1. Third Annual ERPsim Logistics
Game Analysis and Strategy
Company Sponsor: Consumers Energy
Company Mentor: Corey Nykamp
By: Ben Fraser, Terry Bulgarelli, Jordan McClain, Cody Forst
3. Executive Summary
The ERPSIM has been a good learning opportunity for our team in many different
ways that we will continue to carry with us throughout the rest of our lives. When
we first began the competitive business simulation practice we had a limited view
point as to how we should compete against our fellow peers in the upcoming
competition. Over time, through a deep analysis of the data over many practice
sessions, we can now confidently say that we have come together as a highly
efficient competitive team. There are a number of data points we have now analyzed
that we hope will enable us to achieve success. Among these data points we have
deemed a few of them to be crucial to our performance.
The first data point that we view as being highly important is the forecasted demand
data. This data allows us to look at how much we can expect to sell each day for all
of our products by region. Without this knowledge, maximizing profitability would
be almost impossible. It should also be mentioned that while this data is important,
it quickly loses its value under the circumstances of our team stocking out of a
product.
That being said, the next important data point deals with our inventory levels in
relation to the forecasted demand. Understanding the relationship between demand
and inventory levels allows us to minimize our overall inventory warehouse
carrying costs. In addition to minimizing the carrying costs, this data also provides a
guideline as to our inventory refresh cycle times. In other words, we are able to use
the data to lower our logistics shipping costs by sending out fewer shipments per
round.
Finally, we observed how our prices affected our sales figures and the quantity sold
each day. We found that by starting the game off at a low initial price we are able to
attract a large quantity of customers. After we have established a solid customer
base, we then steadily increase the price of each product until we have reached a
price threshold. This price threshold represents the price at which we can maximize
profitability while still maintaining a competitive price in the minds of our
customers. In conclusion, we strongly feel that we will be successful in the ERPSIM
competition by utilizing the strategies within this guide.
4. Opening
We have all experienced the ERPsim game multiple times before this event and we
are confident in our skills. All of the members communicate data effectively and
have SAP experience. This report will not only show our performance, but how we
plan to perform at the main event. The overall goal of the ERPsim game is to
generate the most overall profit. We can do this by minimizing interest expense,
marketing expense, cost of goods movement, and converting from purchase
requisitions to purchase orders.
Agenda
The problems we are trying to solve with this report is how to analyze the data
provided through the ERPsim game, use the data to make efficient and effective
decisions, and to evaluate what we can do better as a group and also how we should
react to game time decisions. We know as a group that we used the time in between
practice sessions data to formulate a logical strategy.
Consumers Behavior
In the beginning, the competition between the two groups of twenty will be very
competitive because low prices is ultimately what draws customers in to your
stream of revenue. Once a customer is in your stream, they will remain loyal to your
company under the following circumstances. One, you must have a competitive
price to draw them initially in they will stay unless you hit the individual customers
tolerance. What this means is you have the ability to increase the price without
necessarily losing the customer, but to a certain and unknown extent. This also
gives teams the crucial ability to exploit each customer and increase the teams
overall gross margin.
Finding a safe price increase should be included in every team’s strategy. If a team
increases the price of a specific trading good past the tolerance level, then the
customer leaves to a competitor who has the lowest price. Remember that this is a
game of numbers, so this is a critical area to take an advantage of.
5. Pricing
We supply six types of dairy products; milk, cream, yoghurt, cheese, butter, and ice
cream. Customers purchase these products off us depending on our price. For our
pricing strategy, the main focus is to capture customers within the first three days,
have a strong profit margin, increase price slowly, maintain customers by finding
their price tolerance, and creating demand for products when demand is low. We
achieve this strategy by analyzing data and finding what price the customers buy the
product at. The tolerance of customers is important because they are extremely
price sensitive and will find a new supplier if the price is too much.
Day One
Starting round one day one is important to capturing our customers. Prices are set
before we begin the game so we need to adjust those in order to lure in customers.
We will set our prices according to the average of the cost of the product and the
price tolerance. This will insure that we will lure in customers with our cheap prices
while still maintaining profitability.
Setting an Initial Price
Product
Code Desc. Cost
Initial
Price
T01 Milk $22.95 $25.48
T02 Cream $72.07 $73.66
T03 Yoghurt $25.85 $29.43
T04 Cheese $82.68 $84.00
T05 Butter $59.88 $62.69
T06
Ice
Cream $43.15 $45.83
Steady Increase in Price
In order to increase profits for our team, we will slowly increase prices throughout
the game. Increasing our prices by nickels or pennies at a time will make our team
more profitable. When we increase the prices we will make sure not to go over our
price tolerance, that way we can maintain our customers while they pay more for
the same product.
6. Price tolerances
Price tolerances are very important when setting prices and maintaining customers.
A price tolerance means that a customer will buy a product at a certain price at max
and nothing over that. We can find price tolerances by looking at when we sell a
product and compare them to previous prices and identify when we can maximize
the amount of sales and customers. According to round one and two data, we were
able to find the max price at which a customer will buy our product. The more we go
over these price tolerances, the more customers we will lose. Keeping our prices
below the price tolerance is important to maintaining customers and having a
healthy profit margin.
Price Tolerance
Product
Code Desc. Price Tolerance
T01 Milk $28.00
T02 Cream $75.25
T03 Yoghurt $33.00
T04 Cheese $89.00
T05 Butter $65.50
T06 Ice Cream $48.50
Creating Demand
Creating demand for products when demand is low will also help increase products.
We can create this demand by lowering our prices depending on what product is
selling the least. We can see when demand is low for a product when doesn’t sell as
much in previous days. So when we find a demand decrease, we will lower the price
slightly. This will increase the demand for our product because the price is low,
increasing our sales. It is important not to lower the price to the cost of the product.
Making the price the same as the cost will make our company unprofitable.
Logistics
Push or Pull
We have decided to pull because it is simpler and involves less risk. Pulling will put
a cap on out products and decrease our group’s chance ofnot stocking out. We will
pull every time storage capacity is approaching towards 10,000 units. Our strategy
includes minimizing the times we pulling to increase our profit.
Converting from a Purchase Requisition to Purchase Order
Converting from a Purchase Requisition to Purchase Order costs 1,000 euros. What
this means is every time we order trading goods from the manufacturer, it costs
7. 1,000 euros to get those trading goods to our warehouse. Our strategy is to
minimize this conversation to increase our profit.
Cost of goods movement:
The cost of goods movement the price we pay to move trading goods from our
warehouse to the three regions. Teams have to pay 100 euros to move trading goods
to each distribution channel. Our strategy is to minimize the cost of goods
movement to increase out profit.
Planned Independent Requirements(PIR):
Based off of previous games, our group decided to set storage capacity to 14,000
units because that amount of stock is almost guaranteed not to stock out. After
setting the storage capacity, we decided to look at the general information off of the
game guide. By doing this, we found out the percentage of capacity that the game
starts every group at. We then took this percentage to find out our units per
purchase order.
PIR’s set based on a storage capacity of 14,000 units
Product Code: Description: Percent of
Capacity:
Units:
T01 Milk 31% 4,340
T02 Cream 10% 1,400
T03 Yogurt 23% 3,220
T04 Cheese 12% 1,680
T05 Butter 13% 1,820
T06 Ice Cream 10% 1,400
Marketing
Since marketing has a minimal effect, we have decided to not spend any money on
marketing.
8. Demand Forecast andInventory Levels
Key Value Points
Low – Red
Mid –Yellow
High – Green
Total Sales (In Units)
Round 1 Round 2 Total
Butter 2,075 2,003 4,078
Cheese 2,775 3,286 6,061
Cream 1,541 2,152 3,695
Ice Cream 2,714 3,377 6,091
Milk 8,653 10,309 18,962
Yogurt 4,555 4,459 9,014
Total 22,313 25,586 47,899
Total Sales in Both Rounds (In Euros)
North South West Grand Total
Butter 62,358.95 168,000.50 36,819.55 267,179
Cheese 97,121.15 52,235.35 387,994.25 537,350.75
Cream 46,436.92 161,329.92 67,991.79 275,758.63
Ice Cream 170,012.14 69,588.01 52,503.28 292,103.43
Milk 328,813.60 66,033.50 123,771.50 518,618.60
Yogurt 59,611.50 179,424.35 46,142.70 285,178.30
Total 764,354.01 696,611.63 715,223.07 2,176,188.71
9. Quantity of Sales by Region (Round 1)
North Round 1 South Round 1 West Round 1
Butter 442 1,295 338
Cheese 545 260 1,970
Cream 304 889 348
Ice Cream 1,607 527 580
Milk 5,559 999 1,985
Yogurt 829 2,934 792
Percentage of Sales by Region (Round 1)
North Sales Ratio 1 South Sales Ratio 1 West Sales Ratio 1
Butter 21.3% 62.41% 16.29%
Cheese 19.24% 9.37% 70.99%
Cream 19.73% 57.69% 22.58%
Ice Cream 59.21% 19.42% 21.37%
Milk 65.51% 11.55% 22.94%
Yogurt 18.20% 64.41% 17.39%
Quantity of Sales by Region (Round 2)
North Round 2 South Round 2 West Round 2
Butter 513 1,267 223
Cheese 551 329 2,406
Cream 318 1,272 562
Ice Cream 1,935 924 518
Milk 6,360 1,420 2,529
Yogurt 1,059 2,724 679
10. Percentage of Sales by Region (Round2)
North Sales Ratio 2 South Sales Ratio 2 West Sales Ratio 2
Butter 25.61% 63.26% 11.13%
Cheese 16.77% 10.01% 73.22%
Cream 14.78% 59.11% 26.12%
Ice Cream 57.30% 27.36% 15.34%
Milk 61.69% 13.77% 24.53%
Yogurt 23.75% 61.09% 15.16%
Maximum Quantity Sold to One Customer
North Round 1 South Round 1 West Round 1
Butter 59 60 55
Cheese 42 43 44
Cream 52 53 53
Ice Cream 83 83 79
Milk 145 136 147
Yogurt 121 124 116
This information has been retrieved from a practice run of the ERPsim game on
February 16th. The data we have retrieved can tell us a lot about the market. We
can tell that the biggest seller in units is milk followed by yogurt. Not only can we
see that the milk is the biggest seller, but we can see that milk sells the most in the
north and the least in the south. Even though milk sold the most units overall,
cheese was our product that brought in the most money. We sold about one third of
cheese as we did milk and still made more money overall. This type of information
is vital for us to make a great business strategy.
The most crucial part of the ERPsim game is the beginning. The beginning of the
game is where we can make or break our business. We have a set amount of
starting inventory and it is important that we do not stock out. If we stock out, we
lose the customers that were just buying from us.
Our inventory starting levels are distributed among the different regions based on
data from the Percentage of Sales by Region (Round 1) table shown in the demand
forecasting section. After figuring out our starting inventory levels, we then
proceeded to set our ideal inventory to be distributed according to the same
Percentage of Sales by Region (Round 1) table data. This will help our start to the
game tremendously as we can try to capture as much of the market base as possible.
11. We can replicate this with every trading good we sell as well. The data was very
consistent for the two rounds we played through. The only trading good that
changed was ice cream in the second round, but that was due to a stock out. We can
use the ratios the Percentage of Sales by Region (Round 1) table to help guide our
decisions and the raw numbers to help guide the amount we should distribute. This
should help keep our warehousing costs down.
In addition to using the Percentage of Sales by Region data in order to keep our
inventory levels down, we also created a formula to indicate the best minimum
stock levels for each product by region. To find this information we took the average
amount of each product sold per region and multiplied that number by the average
amount of transactions performed by customers daily for those same products.
This data can also help us create our planned independent requirements. We know
that from the Total Sales table how much we sold of each product in each round. We
can use this information to see how much of each product is sold on average per
day. For example, we sold 8,653 units of milk in round one. If we divide that
number by 20 days, we will get an average of 433 units of milk sold per day. From
this data, we can create a more accurate planned independent requirement to
reduce the transportation costs and warehouse expenses. Having this information
is very beneficial and it makes a pushing method seem less risky, but we must take
into account that all of this data is from one game, not multiple games. The data is
subject to have some variances and we will approach the game with the safer
method of pulling, while keeping the push method on the back of our minds.
12. Bank loan
With our planned independent requirement being so high, we will most likely have a
loan to take out within the first round. This will affect our net profit because we will
have to pay the interest rate at the end of the round. So in order to have a low
interest rate, we will pay off some of the loan at a time as long as our bank account
has more than $100,000 on hand.
We decided to have the minimum cash on hand to be one hundred thousand
because want as much money on hand as possible, while still being able to run a PO.
When we convert purchase requisition to purchase order we will pay for those
products with our cash account. So keeping communication between team members
is very important so there isn’t another loan taken out. If someone sends out a PO
the day we pay a loan, there will be much less money on hand. This could lead to
another loan, which we want to avoid. Having the minimum cash on hand before
paying off a loan will ensure that we do not have to take out a loan to keep our
business running. The amount of the loan is up to whoever is in charge of the
transaction. The most important thing is to make sure that when you pay some of
the loan that the cash on hand account stays above $100000.
Credits
A special thank you to the team, Consumers Energy, and Mr. and Mrs. McBride,
professor Tracy, and Dr. Andrea that helped us along the way.
