Uneak White's Personal Brand Exploration Presentation
Enduse Extract Efficiency
1. M. Khairul Bahri
Review Model
Which is the better end use efficiency or extraction efficiency?
by :
Muhamad Khairul Bahri
POSTGRADUATE PROGRAM DEVELOPMENT STUDI ES
SCHOOL OF ARCHI TECTURE, PLANNI NG AND POLI CY DEVELOPMENT
I NSTI TUT TEKNOLOGI BANDUNG
2008
2. M. Khairul Bahri
Phenomenon: Efficiency in Natural Resource Utilization.
Problem : Which is the better end use efficiency or extraction efficiency.
This paper is a further task of Annababette Wills’s End-use or extraction efficiency in natural resource
utilization: which is better ? (System Dynamics Review vol 14, 1998, page 163-188). Wills (1998) stated that
end use efficiency is socially optimal for society need.
Differ from Wills’s work, this paper focus on understanding dynamic interaction between two types of
efficiency. My paper conclude that from starting time, end use efficiency is a very important factor but until
its time, extraction efficiency will need more notice to support society need.
List Variables
Endogenous Exogenous Excluded
Cumulative Use, Cumulative Service Cost Increased Unit (Resource) Government Subsidy on
Demand (Resource) Maximum Subsitution Fraction (dimensionless) technology development
Effort Multiplier (Dimensionless) Unit, unit cost , substitute price (US$/
Actual Cost (US$/ Resource) Resource)
Use Rate, Service Demand Subsititute Teechnology Adjustment, delay
(Resource/year) efficiency technology (year)
Price/Service Unit (US$/ Resource) Efficiency Initial (Technology)
Subsitution Fraction,Potential Initial Market Size (resource/year)
Subsitution Fraction (Dimensionless) Market Growth Rate, percent invested
(dimensionless)
Cost all efficiency type (US$/technology)
Sales Revenue, R-D Investment
(US$/year)
All type Efficiency (Technology)
Profit US$/year
The first modified Model
Cumulative_Use
Annual_demand
sector mining
Profit_Loss
service_demand RD_investment
sales_revenue
mining_margin
ciu
Cumulative_Use
effort_multiplier mining_selling_price_to_service percent_RD
unit_cost operating_cost
actual_cost
sector subsitution
extraction_efficiency
sector service Annual_demandprice_per_service_unit
end_use_efficiency
derivn_actual_cost price_subsitute max_subsitution
price_per_service_unit
market_size
substitution_fraction potential_subsitution_fraction
switch_1
fraction_extraction_cost_dynamic
service_demand subsitution_time
unit substitution_fraction
fract_extraction_cost_fixed substitution_change_rate
cum_service_demand
service
By modified Wills’s work, this model has new variables: Profit_Loss, Mining_Margin, Mining_selling_price
to service, derivn_actual_cost (the first derivative of actual cost variable) and fract_extraction_cost_dynamic.
3. M. Khairul Bahri
In short words, this model work on two basic principles, if profit_loss > 0, then allocate it to RD_investment
and if derivn_actual_cost greater then RD allocation (research and development) for extraction also rise.
This model sums that we need interact dynamically between two efficiency, indeed extraction efficiency
need more allocation while extraction go to very expensive matter to support society need.
40 3
cum_service_demand
2.500 1
1
service_demand
30
2.000 3
3
1.500 2 20 2
1 1
1.000
3
3 10 12
500 12 123
3
01 2 3 12 01 2 3
0 50 100 150 200 0 50 100 150 200
Time Time
6 3 40 3
price_per_service_unit
5
actual_cost
30
4 23
1
3 20 3
2
1
2 23
1 10
11 2 3
3
123 12
0 01 2 3 123
0 50 100 150 200 0 50 100 150 200
Time Time
0,4 3
1
substitution_fraction
Cumulative_Use
0,3 600
23
1
0,2 23 400
1 23
1
0,1 200
3 23
12 1
0,0 1 2 3 123 01 2 3
0 50 100 150 200 0 50 100 150 200
Time Time
Line 1 and 2 allocation of 15%, 20% for extraction DERIVN(actual_cost)
technology of sales revenue (switch_1=0). While GRAPH(derivn_actual_cost,0,0.1,[0,0.08,0.2,0.3,0.4,0.5,
switch_1 set 1, line 3 show that extraction efficiency 0.6,0.65,0.7"Min:0;Max:1.5"])*(1-
has dynamics RD funding – as the right equation. switch_1)+switch_1*fract_extraction_cost_fixed
1,0 1 1,0 1 2 3
extraction_efficiency
2 3
end_use_efficiency
3 0,8 1
0,8 2 3
0,6 1
0,6 1
2 2
3 0,4
0,4 1
23
12 0,2 1
3
0,2 123
1 0,0
0,0 0 50 100 150 200
0 50 100 150 200 Time
Time
The simulation shows that to fulfill society needs, we need dynamics interaction between two efficiency. The
right-above graph reveals that extraction efficiency more important since year 150.
4. M. Khairul Bahri
1 3
5
Cumulative_Use
Annual_demand
600 2
23 1 3 1
1 4 2
3 1
400 123
23 3
1
200 2
23
1 3
01 2 3 11 2
0 50 100 150 200 0 50 100 150 200
Time Time
The second modified Model
The second modified model based on addition of profit loss variable for two sector (mining and service). If
actual cost greater then R&D for extraction cost rise. In service sector, if subsitution greater then we shall
rise R&D for end use efficiency. For comprehensive view, how important combination between two
efficiency, effort equation change to be ((1+(Cumulative_Use/ciu)^8) and simulation time set to 300 years-
simulation.
+
Extraction Effort
+
- Extraction Cost -
Cumulative Use
End Use
+ + Efficiency
Potential Subsitution +
Operating cost
Selling price mining to service + service +
Service
+ - -
Actual Subsitution +
-
+ Sales Revenue RD Investment
Annual Demand Selling price in society Sector Service End Use
Market Size
+ +
+ + - - +
Derivn Actual
Subsitution
Service demand -
+
Actual Subsitution
Extraction Cost + +
+ + Extraction
Sales Revenue RD Investment Efficiency
Operating cost Sector Mining Extraction
+ + mining +
Derivn - +
Extraction Cost
5. M. Khairul Bahri
By looking at causal loop we simply can conclude that RD for extraction efficiency rise while actual cost
more expensively. In line with that, if subsitution rise then service sector tend to set greater RD funding for
end use technology.
End Use Efficiency-end value Extraction Efficiency-end value
Simulation 1 0,0720 0,0787
Simulation 2 0,0342 0,0553
Simulation 3 0,0698 0,0608
Simulation 4 0,0332 0,0430
Simulation 1 : variable percen_RD_extraction dan percen_RD_end-use not increase.
Simulation 2 variable percen_RD_extraction not increase while percen_RD_end-use increased twice;
Simulation 3 : variable percen_RD_extraction set twice and percen_RD_end-use no incresae;
Simulation 4 : variable percen_RD_extraction and percen_RD_end-use set twice together.
Simulation reveals that increasing percen_RD_end-use force setting higher of extraction efficiency. This
simply give us more understanding that society need two efficiency work together dynamically.
Cumulative_Use
Annual_demand
sector mining
Profit_Loss_Mining_Sector
service_demand
sales_revenue RD_extraction_tech
ciu
mining_margin
Cumulative_Use
effort_multiplier selling_price_mining_to_service
unit_cost operating_cost_mining_sector
percent_RD_extraction
actual_cost extraction_efficiency
end_use_efficiency Annual_demand
derivn_actual_cost
percent_RD_extraction sector subsitution
price_per_service_unit
price_subsitute max_subsitution
sector service
operating_cost_sektor_service
selling_price_in_society potential_subsitution_fraction
end_use_efficiency
service_demand
service_margin
subsitution_time
price_per_service_unit substitution_fraction
Profit_Loss_Service
substitution_change_rate
market_size substitution_fraction
selling_price_mining_to_service
service_demand market_growth_rate
sales_revenue_service
unit sector market
cum_service_demand
market_size previos_ms_x_mkt_growth
service percent_RD_end_use
Profit_Loss_Service
previous_market_size
RD_end_use_tech
initial_market_size
substitution_fraction
derivn_SF
