New John Deere - Cloudinaryres.cloudinary.com/general-assembly-profiles/image/... · 2017. 8....
Transcript of New John Deere - Cloudinaryres.cloudinary.com/general-assembly-profiles/image/... · 2017. 8....
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John Deere Financing and Operation
Optimization Strategy by
Rameez Rosul
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Introduction
Hi I am Rameez , I am an Analyst with John Deere .We make agricultural machinery. My manager John is worried lately
It turns out that that he has been given a task to find a road map for underwriting strategies and reducing operation costs .
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John Deere's revenue by region 2008-2016
John Deere's (Deere & Company's) revenue from 2008 to 2016, by region
(in million U.S. dollars)
Source: Deere & Company ID 271864
Note: United States; 2008 to 2016
17,065
14,823
16,611
19,214
22,737
23,852
22,391
18,750
16,742
11,008
7,961
9,036
12,41512,999
13,495 13,147
9,616 9,339
0
5000
10000
15000
20000
25000
30000
2008 2009 2010 2011 2012 2013 2014 2015 2016
Revenue in
mill
ion U
.S. dolla
rs
U.S. and Canada Outside U.S. and Canada
Further information regarding this statistic can be found on page 8.
http://www.statista.com/statistics/271864/revenues-of-john-deere-by-region-since-2008/
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John shares
some insight :
Low prices for major
crops
Over production of
crops
Preference for
Leasing of machinery
Farmers refuse loans
and thus sales suffer
Used vehicle market
increase
Possible
reasons for
decline
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Problem statement
Which region will be most profitable for the underwriters to provide financial options ?
Solution
Using past data of crop value, we will find crops that are most probable to be of high value .
The crop prices and harvest size will determine value of crops
Following up with its regions they are grown in .
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John “how are you defining High/low
value “
BY Creating two engines in our model
Weather Engine :BY using the weather data to decide the value of harvest next year. We are taking 50/50 probability for this model.
Value Engine :This contains the data of the prices ,harvest size and other market forces to determine the value .
We use the max ,min and average values for determining the value .
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Assumptions in the model
Our Model is taking overall country averages to
distinguish our target crops .
The weather engine has been kept at 50 %
good
It is assumed that good weather will give good
crops
Using past data for analysis is a good indicator
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Rameez to John
Rameez ”John, As our initial analysis
clearly shows that field crops are doing
well , we should proceed further analyzing
field crops .
John” Yes you are right . Our majority
products also deal with field crops “
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Simulations results using 1000 Trails
Adding uncertainty with respect to weather and
market forces
model gave us
• Wheat
• Rice
• Hay
• Cotton
• Soybean
• Corn
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Probability of corn 51% being high value
49% being low value crop
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Probability of Hay 45% being high value
55% being low value crop
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Probability of Rice 05% being high value
95% being low value crop
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Probability of Wheat 40% being high value
60% being low value crop
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Probability of Soybean 51% being high value
49% being low value crop
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Probability of Cotton 11% being high value
89% being low value crop
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John asks:
“Now that we have found the crops to concentrate on ,
Which regions do you suggest for the crops in question
?.”
Solution :”By finding the expenditure , the area under
harvest and the history of growing patterns for the
crops ,we come up with the common states”
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Rameez to John
Finding probable high value crops
and regions with favorable aspects we
can make our operation lean in terms
of inventory and assets
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States with large cultivated area
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States with most expenditure
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Common states for wheat
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Common states for Hay
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Common States for Rice
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Common States for Soybean
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Common States for Corn
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Common States for Cotton
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John to Rameez
“Great Job Rameez ,you truly are a asset to the team and
the company ,where will we be without you .you are God
send”
Rameez replies”But wait John! there is more .What if you
wanted to further rank the states for crops like Wheat.
John”You read my mind .do tell”
Rameez ”we ranked the states further on bases of
1)Forecast Machinery expenditure growth%
2)Forecast area under cultivation growth%
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Recomndation
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John Summarizing
So Rameez you suggest that we should lend
to farmers next year who plan to grow
Corn
Hay
Soybean
Wheat
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John Summarizing :Ranking for States
Corn: Michigan, Indiana, Missouri, Wisconsin,
Ohio, Illinois, Iowa
Hay: Michigan ,Missouri, Wisconsin, Ohio, Illinois,
Iowa
Wheat: Michigan, Indiana, Missouri ,Ohio, Illinois
Soybean: Michigan, Indiana, Missouri, Wisconsin,
Ohio, Illinois, Iowa
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Ok John I am leaving for the day .
Thank you Does any one other than john have any question?