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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.
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%
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.
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
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)
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
So this is the review of the work that we have done in the mid term..
(Click)As for the review, Our group conducted the first survey and we were able to define what causes customer to join our business, and these are the variables found in the survey.
First of all, (Click)We were able to define the demographics of potential participants are middle-old aged male and their income level should be above lower middle class.
(click) One of critical finding in the mid term survey was that among other factors, people are more driven by the profit.
There we concluded that in order to promote our business to the people in general and the people who doesn’t care about the solar panel at all, we should focus more on how much customer can earn from joining our project.
We were able to define the demographics of respondents are middle-old aged male and in scale, they are the people over age of 40s
And as you can see the percentage of them in total population is 30% out of total population.
As for the income level, we already found that the participants who might be our potential customer has to have the income level above $2000 a month.
And that as our minimum standard, we calculated the total population’s income level (Click)
And people who earn over $2000 per month is 67.8% of the population.
And for the profit driven factor, we figured that the profit is the driving factor among all variables and that scale is from $30~$50 a month but people are more willing to participate when they can earn over $70 and it can be shown in graph how steep the slope gets after $70 (Click)
And these are the equation that will go into the conversation rate.
(Click) Then how about the awareness rate which defines the number of potential customer pool?
(Click)The reason to bring up the awareness rate as separate rate as conversion rate is because after midterm, we figured that dividing our customer pool into two groups which are potential customer pool and number of customer are more logical to do it because the variables that factor the awareness is the inducing factor which basically asks their attitude about the solar panel not conditional factors.
This is the first Stock and Flow Diagram of our model. Initially, we thought that we need 3 stocks to express our model. So we made the SFD by using 3 stocks. However, it was not sufficiently detail so that it cannot explain the relationship between stocks or exact flows.
So we divided the SFD into 3 parts and find out each stock SFD in more detail.
Then we combined the 3 divided SFD into one and we can get the updated SFD. This one shows exact relationship between stocks or flows. However, still it does not contain the new survey result and it is not specific for VENSIM simulation.
While inserting the function in VENSIM, we can get the final version of SFD. This final version is specified for VENSIM simulation and also contains the functions that are needed to represent the relationship and flows.
The functions inserted in VENSIM are like these. We revised the awareness rate from the second survey we did. And we set price of each panel as two hundred thousand – log 10 of panel order rate so that for panel order rate larger than specific point, the price of each panel does not almost change. Also, we set employment rate as panel installation rate divided by 30 panels. However if money balance is smaller than 0, employment rate is 0.
With this model, we did simulation and could get these results. Initial guess was that the percentage of revenue shared to customer was 90% and we can get most of our profit from leasing fee. However, as we can see from the graphs, our money balance decreased continuously. Though the number of customer was very large, the total cost was much larger than the revenue. So we decided to analyze the cost.
The largest factors of cost were revenue shared to customer and panel cost. Incease of panel cost seems natural one as the number of customer increase and it cannot be changed by our decision. So we thought thst the reason why our money balance is negative comes from the percentage of revenue shared.
So we decided to decrease the percentage to 57.5% and also decrease the leasing fee for each panel. Then we find out that the number of customer and number of PV installed decreased but the money balance showed more positive tendency. However, money balance was still oscillating and unstable. We reanalyzed the cost.
Cost was mainly composed of revenue shared, panel cost, and employee cost. This time, panel cost and revenue shared are both natural things and overall graph is determined by these two variables. However, what's important in the cost graph is inflection points for this time. Inflection points are determined by employee cost. So we thought that we need to change the employee policy for this time. Our initial policy was to hire one employee for installation of 30 panels.
Then we changed the policy to hire employees as much as log 10 of installation rate of panel. Then break-end point of our model was 48 months and increased continuously.
When we see the cost, it smoothly increases unlike the previous one. It means that the dependency on employee cost decreased a lot. So by minimizing the cost, we can improve our business.
What we have analyzed from VENSIM model is like these. Revenue inflows and cost outflows and their difference becomes money balance. Most part of revenue comes from the revenue from electricity. On the other hand, leasing fee does not almost have no impact on the revenue. For cost part, panel cost and revenue shared compose most of it. However, panel cost cannot be changed by our will. Revenue shared is related with the percentage of revenue shared to the customer. There are two factors consisting the cost. Tax affects relatively weak to the cost. Employee cost sometimes affects a lot to the cost, and it is exposed as the oscillation of money balance.
From the analysis from VENSIM model, we can determine the procedure to maximize our profit. First we need to check out whether the cost graph is much higher than the revenue graph. If it is, it means that the percentage of revenue to customer is inadequate. If the cost graph is almost similar to revenue graph, but the money balance is oscillating, it means that there is a problem of employee cost. In the cost graph, it is represented as inflection points. So we need to revise the employee policy.
So we verified our initial hypothesis. First one is partially true because number of customer increases then the revenue itself increases but it does not mean that our money balance increases. Second one and third one are proved to be true. As the revenue shared to the customer goes up, then the overall number of the customer increased. Also, when we ordered more panels, the total panel cost increases naturally, but the cost of each panel decreased to some specific points. Fourth hypothesis cannot be verified in current model, and we did not inflect the change of REC weight in our model.