Business Modeling 101 finals

1. 1
Final Presentation
Business Model : Capstone Project For
Digital Start-Up
reen unlight

2. Business Model Canvas
Problem Articulation
Data Collection
& Analysis
Formulation of
Dynamic Hypothesis
2
Formulation of a
Simulation Model
Testing
Policy Design &
Evaluation
Contents
1
2
3
4
5
6
7

3. Business Model Canvas
3
Key Partners
1. Government
2. Companies and
businesses with tall
buildings, or other
immobile structures
that receive a lot of
sunlight
3. Providers of solar
panels
4. Local communities
Key Activities
1. Installing/selling electri
city generated by panel
s to central grid, which i
s owned by the govern
ment-run power utility
company.
2. Maintaining a digital/pl
atform for keeping trac
k of power sold
3. Collecting data on how
much electricity was ge
nerated and sold at whi
ch price
Value Propositions
1. Utilize city wide spaces
to generate surplus
electricity
2. Sell the surplus energy
to the smart power
grid, and then share the
profits with those who
have lent us the spaces
3. For most participants
like a part-time job with
revenue
4. Environmentally
friendly tech
Customer Relationshi
ps
1. On the selling electricity
side, since there is only
one main customer, the
main focus is on
maintaining a healthy,
yet neutral relationship
with the government
body
2. Lenders of spaces to
install the panels get
data on energy prices
and part of profits
Customer Segments
1. Government-run power
utility company
2. Individual who agree to
install the panel and sha
re the profits.
Channels
1. Government – only one
channel
2. Users of grid such as ind
ividual, companies and l
ocal community (Throu
gh digital platform)
Key Resources
1. Solar panels – installatio
n
2. Digital platform
Cost Structure
1. PV Panels (lease or purchase from manufacturer)
2. Low skilled technicians for installation
3. Digital app / platform
4. Marketing – Promotion
Revenue Streams
1. Leasing fee from customer (individual)
2. Electricity sales from government

4. Problem Articulation
4
How is the profit viability of our proposed new business, Green
Sunlight, affected by various factors in terms of revenue and costs?
< Theme
Selection >
< Key Variables >
• Number of Users
• The number of people using Green Sunlight’s services and thus installing the solar panels at their homes
• REC Value & SMP Value
• The market value of electricity sold to governmental power and to contractual power suppliers using traditional
forms of electricity generation
• Monthly Leasing Fee
• Amount charged to customer for usage of Green Sunlight’s panels and independent variable under the firm’s
control in entirety
• Revenue Shared with Customer
• Percentage of incoming revenue that results from sale of electricity that is to be shared with the customers,
perceived as an independent variable

5. Problem Articulation
5
< Time Horizon >
48 Months
100 – 120 months
Rough
Calculation
s:
 Estimated revenue (per
customer):
REC sales + Reduction of
electric bill (SMP)
3000/month+1800/month
 Estimated cost (per customer):
Price of REC + Installation fee
+ Maintenance
100,000+20,000+2000/month
Estimated period of
return: About 4 years
Basis of
decision:How far back in the past lie the roots of the problem?
• For this problem, there is no need to go
back because the goal of the model is to
show viability of future business.
How far in the future should we consider?
• The model will look at two cycles of lease-
repayment of solar panels to customers.
Reasons for Adjustment in Time
Horizon:1. Initial calculations did not take into account the time it would
take for the potential customer pool and the number of
customers to build up to significant numbers.
2. The importance of upfront revenue from panel lease was
smaller than initially thought.

6. 6
Midterm Review
Data Collection & Analysis

7. • Environmental concerns
Data Collection & Analysis
7
• Interested in making profits rather than saving
electricity bill
Revenue to customer
Ta r g e ting
Demographics
Awareness
Revenue to customer
S e g m e n t
• Middle-Old aged
• Male
• Lower-Middle class income
Demographics
Awareness
Division of customer pool
Into Potential customer &
Real number of customer

8. Data Collection & Analysis
8
Demographics
4,163+4,148+4,725 / 43863
= 0.297 (About 30% of population)
Conditional factors
Income level
200~300만 미만: 16.08%
300~400만 미만: 13.87%
400~500만 미만: 10.72%
500~600만 미만: 8.8%
600~700만 미만: 5.87%
700만원 이상: 12.46%
전체 인구 대비 누적: 67.8%

9. Data Collection & Analysis
9
0-30구간: Y=1/30*x
30-50구간: Y=1/20*x
50-70구간: Y=1/20*x
70-90구간: Y=3/20*x
90-110구간: Y=1/5*x
110이상구간: Y=3/20*x

10. Data Collection & Analysis
10
Result of Conversion rate (Conditional factor)
𝐶𝑜𝑛𝑣𝑒𝑟𝑠𝑖𝑜𝑛 𝑟𝑎𝑡𝑒 = 𝑃𝑜𝑡𝑒𝑛𝑡𝑖𝑎𝑙 𝐶𝑢𝑠𝑡𝑜𝑚𝑒𝑟 𝑃𝑜𝑜𝑙 ∗ 67.8%, 𝐷𝑒𝑚𝑜𝑔𝑟𝑎𝑝ℎ𝑖𝑐𝑠 ∗
(30%, 𝐼𝑛𝑐𝑜𝑚𝑒 𝑙𝑒𝑣𝑒𝑙) ∗ 𝑟𝑒𝑣𝑒𝑛𝑢𝑒 𝑡𝑜 𝑐𝑢𝑠𝑡𝑜𝑚𝑒𝑟 ∗ Function
Awareness
• Background knowledge of solar
energy & Environmental Concern
Attitude factors

11. 11
Newly included factor
Data Collection & Analysis

12. Y(Awareness)=-6.078+1.279*(WOM1)+0.533*(WOM3)+0.449*(Environmental Concern)
Regression AnalysisData Collection & Analysis

13. Formulation of Dynamic Hypothesis
13
< Initial Hypothesis Generation >
1. There is a positive relationship between number of customers and revenue from REC
sales.
2. As the revenue shared to customers goes up, the overall number of customer also goes
up.
3. The overall cost of solar panels increase as the number of customers go up, but at a
much slower rate due to economics of scale.
4. As an end goal, the increase in our profit and increase in number of customers will lead
to increased value creation for society, and in addition will lead to positive feedback
from society in general to our business.
5. As total electricity produced by our customers increases, the REC weight will go
through a period of decline and then a period of stabilization.

14. Formulation of Dynamic Hypothesis
14
< Subsystem Diagram >

15. Formulation of Dynamic Hypothesis
15
Endogenous Exogenous Excluded
• Revenue to Customer
• Word-of-mouth
• Price of Each PV
• Employee Cost
• Monthly Leasing Fee per
Panel
• Number of Customers
• Potential Customer Pool
• Panel Stock
• Number of PV Installed
• Number of Employees
• Money Balance
• Environmental Concern
• Demographics
• Income Level
• Movement Rate
• Average Size of
Contract
• Panel Disposal Rate
• Maintenance Cost
• REC Value & Weight
• SMP Value
• Tax Rate
• Advertisement Fee
• Oil-Coal Price
• Governmental Subsidies
• Unemployment Rate
• Technological Changes
< Model Boundary >

16. Formulation of a Simulation Model
16
< Causal Loop Diagram #2 >
• Better organized by dividing into
Cost and Sales parts
• Still, unorganized Model Boundary
< Causal Loop Diagram #1 >
• Too complex and unorganized Model
Boundary
• Cannot find out overall operation of
the business

17. Formulation of a Simulation Model
17
Customer
Part
Panel Part
Money Part
< Closed Loop Diagram
#3 >
• Much more organized
Model Boundary
• Divide into Revenue, Cost
parts and into Panel,
Customer, Money parts.
• Accept easily overall
operation of the business

18. Formulation of a Simulation Model
18
< Stock and Flow Diagram #1 >
• Three Stocks : need more explanation
about the relationship between stocks
• Too simple so that cannot explain the
exact flows

19. Formulation of a Simulation Model
19
< Divided Stock and Flow Diagram >
• In order to explain the flows in more
detail, we divided into 3 SFDs
Customer Part
Money Part
Panel Part

20. Formulation of a Simulation Model
20
< Stock and Flow Diagram #2>
• Combined the divided SFDs into
one
• Not specific for VENSIM
simulation
• Additional Surveys were not
reflected

21. Formulation of a Simulation Model
21
< Stock and Flow Diagram
#3>
• Specified for VENSIM
simulation

22. Formulation of a Simulation Model
22
< Functions >
- Customer Part -
• 𝐴𝑤𝑎𝑟𝑒𝑛𝑒𝑠𝑠 𝑟𝑎𝑡𝑒 = 0.449 ∗ 𝐸𝑛𝑣𝑖𝑟𝑜𝑛𝑚𝑒𝑛𝑡𝑎𝑙 𝑐𝑜𝑛𝑐𝑒𝑟𝑛 + 1.812 ∗ 𝑊𝑜𝑟𝑑 − 𝑜𝑓 − 𝑀𝑜𝑢𝑡ℎ
• 𝐶𝑜𝑛𝑣𝑒𝑟𝑠𝑖𝑜𝑛 𝑅𝑎𝑡𝑒 = 𝑃𝑜𝑡𝑒𝑛𝑡𝑖𝑎𝑙 𝐶𝑢𝑠𝑡𝑜𝑚𝑒𝑟 𝑃𝑜𝑜𝑙 ∗ 𝐷𝑒𝑚𝑜𝑔𝑟𝑎𝑝ℎ𝑖𝑐𝑠 ∗ 𝐼𝑛𝑐𝑜𝑚𝑒 𝐿𝑒𝑣𝑒𝑙 ∗ 𝑅𝑒𝑣𝑒𝑛𝑢𝑒 𝑡𝑜 𝐶𝑢𝑠𝑡𝑜𝑚𝑒𝑟
• 𝑃𝑎𝑛𝑒𝑙 𝑂𝑟𝑑𝑒𝑟 𝑅𝑎𝑡𝑒 = 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝐶𝑢𝑠𝑡𝑜𝑚𝑒𝑟 ∗ 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑠𝑖𝑧𝑒 𝑜𝑓 𝑐𝑜𝑛𝑡𝑟𝑎𝑐𝑡 𝑝𝑒𝑟 𝑐𝑢𝑠𝑡𝑜𝑚𝑒𝑟
- Revenue Part -
• 𝑅𝑒𝑣𝑒𝑛𝑢𝑒 𝑓𝑟𝑜𝑚 𝐸𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦 = 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑃𝑉 𝐼𝑛𝑠𝑡𝑎𝑙𝑙𝑒𝑑 ∗ 𝐸𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦 𝑝𝑟𝑜𝑑𝑢𝑐𝑒𝑑 𝑝𝑒𝑟 𝑝𝑎𝑛𝑒𝑙 ∗ 𝐸𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦 𝑝𝑟𝑖𝑐𝑒
• 𝐸𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦 𝑃𝑟𝑖𝑐𝑒 = 𝑅𝐸𝐶 𝑉𝑎𝑙𝑢𝑒 ∗ 𝑅𝐸𝐶 𝑊𝑒𝑖𝑔ℎ𝑡 + 𝑆𝑀𝑃 𝑉𝑎𝑙𝑢𝑒
• 𝐿𝑒𝑎𝑠𝑖𝑛𝑔 𝐹𝑒𝑒 = 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑃𝑉 𝐼𝑛𝑠𝑡𝑎𝑙𝑙𝑒𝑑 ∗ 𝑀𝑜𝑛𝑡ℎ𝑙𝑦 𝐿𝑒𝑎𝑠𝑖𝑛𝑔 𝐹𝑒𝑒 𝑓𝑜𝑟 𝑒𝑎𝑐ℎ 𝑝𝑎𝑛𝑒𝑙
- Cost Part -
• 𝑁𝑒𝑤 𝑃𝑎𝑛𝑒𝑙 𝐶𝑜𝑠𝑡 = 𝑃𝑎𝑛𝑒𝑙 𝑂𝑟𝑑𝑒𝑟 𝑅𝑎𝑡𝑒 ∗ 𝑃𝑟𝑖𝑐𝑒 𝑜𝑓 𝑒𝑎𝑐ℎ 𝑝𝑎𝑛𝑒𝑙
• 𝑃𝑟𝑖𝑐𝑒 𝑜𝑓 𝑒𝑎𝑐ℎ 𝑝𝑎𝑛𝑒𝑙 = 200000 − log10 𝑃𝑎𝑛𝑒𝑙 𝑂𝑟𝑑𝑒𝑟 𝑅𝑎𝑡𝑒
• 𝑇𝑎𝑥 =
0, 𝑖𝑓 𝑀𝑜𝑛𝑒𝑦 𝐵𝑎𝑙𝑎𝑛𝑐𝑒 < 0
0.2 ∗ 𝑀𝑜𝑛𝑒𝑦 𝐵𝑎𝑙𝑎𝑛𝑐𝑒, 𝑖𝑓 𝑀𝑜𝑛𝑒𝑦 𝐵𝑎𝑙𝑎𝑛𝑐𝑒 < 30𝑀
0.4 ∗ 𝑀𝑜𝑛𝑒𝑦 𝐵𝑎𝑙𝑎𝑛𝑐𝑒, 𝑖𝑓 𝑀𝑜𝑛𝑒𝑦 𝐵𝑎𝑙𝑎𝑛𝑐𝑒 > 30𝑀
• 𝐸𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡 𝑟𝑎𝑡𝑒 =
0, 𝑖𝑓 𝑀𝑜𝑛𝑒𝑦 𝐵𝑎𝑙𝑎𝑛𝑐𝑒 < 0
𝑃𝑎𝑛𝑒𝑙 𝐼𝑛𝑠𝑡𝑎𝑙𝑙𝑎𝑡𝑖𝑜𝑛 𝑅𝑎𝑡𝑒
30
, 𝑖𝑓 𝑀𝑜𝑛𝑒𝑦 𝐵𝑎𝑛𝑎𝑐𝑒 > 0

23. Testing
23
< Simulation Result with Initial Guess >

24. Testing
24
< Cost
Analysis >
• Main factor of cost was revenue
shared and panel cost
• Panel cost cannot be changed by us,
so we decided to short the
percentage of revenue shared

25. Policy Design & Evaluation
25
< Result with changing the percentage to
0.575 >
• Number of customer decreased, but Money
balance showed more positive tendency
• Still, money balance is not stable

26. Policy Design & Evaluation
26
< Cost Analysis >
• Cost is mainly dependent on revenue
shared, panel cost, and employee cost.
• Panel cost is unchangeable. Overall
graph is determined by the revenue
shared, but it shows naturally
increasing as the increase of number
of customer.
• Shape of cost graph is dependent of
employee cost.
• Initial employee policy : one person
per 30 panel installation

27. Policy Design & Evaluation
27
< Result with new employee policy > : Employment Rate = log10(𝑃𝑎𝑛𝑒𝑙 𝐼𝑛𝑠𝑡𝑎𝑙𝑙𝑎𝑡𝑖𝑜𝑛 𝑅𝑎𝑡𝑒)

28. Policy Design & Evaluation
28
• Dependency on employee cost
decreased
• Cost graph shows natural increase

29. Policy Design & Evaluation
29
< Analysis From VENSIM Model >
1. Most part of revenue comes from the revenue
from electricity.
2. Leasing fee does not almost have impact on the
revenue.
Money
Balance
Cost
Revenue
1. Panel cost and revenue shared compose most
of cost.
2. Panel cost is unchangeable by our will.
3. Revenue shared is related with the percentage
of revenue shared to the customer.
4. If money balance is oscillating, it means that
employee cost is affecting a lot.
5. Affection of tax is relatively weak.

30. Policy Design & Evaluation
30
• First thing to check is whether the percentage of revenue to customer is
adequate.
• If the percentage is adequate, we need to check out the factors affecting to
cost.
• Overall graph is determined by Panel cost and revenue shared. However,
what’s important is the inflection points of cost graph.
• Inflection points are determined by Employee cost.
< Maximizing our profit in VENSIM Model >

31. Policy Design & Evaluation
31
< Hypothesis
Verification >
1. There is a positive relationship between number of customers
and revenue from REC sales.
2. As the revenue shared to customers goes up, the overall number
of customer also goes up.
3. The overall cost of solar panels increase as the number of
customers go up, but at a much slower rate due to economics of
scale.
4. As an end goal, the increase in our profit and increase in number
of customers will lead to increased value creation for society, and
in addition will lead to positive feedback from society in general
to our business.
5. As total electricity produced by our customers increases, the REC
weight will go through a period of decline and then a period of
stabilization.
Partially True
True
True
Cannot Prove with
Current Model
Excluded in Final
Version of Model

32. 32
Q&A

33. Thank you