory was needed to satisfy demand for all the locations in the simulation initially
and after calculating the average demand of all locations, on day 730, we increased its capacity to 80.
This was done in order to build up inventory faster to support the larger orders needed for the Entworpe
warehouse, while also having more inventory available for the peak season in Calopeia.
Factory at Frado:
Average demand was found to be 16 units. Because of the higher shipping costs, we wanted
Fardo to be self-sufficient. Therefore, we set the capacity at 17, built up inventory, and from there,
Fardo remained self-sufficient throughout the course of the simulation.
Factory Capacity at Sorange:
With the demand rising linearly, there was a definite need to increase capacity throughout the
location. We estimated that based on where the demand was trending to end up by day 1130, we
needed to get to a capacity of 85-90. We started with a capacity of 30 and added 5 capacity every 30
days or so. The reason for doing it this was all the capacity was not needed at the beginning and we
wanted to keep that capital to collect interest. Additionally, all capacity was purchased before day 1100,
because after this point, purchasing capacity would not be justified because the amount of money spent
would not be made back in time.
Analysis and Justification for ROP
For this simulation unlike in the first one, the determination of ROP for each warehouse was
more a matter of feel than calculation. Because of the low holding cost of inventory and the large
amount of profit missed due to missed demand, we decided to focus on building higher inventory for
safety as opposed to trying to determine an ideal quantity.
For the regions with factories (Sorange, Calopeia, and Fardo), we set the inventory levels high
such that for Fardo production never stopped at the factory and at Sorange and Calopeia, any time
Entworpe or Tyran did not have an order in process, the warehouses in Sorange and Calopeia would
continue to be filled. This strategy worked well for us because it allowed us to build inventory during
Calopeia’s down seasons and be prepared for the rise in demand during peak season and as Sorange’s
demand rose.
For the regions without a factory, Entworpe and Tyran, we went about things a different way.
For Entworpe, because the orders were so large and we didn’t know when they would be triggered, we
decided to always keep two orders worth or 500 batches in inventory in that warehouse, so that while
more material was being made, if two orders came in close proximity, we would be in good shape. For
Tyran, we decided to keep roughly 20 days of supply or roughly 300 batches in inventory. With new
shipments taking 10 days, plus 4-6 days to make (Depending on whether Sorange or Calopeia made it),
we wanted to have enough to cover a period of time in case we couldn’t produce at either plant.
Towards the end of the simulation, we changed the Entworpe and Tyran warehouses to 0,
essentially closing them down in order to better manage inventory and at the Calopeia, Fardo, and
Sorange warehouses, we set the ROP high to always keep the factories in production.
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Analysis and Justification for Batch Size
Below is the formula for EOQ:
2∗𝐷∗𝑆
𝐸𝑂𝑄 = √
𝐻
Under normal circumstances we would use this formula to determine economic order quantity. But for
this simulation, the fact that travel via truck uses a truck with 200 spots available, this means that
shipping in increments of 200 will minimize our shipping cost per unit. Therefore for the smaller demand
territories, Fardo, Entworpe, and Tyran, we generally stayed around 200 units per order and for Calopeia
and Sorange we stayed around 400 units per order. Now we deviated from 400 back to 200 at times
because ordering 400 units at a time would basically leave the other warehouses at risk to run out of
inventory based on where our ROPs were. So for the majority of the simulation we stuck with 200
batches per order as it minimized our shipping cost and gave us the most flexibility for shifting around
production to address inventory issues across our warehouses.
Analysis and Justification of Shipping By Truck or Mail to Warehouse
The cost of transportation by trucks is cheaper than by mail and we set the shipping nominally
to trucks. We used trucks to ship to the same region and the same continent due to lower costs.
Mathematically it made sense as the cost per unit of shipping by mail is twice as expensive as by truck
for the same region and continent. As the batch size is 200, for the same region, the cost of truck is
$15,000, which is $75 per drum. It costs $150 to mail the same order. Similarly for the same continent –
truckload costs $20,000 which is $100 per drum. Mailing costs $200 per drum. Even though
transportation by mail was much faster, at the time of relatively stable demand we could not really
benefit from faster shipments, and it would have only resulted in bigger costs. Such strategy allowed us
to reduce costs at the time when our production could satisfy the demand. However, at some point we
started missing orders due to increased demand as we were not shipping fast enough. Particularly
amusing was Entworpe having ordered four times orders of 250 in six days. Because of that, we began
using more mail to catch this demand. The cost were higher, but so were our profits due to capturing
the demand. In the end, Fardo was shipping by mail to Sorange, Tyran and Entworpe – 2 day shipping
helped us satisfy these regions as trucks would have taken 2 weeks and would have resulted in a lost
demand.
Conclusion and Improvements
Overall, our team performed well in this simulation, far outpacing the donothing model and
coming in first-place for the simulation. There aren’t any large scale improvements that needed to be
made, but two changes could have possibly widened the gap between our team and the team coming in
2nd place. First, it would have been beneficial to add an additional 5 capacity at the Sorange facility to
bring the total capacity to 90 batches per day. In the latter part of the simulation where Sorange was
rising and Calopeia’s demand hit its peak season, we began missing orders due to insufficient capacity. I
don’t think it would have been worth it to try and capture all of this demand, but an additional 5
capacity, most certainly would have been justified. The main improvement, one that without this almost
cost us first place, is handling the end-of-life of the product between days 1430 and 1460. We missed
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managing this correctly and ordered 400 units on day 1432 after which we realized these may not sell
causing us to not be in the top place. It ended up working out thanks to us being able to supply an
Entworpe order on day 1450, but otherwise, this additional inventory could have cost us. Having a
better system in place to ensure that inventory was minimized on day 1460 as opposed to the 800
batches we had remaining (roughly $1 million in inventory), would have pushed us even further ahead.
Overall, outside of the end of the simulation, our system worked exactly as predicted. We built
capacity up, were able to handle most of the demand fluctuations, and were able to address inventory
issues in the moment as needed. With some adjustments, I believe our simulation strategy would
improve from good to great if given the opportunity to complete this simulation again.
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