Optimizing Supply Chain Management in Coal Power Generation
Muhamad Iqbal Felani, Ariyana Dwiputra Nugraha and Mujammil Rahmanta
PT PLN (Persero) Research Institute, Duren Tiga Street 102, Jakarta, Indonesia
Keywords: Supply Chain Management, Optimization, Simulation, Coal Power Generation.
Abstract: Indonesian government launched Fast Track Program Phase-1 in 2009 to increase national electricity ratio
by installing 35 coal power generations with total capacity 10,000 Mega Watt. However only 25 coal power
generations had been installed by now, spread all over Indonesia. Coal necessities were supplied by 14
domestic coal mining companies. There are two factors that affect the price of coal i.e : distance and unit
price. Distance between supplier and coal power generation would determine the transportation cost while
unit price would determine the price of procurement. The aim of this research is to minimize total price of
coal by optimizing the distance and unit price (USD/Ton), allocating the coal necessities and scheduling the
delivery. The optimization would be simulated using software What’sBest. By this simulation, 24 power
plants were suggested to change their existing suppliers, while only one power plant was fitted. This change
could reduce USD 27 Million/year for total price of coal.
1 INTRODUCTION
One of the important factor in increasing the
national economic development is the availability of
low cost energy (Wang, Feng and Tverberg, 2013).
Therefore every country tries to minimize the fuel
cost in electricity supply. As a development country,
Indonesia is in progress to install coal fire power
plants with lower cost. The coal power plants spread
all over the country, while coal suppliers are
concentrated in particular island. The price of coal
consists of two factors: transportation cost
(USD/nautical mile) and unit price (USD/ton). So
the transportation factor will dominate the cost of
coal procurement since Indonesia is an archipelago
country (PT PLN (Persero) Coal Procurement
Division, 2013).
Figure 1: Map of Coal Power Plant and Coal Supplier
Spread in Indonesia.
Every power plant requires a specific coal that
must be supplied by appropriate supplier. In
consequence of this problem, the utility should
arrange the best supply chain management to
minimize the cost. In case utility could minimize
the distance between power plant and appropriate
supplier, the cost would be minimal.
There are 25 power plants with specific coal
requirement and 14 coal supplier that would be
optimized in this paper. The purpose of this paper is
to optimize supply chain management by
minimizing the distance between power plant and
appropriate supplier and ensure that production
capacity of supplier could serve the power plant as
long as contract duration. Software What’sBest is
used to simulate this problem. The results of the
simulation are the demand – supply mapping,
allocating the coal requirement and scheduling of
shipping.
2 METHOD
This paper simulated the coal supply chain
management planning according to the real
condition. The simulation would be processed by
What’sBest software. The first step in this research
was data collection. Data used in this simulation;
Information about coal power plant as a demand,
involving the name of Power Plant, the location,
460
Felani M., Nugraha A. and Rahmanta M.
Optimizing Supply Chain Management in Coal Power Generation.
DOI: 10.5220/0006250904600463
In Proceedings of the 6th International Conference on Operations Research and Enterprise Systems (ICORES 2017), pages 460-463
ISBN: 978-989-758-218-9
Copyright
c
2017 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
the quantity of coal demand, the quality
requirement of power plant, safety stock level,
and jetty capacity.
Information about coal supplier, involving the
name of supplier, the quality of coal, unit price
(USD/ton), transportation cost (USD/Nautical
miles), productivity (ton/year), contract duration
and ship capacity.
Information about ocean condition in Indonesia
yearly, involving the height of wave, the velocity
and direction of wind (Fig.2).
Distance and lead time between supplier and
demand. The distance is determined by
Geographic Information System (GIS)
Directorate General of Marine Transportation,
The Indonesian Ministry of Transportation.
Therefore the distance is not straight line, but
following the cruise lane (fig.3).
Figure 2: Wind and Wave Ocean Condition in Indonesian.
Based on Fig.2 above, the information is
converted in binary number. Number 1 means a high
wave that affect the ship velocity, and number 0
means normal condition that not affect the ship
velocity.
Figure 3: Wind and Wave Ocean Condition in Indonesian.
The velocity of ship in Fig.2 and the distance
between supplier and demand in Fig. 3 results in
determining lead time.
All of data above were processed into a database.
The next steps were allocating the coal necessity and
scheduling. The steps in simulation process could be
seen in Fig.4 as follow:
Figure 4: Steps of Simulation Process.
Some decisive variables must be considered
when allocating the coal demand. These variables
are presented by mathematical equation;
Supplier i only could send coal q to the power
plant j as if coal q has the same specification
with power plant requirement. In other word, the
caloric value of the coal from supplier must be
appropriate with caloric value required by power
plant. This decisive variable is capable or not,
noted by Y
i,j
.
Coal q ϵ Q was supplied by supplier i ϵ I to
power plant j ϵ J. This decisive variable is the
amount of each type of coal which is delivered
by supplier, noted by X
i,j,q
.
The purpose of this allocation is to minimize total
cost (f) of coal. There were 2 main components
contributing in total cost; procurement cost (f
1
) and
transportation cost (f
2
). Therefore this function can
be determined as follows:
Minimize f = f1 + f2 (1)
Procurement cost is multiplication between unit
price (P
i,q
)of coal q to supplier i with total amount
(X
i,j,q
) of coal q that is delivered by supplier i to
power plant j. Therefore the formula of procurement
cost (f
1
) can be determined by:
f
1
=
,,ijq
iI jJ qJ
X


,iq
P
(2)
While transportation cost is multiplication between
transportation cost (TC
i,j
) from supplier i to power
plant j with total amount (Xi,j,q) of coal q that is
delivered by supplier i to power plant j with distance
(R
ij
) from supplier i to power plant j. Therefore the
formula of transportation cost (f
2
) can be determined
by:
F
2
=
,,ijq
qQ iI jJ
X


,ij
TC
,ij
R
(3)
To achieve the optimal function above, then
allocation model must comply with these limitation:
1. Maximum amount (X
i,j,q
) of coal q that could be
procured by power plant j is limited by
Optimizing Supply Chain Management in Coal Power Generation
461
maximum capacity (O
i,q
) of coal q which is
available in supplier i comply with contract
document between supplier and utility, this
limitation could be formulated as:
,, ,ijq iq
jJ
XO
i ϵ I, j ϵ J, q ϵ Q
(4)
2. Every power plant has its typical caloric value
of coal. Delivery of coal could be processed as
if caloric value of coal in supplier comply with
power plant‘s caloric value. The coal which has
caloric value beyond the range could be
processed. This limitation could be formulated
as:
(5)
3. The coal q from supplier must be able to fulfil
the demand (D
j
) every power plant
j
. This
limitation could be formulated as:
,, ,ijq jq
iI
XD
∀j ϵ J, q ϵ Q
(6)
The final step of the simulation is scheduling,
which time of delivery of coal would be determined.
The limitation in scheduling is that power plant j
only could receive of coal from supplier i once a day
(t). This limitation can be formulated as RC
i,j,q,t
3 RESULT
The simulation gave a result that from total 25
existing power plant, there are 24 power plants
should change their supplier due to cost optimizing.
This simulation also gave the optimal amount of
coal that should be procured by power plant, it calls
optimal allocation. Therefore all of power plants
could make a coal procurement plan effectively. The
next step is scheduling. All of the limitations of
allocating and scheduling are conducted by
What’sBest software. The scheduling covers all
information about when supplier must deliver their
coal to power plant, how much coal that must be
delivered to the power plant, when the coal would be
received by power plant considering the ocean
condition and how much coal that available in power
plant inventory as consequence of lead time
variance. The example of scheduling table could be
seen in Table 1.
From Table 1 above could be seen that power
plant “A” would be supplied by supplier “1” as
much as 7,500 ton in September 24
th
(purple cell)
and would be received by power plant “A” in
September 29
th
(yellow cell). Safety stock level in
power plant “A” in September 24
th
is 54,080 ton
(orange cell). This safety stock level would be
maintained in 25 operating days. Lead time is
presented as green and blue cell. Green cell is for
normal condition (weather) while the blue one is for
bad condition (weather).
4 DISCUSSION
Total cost (procurement cost and transportation cost)
before simulation and after simulation was
compared to evaluate it significance. Transportation
cost before simulation was not available due to poor
of information. There was no data about the amount
of coal that is delivered by supplier to power plant.
While total cost after simulation is determined by
model. The comparison before and after simulation
could be seen in Table 2.
The Table 2 above informed that after simulation
procurement cost decreased as much as
24,110,173.53 USD per year. In case it is assumed
that amount of coal that is delivered by supplier to
power plant before simulation is equal to after
simulation, then it definitely results that
transportation cost after simulation is less than
before simulation. In other word we could say that
there is a benefit after simulation.
Table 1: The Example of Scheduling.
Power
Plant
Supplier
Total of
coal
Ship
capacity
Initial
Capacity
September
Normal* Bad* 24 25 26 27 28 29 30
A
Level Inventory 51,750 54,080 51,990 57,420 55,350 57,420 58,710 56,640
1 755,550 7,500 Received 0 0 7500 0 0 7500 0
Lead Time 5 6 Order 7500 7500
B
Level Inventory 47,040 48,652 47,771 50,889 49,008 50,889 50,244 48,363
2 552,151 5,000 Received 5,000 0 0 5,000 0
Lead Time 16 24 Order 5,000 5,000
3 134,632 7,500 Received 0 0 0 0 0
Lead Time 2 2 Order
ICORES 2017 - 6th International Conference on Operations Research and Enterprise Systems
462
Table 2: The Comparison of Total Cost.
Cost Component
Before Simulation
(USD)
After Simulation
(USD)
Benefit
(USD)
Procurement Cost 897,183,961 873,073,787.5
24,110,173.5
Transportation Cost Not Available 363,454,502.5
Not Available
Total Cost 897,183,961 + NA 1,236,528,290
Last year, Indonesian government launched
instalment of power generation program with total
capacity 35,000 Mega Watt (MW). This program
will be completed in 2019. Majority of them are coal
fired power plant. The most critical problem in coal
power generation is the reliability of coal supply. It
will need a huge demand of coal in 2019. Therefore
local coal suppliers need to be mapped. Then
scheduling each power generation will be arranged
and optimized. The method in this research is being
used to solve the problem.
5 CONCLUSIONS
The research proved that the model simulation could
optimize total cost of coal demand in power plant.
There are 24 of 25 existing power plant that should
change their allocating and scheduling planning to
increase cost effective. By optimizing supply chain
management it could reduce total cost at least
24,110,173.53 USD per year. The method in this
research is being used to ensure the reliability of
coal supply to support government’s program;
Instalment of 35,000 MW power generation.
ACKNOWLEDGEMENTS
The author thanks the PT PLN (Persero) Research
Institute for the chance and support. The author also
thanks to PT PLN (Persero) Head Quarter Division
of Coal Procurement for valuable data and direction.
Thanks to Indonesian Coal Fire Power Plant for
group discussion, data and valuable supports during
this research.
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