Post on 09-Mar-2018
1
Demand Forecasting in Downstream Supply Chain Telco Product
Ratih Hendayani
1 and Adrian Darmanda
2
1,2
Telkom University
ratihhendayani@telkomuniversity.ac.id
adriandarmanda@ymail.com
Abstract – This study aims is to manage the uncertainty demand in the
downstream supply chain for a starter pack Telco product by measuring demand
forecasting in one area of West Sumatra at PT. Pioneering Citra Pratama and its
outlets as a distributor of PT. Indosat, one of the biggest operational cellular in
Indonesia. With demand forecasting, the operator cellular can avoid a big gap
between orders and demand and lower their lost opportunity. This research is a
descriptive study and data collection techniques using literature studies that
required starter pack product orders and demand in 2012. After getting the data,
calculate the gap outlet which has the highest demand management and
forecasting what the appropriate method for forecasting demand for products
starter pack PT. Pioneering Citra Pratama and outlet. Calculation forecasting
using WinQSB statistical software. The results obtained the outlet that is the value
of the difference between demand and bookings greater than another outlet is a
BM outlet with 90 gaps. The appropriate method of forecasting demand for the
starter pack products in PT Indosat Pioneers Citra Pratama and its outlets are
used a forecasting method models Simple Average (SA) for outlets BM Cell, outlet
D&R Cell, outlet Megajaya Cell, outlet Moranza Cell, outlet Home Poncell and
Double Exponential Smoothing (DES) for the outlet Line, outlet Minang Cell, and
outlet Aito Mobile. The results of this study are an alternative solution therefore
further research should analyze the implications of the solution when it has been
implemented.
Key Words – Demand Uncertainty; Downstream; Forecasting methods; Supply
Chain Management; Telco Products.
1 Introduction
The growth of the telecommunications industry in 2012 according to reports international credit rating
agencies, Fitch Ratings still competitive though overshadowed by intense competition. Five largest
telecommunications companies in Indonesia will still own 90% of the market. Five telecom companies
are PT. Telekomunikasi Indonesia Tbk, PT. Telkomsel, PT. Indosat Tbk, PT. XL Axiata and PT. Bakrie
Telecom Tbk (Sugesti, 2012).
Beginning in 2011 as one of the five largest telecommunications companies in Indonesia, Indosat
seeks to provide telecommunications networks with the latest technology and energy-efficient to
provide the best experience for mobile subscribers. One of the major initiatives since 2011 is a
Network Modernization Program, which is an implementation of Indosat's network readiness using
900 and 3G technologies to 4G LTE. The program will be held nationwide provide an improved
customer experience in enjoying voice, SMS and data high speed (broadband) through higher network
capacity and broader coverage of services. Currently modernization has been carried out in West
Sumatra, Bandung, Bali and Greater Jakarta. Director & Chief Commercial Indosat Erik Meijer said
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program done in order to increase network capacity and increased bandwidth in order to speed up the
ride-quality broadband network. With the increase in capacity is expected to increase the number of
subscribers. This is not a positive impact seen in the area of West Sumatra.(koran-jakarta.com 06
Februari 2013 takes 2 Juni 2013).
However, increasing the quality of PT. Indosat above no accompanied with their customer growth area
of West Sumatra which have not improved as expected. Data obtained, decrease their customer as
much as 1.2 % in 2012 to 2.6 million current subscribers (mobile subscribers). This is a separate issue
in the internal management During this time, the company is difficult to determine the demand for
goods to come, due to the demands of the supplier applying the product to be sold out. One of the
strategies adopted distributor Pioneer Primary image is to conduct product sales bonus in a year and
result in erratic price fluctuations. If prices are falling, then the buyer will buy in large quantities to
stockpiling. When prices rise, buyers delay purchase until supplies are sold out again. As a result, the
request does not reflect the actual customer consumption patterns (Susilo, 2008).
Dalam bukunya, Pujawan (2010) stated that the incompatibility of a reservation request and resulted in
a lot of inefficiency in the supply chain and the gap between demand and reservations. When an order
made distributors excess, there will be an accumulation of goods resulting in additional storage costs
or damage to the goods. But when distributors reduce orders, a greater impact can happen, such as
losing the opportunity to sell or even lose customers. The eventual effect, reduced the profitability of
the company (Chopra and Meindl, 2013:265).
Fig.1: Target and Actual Sales of PT. Perintis Citra Pratama in year 2012
(Source: PT. Perintis Citra Pratama)
Based on Picture 1, there are almost all products that sales targets are not achieved, the difference
between the target and the realization of very large, averaging 3,000 units. For Mentari starter pack
product sales target of 72,350 units and 66,342 units were sold in just a year, starter pack IM3 product
sales target of 68,400 units sold only 60,040 units, starter pack Matrix product sales target of 2,310
and 1,561 units sold, starter pack IM2 product sales target of 10,500 units sold 9810 units and for
products starter pack StarOne sales target of 2,030 and only sold 1,559 units in a year. If viewed from
the sale that was one aspect of profitability PT. Pioneer Citra Pratama, then the possibility of a gap of
demand management on all products starter pack Indosat. The company requires an application of
forecasting methods in analyzing sales data and forecast the demand for goods will come stable, so the
buildup or no inventory at distributors and outlets did not happen. It has been recognized that the
policy of forecasting the demand (demand) and ordering (the order) is a major cause demand
management. Forecasting method is the most commonly used indicators by adjusting the plot and
historical data. (Priyanto, 2010).
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
Mentari IM3 Matrix IM2 StarOne
Target
Penjualan
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2 Previous work
Rudi Awaludin (Bogor Agricultural University: 2006), Conclusion: a. The pattern of data demand for
car tires and Eagle Ventura GT3 models show a pattern of horizontal (stationary), due to fluctuations
in demand is around the average value. Identifying patterns can determine a more appropriate method
of forecasting. b. The results of calculations using the method of time series and causal methods are
then compared with the methods of companies that have used the best method that can be used to
predict tire GT3 models that use the Single Exponential Smoothing models. While the best model has
the smallest MSE for forecasting models Eagle Ventura tires recommended using the additive
decomposition model. c. Demanders projections for the next 12 months on a car tire GT3 models have
increased by a total demand of 364 680 units will be achieved. When compared with the 12 -month
total demand for 340 291 backward, then an increase in demand of 7.2 percent . While the prospects
for Eagle Ventura for the next 12 months remained relatively constant and an increase of about 1.5
percent. However, with the condition at the present time where the purchasing power is decreasing,
indicated decreases. Equation: a. This study has similarities to the variables used. b. The purpose of the
study with the application of forecasting methods to minimize the gap between demand and order. The
difference: a. The difference in the object of this study, namely the company's tire production.
Simatupang (Univ. North Sumatra: 2009) Conclusion: a. PT. Field Sibayak not apply in determining
the method of forecasting the demand for goods will arrive. b. In determining the demand for goods to
come, the company uses last sale data as a reference. c. Based on the analysis results of the study,
found that the single moving average forecasting method is more demand for goods in accordance
with the conditions of the company. Equation: a. This study has similarities to the variables used. The
difference: a. Measurements performed at each supply chain forecasting b. The purpose of the study
with the application of simulation methods in the measurement arena forecasting.
Muhammad Aidil (Univ. Bina Darma: 2011) Conclusion: This method is a method that uses a different
weighting technique over the data available at the thought that the most recent data is the most relevant
data for forecasting thus given greater weight. Methods Weighted Moving Average (WMA) is used to
predict values in the next period. Based on the above description, the author makes an information
system entitled " Sales Forecasting Information System on CV Nasta Com Laptop use Method Using
Weighted Moving Average (WMA) " which is expected to assist and facilitate the data processing
purchases, sales and forecasting for the next period in the CV Nasta Com . Equation: a. This study has
similarities to the variables used. b. Forecasting measurements have in common in this study, which
uses forecasting methods WMA. The difference: a. The difference in the object of this study, namely
the company's retail snacks.
Aang Munawar (International Journal: 2008, Vol 4) Conclusion: The results of this study may
recommend to the company's sales method that comes closest to the realization that can help
companies with bottled water. Equation: a. This study has similarities to the variables used. b.
Measurement forecasting that shares a common measurement method of forecasting this research, i.e.
using software WinQSB. The difference: a. A shopping passage and the object of this study, namely
the production of drinking water companies.
Eko Priyanto (Univ Bina Nusantara: 2010) Lead time has a linear relationship with the magnitude of
the bullwhip effect, whereby the greater the lead time will eat more and magnitude of the bullwhip
effect will be enlarged. There is a linear relationship between the parameters of exponential smoothing
with the bullwhip effect where the hike will mean there will be more attention to the new data. Also,
there is a linear relationship to the increase of P in the moving average method for the reduction of the
bullwhip effect. It was found that the forecasting method using the moving average over impact on the
reduction of the bullwhip effect. In this study, the factors that determine the magnitude of the bullwhip
effect among other methods of forecasting, lead times, and inventory replenishment policy factors.
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Equation: b. Research using the moving average method of forecasting . The difference : a. Object of
research
3 Purpose
Based on the formulation of the problem is there, then the purpose of the study that can be identified
include:
1. Measure outlet which has the highest demand management gap.
2. Determine what the appropriate forecasting method for forecasting product demand starter pack PT
Indosat on the outlet of Pioneer Citra Pratama.
4 The contribution of the paper.
The benefits expected to be obtained from the implementation of this final study is:
Companies can find out the concept of demand management to predict upcoming demand forecasting
method.
5 Methodology
Planning and control of the supply chain play a very vital role. This section is the one who is
responsible for creating tactical and operational coordination so that activities of production, material
procurement, and delivery of products can be done efficiently and on time. Today, planning activities
should also be carried out in coordination with other parties in the supply chain. For example, in
determining how much of a product will be produced, information about last sale data at the retail
level as well as how much stock the products that they still have (Chopra, Meindl, 2012: 190).
In this study, the authors will try to predict product starter pack Indosat in 2013 with the forecasting
methods that have been there, adapted to plot historical data with the hope to provide input in the
application of forecasting methods for the company. Forecasting methods are available quite a lot, so it
should do the selection and determination of the most suitable method for the company.
One of the criteria in the selection of this method is to choose a method that has the smallest
forecasting error. In the selection of forecasting methods are not located in the forecasting method that
uses a complex mathematical process or using sophisticated methods, but the method chosen should
produce a prediction that is accurate, timely and understandable by management as a prediction that
can help produce better decisions. Forecasting methods used in this study are the method of time
series, the election is based on the data pattern, data pattern identification is done by plotting the data
and values that can be expected autocorelation appropriate model for a while, after it was last
calculated value of the MSE (Mean Square Error). The model that gets the smallest MSE value will be
taken / chosen to be become the best models of Time series.
This research has some similarities with previous studies, among others, the object of research and
research methods used by Awaludin for research in 2006, which is the research object of PT .
Goodyear which is a passenger car tire. The research method used with the same formula. The
similarity is also seen in the variables used in previous research. These variables are also used by Aidil
(2011), Aang Munawar (2008), Tita Talitha (2010), and Priyanto (2010).
Research Object PT. Pioneer Primary image as a distribution company that organizes the distribution
and trading activities of telecommunication products based in Paandg Indosat. 1996 as one of the
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authorized dealers Indosat area which is located in West Sumatra Road S. No. Parman. 144, Jl. No.
veterans. 32 F, Plaza Andalas Paandg in West Sumatra. PT. Pioneer Citra Pratama has a 45% market
area of West Sumatra (West Sumatra Indosat dealer performance), have a sales / canvasser / sub
dealers in nearly all cities / districts / sub districts in West Sumatra are built directly by 10 canvasser
who routinely perform the distribution of goods and coaching against these outlets, among others BM
Cell, Cool, Minang cell, D & Rcell, Aito Mobile, Megajaya Cell, Cell and Home Moranza Poncell
areas where demand is high and has the highest frequency among the bustle of other outlets.
6 Result and Discussion
6.1 Calculate gap between order and demand in distributor and each outlet.
a. PT. Perintis Citra Pratama
Fig. 2: a graph of order and demand PT. Perintis Citra Pratama during 2012.
Fig. 2 is Order and Demand Results, Order number PT. Perintis Citra Pratama in 2012 amounted to 36
650 units and the amount of demand for 36 650 units. Thus, the gap between demand and reservations
at PT. Pioneer Primary image of 0.
b. BM Cell
Fig. 3: a graph of order and demand BM Cell during 2012.
The sum of the order in BM outlets in 2012 amounted to 710 units and the sum of demand of 800
units. Thus, the gap between demand and bookings at your BM outlets by 90.
0
1000
2000
3000
4000
5000
6000
1 2 3 4 5 6 7 8 9 10 11 12
Order
Demand
0
50
100
150
200
250
1 2 3 4 5 6 7 8 9 10 11 12
Order
Demand
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c. Popular
Fig. 4: a graph of order and demand outlet Popular during 2012.
The sum of the order in outlet Popular in 2012 amounted to 660 units and the sum of demand of 606
units. Thus, the gap between demand and reservation at the outlet line by 54.
d. Minang Cell
Fig. 5: a graph order and demand outlet Minang Cell during 2012.
The sum of the order in Minang outlets in 2012 amounted to 580 units and the sum of demand of 522
units. Thus, the gap between demand and bookings at your Minang outlets by 58.
e. D&R Cell
Fig. 6: a graph order and demand outlet D&R Cell during 2012.
0
20
40
60
80
100
120
1 2 3 4 5 6 7 8 9 10 11 12
Order
Demand
0
20
40
60
80
1 2 3 4 5 6 7 8 9 10 11 12
Order
Demand
0
10
20
30
40
50
60
1 2 3 4 5 6 7 8 9 10 11 12
Order
Demand
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The sum of the order in outlet D & R Cell in 2012 amounted to 325 units and the sum of demand of
269 units. Thus, the gap between demand and reservation at the outlet of D & R Cell is 56.
f. Aito Mobile
Fig. 7: a graph order and demand outlet Aito Mobile during 2012.
The sum of the order in Aito Mobile outlets in 2012 to 150 units and the sum of demand of 142 units.
Thus, the gap between demand and reservation at the outlet Aito Mobile by 8.
g. Megajaya Cell
Fig.8: a graph order and demand outlet Megajaya Cell during 2012.
The sum of the order in your Megajaya outlets in 2012 amounted to 115 units and the sum of demand
of 100 units. Thus, the gap between demand and bookings at your Megajaya outlet at 15.
0
5
10
15
20
25
30
1 2 3 4 5 6 7 8 9 10 11 12
order
demand
0
2
4
6
8
10
12
14
16
18
1 2 3 4 5 6 7 8 9 10 11 12
order
demand
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h. Moranza Cell
Fig. 9: a graph order and demand outlet Moranza Cell during 2012.
The sum of the order in Moranza outlets in 2012 amounted to 70 units and the sum of demand by 56
units. Thus, the gap between demand and bookings at your Moranza outlet at 14.
i. Rumah Poncell
Picture 6.9 is a graph order and demand outlet Rumah Poncell during 2012.
The sum of the order in outlets Poncell house in 2012 for 40 units and the sum of demand by 34 units.
Thus, the gap between demand and bookings outlet Poncell house at 6.
The Gap of distributor and each outlet can explain:
1. PT. Pioneer Citra Pratama
PT. Pioneer Citra Pratama shows Gap / increment the number of requests and order of 0,
meaning no amplification request. That is because the function of PT. Pionner Citra Pratama in
the distribution network as a liaison between the factory distributor warehouse or facility
without a buffer, so that the number of products ordered to the same plant with the purchase
from the distributor. In other words your BM Outlet applying the method push strategy, meaning
that all of the products ordered will be directly distributed to the outlets.
2. Outlet BM Cell
Gap Outlet shows BM / increment the number of requests and order by 90, meaning that
demand amplification is very high at the outlet. In addition, your BM outlet is the outlet has the
0
2
4
6
8
10
12
1 2 3 4 5 6 7 8 9 10 11 12
order
demand
0
2
4
6
8
10
12
1 2 3 4 5 6 7 8 9 10 11 12
order
demand
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highest bustle than other outlets and chart orders to PT. Pioneer Primary image is more volatile
than sales.
3. Online outlet
Gap Outlet Online shows / increment the number of requests and order by 54, meaning that the
demand amplification Outlet Online is quite high. In addition, the graph order and demand more
volatile in late 2012 rather than early 2012.
4. Outlet Minang Cell
Gap Outlet Minang show / increment the number of requests and order by 58, meaning that the
demand amplification Outlet Online is quite high. In addition, the graph order and demand more
volatile in late 2012 rather than early 2012.
5. Outlet D & R Cell
Outlet D & R show Gap / increment the number of requests and order by 56, meaning
amplification, high demand at this outlet additionally, orders and demand graphs are equally
volatile in 2012.
6. Aito Mobile Outlet
Aito Mobile shows Gap Outlet / increment the number of requests and order by 8, meaning a
smoothing demand pattern. Although the amount of the order and demand are relatively equal,
but the number of orders and the demand is not stable and has fluctuated each month.
7. Outlet Megajaya Cell
Gap Outlet Megajaya show / increment the number of requests and order at 15, meaning
amplification high demand at this outlet. In addition, orders and demand graphs are equally
volatile in 2012.
8. Outlet Moranza Cell
Gap Outlet Moranza your show / increment the number of requests and order sebesar14,
meaning amplification high demand at this outlet. In addition, orders and demand graphs are
equally volatile in 2012.
9. Outlet Home Poncell
Poncell indicate Gap Outlet Home / increment the number of requests and order by 6, meaning a
smoothing demand pattern. Although the amount of the order and demand are relatively equal,
but the number of orders and the demand is not stable and has fluctuated each month.
6.2 Selection of an appropriate forecasting method for forecasting product demand starterpack
Data processing at each forecasting begins with the identification of historical data which is then
carried plotting these data. Plotting the data will produce a pattern of data that will be used to
determine the appropriate method of forecasting and forecasting calculations then proceed.
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Fig. 11: Plot data on sales in Distributor and Outlet
Fig. 11 shows a plot of the data distributors and sales outlets. It is seen that the condition of the data is
random. At the beginning of 2012 until the end of the year the demand fluctuated.
Based on the data that formed the plot for all distributors of the forecasting methods used are:
1. Method Simple Average (SA)
2. Method Weighted Moving Average (WMA)
3. Method Single Exponential Smoothing (SES)
4. Method Double Exponential Smoothing (DES)
Once known patterns of historical data, it is then done using the software WinQSB calculations. Of
several forecasting methods have used the best forecasting method by considering the value of the
error (MSE) is the smallest of any of the forecasting methods. This is below the value of the error
(MSE) of each distributor. Here is the selection of forecasting methods for each of the distributors and
outlets.
Table 1. Forecasting calculated result distributor PT. Perintis Citra Pratama
No Distributor Histories
data Method MSE Trk.signal
Chosen
Method
1
PT. Perintis
Citra
Pratama
(random) SA 2375755 -5,1938 SA
(random) WMA 3608809 -2,6480
(random) SES 3999454 -10,205
(random) DES 3608809 -2,648
010000
1 3 5 7 9 11
PT. Perintis Citra Pratama
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Table 1, forecasting calculated result PT. Perintis Citra Pratama, resulted the chosen forecasting
method is Simple Average (SA) method of plot sales data (random) with the smallest Mean Square
Error (MSE) value compared with other method is 2375755.
Table 2. Forecasting calculated result outlet BM Cell
No Outlet Histories
data Method MSE Trk.signal
Chosen
Method
2 BM Cell
(random) SA 916,7228 -4,7356 SA
(random) WMA 961,9091 -1,2222
(random) SES 964,2748 -6,855833
(random) DES 1145,823 -8,576234
Table 2, forecasting calculated result outlet BM Cell, resulted The chosen forecasting method is Simple
Average (SA) Method of plot sales data bersifat acak (random) with the smallest Mean Square Error
(MSE) value compared with other method is 916,7228.
Table 3. Forecasting calculated result outlet Popular
No Outlet Histories
data Method MSE Trk.signal
Chosen
Method
3 Popular
(random) SA 581,4471 -1,956668
(random) WMA 799,4545 -1,298611
(random) SES 525,9555 -2,39314
(random) DES 503,4013 -2,935657 DES
Table 3, forecasting calculated result outlet Popular, resulted The chosen forecasting method is Double
Exponential Method Smoothing (DES) from plot sales data bersifat acak (random) with the smallest
Mean Square Error (MSE) value compared with other method is 503,4013.
Table 4. Forecasting calculated result outlet Minang Cell
No Outlet Histories
data Method MSE Trk.signal
Chosen
Method
4 Minang
Cell
(random) SA 141,9398 -0,419141
(random) WMA 265,8182 0,7142857
(random) SES 130,3629 -1,681455
(random) DES 122,5066 -2,713259 DES
Table 4, forecasting calculated result outlet Minang Cell, resulted The chosen forecasting method is
Double Exponential Smoothing (DES) Method from plot sales data bersifat acak (random) with the
smallest Mean Square Error (MSE) value compared with other method is 122,5066.
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Table 5. Forecasting calculated result outlet D&R Cell
No Outlet Histories
data Method MSE Trk.signal Method
5 D&R Cell
(random) SA 105,4285 1,464436 SA
(random) WMA 226,3636 0,3098592
(random) SES 106,8077 4,550336
(random) DES 111,1044 6,538503
Table 5, forecasting calculated result outlet D&R Cell, resulted The chosen forecasting method is
Simple Average (SA) Method from plot sales data bersifat acak (random) with the smallest Mean
Square Error (MSE) value compared with other method is 105,4285.
Table 6. Forecasting calculated result outlet Aito Mobile
No Outlet Histories
data Method MSE Trk.signal
Chosen
Method
5 Aito
Mobile
(random) SA 30,66107 -2,406895
(random) WMA 40,90909 0,7767442
(random) SES 26,84842 -1,55239
(random) DES 25,69649 -0,956277 DES
Table 6, forecasting calculated result outlet Aito Mobile, resulted The chosen forecasting method is
Double Exponential Smoothing (DES) Method from plot sales data bersifat acak (random) with the
smallest Mean Square Error (MSE) value compared with other method is 25,69649.
Table 7. Forecasting calculated result outlet Megajaya Cell
No Outlet Histories
data Method MSE Trk.signal
Chosen
Method
7 Megajaya
Cell
(random) SA 18,09498 3,245069 SA
(random) WMA 32,36364 0,423076
(random) SES 18,39948 6,307907
(random) DES 19,36179 0,357592
Table 7, forecasting calculated result outlet Megajaya Cell, resulted The chosen forecasting method is
Simple Average (SA) Method from plot sales data bersifat acak (random) with the smallest Mean
Square Error (MSE) value compared with other method is 18,09498.
Table 8. Forecasting calculated result outlet Moranza Cell
No Outlet Histories
data Method MSE Trk.signal
Chosen
Method
8 Moranza
Cell
(random) SA 6,842336 0,881062 SA
(random) WMA 12,63636 -1,064516
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(random) SES 7,124068 -3,417248
(random) DES 7,329283 -5,184213
Table 8, forecasting calculated result outlet Moranza Cell, resulted The chosen forecasting method is
Simple Average (SA) Method from plot sales data bersifat acak (random) with the smallest Mean
Square Error (MSE) value compared with other method is 6,842336.
Table 9. Forecasting calculated result outlet Rumah Poncell
No Outlet Histories
data Method MSE Trk.signal
Chosen
Method
9 Rumah
Poncell
(random) SA 2,812885 -2,576987 SA
(random) WMA 6,090909 -0,44
(random) SES 3,130307 -5,878595
(random) DES 3,481358 -7832938
In Table 6.9, the results of calculation of house outlets Poncell forecasting, yield forecasting method
chosen is the method of Simple Average (SA) from plot sales data is random the value of the Mean
Square Error (MSE) smaller than other methods, namely 2.812885.
6 Conclusion
6.1 For the question 1 in the purpose can explain in Table 10 is a Gap from order and demand
distributor and outlets.
Table 10. Gap from order and demand distributor and outlets
N
No Distributor and Outlets
Gap
1
1 PT. Perintis Citra Pratama 0
1
2 Outlet BM Cell 90
3
3 Outlet Popular 54
4
4 Outlet Minang Cell 58
5
5 Outlet D&R Cell 56
5
6 Outlet Aito Mobile 8
7
7 Outlet Megajaya Cell 15
8
8 Outlet Moranza Cell 14
9
9 Outlet Rumah Poncell 6
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The table shows that the highest Gap is Minang Cell outlets with 58 gaps, its mean that Minang Cell
most incur losses than other outlets.
6.2 Forecasting for each distributor conducted after the identification process historical data through
the plotting of historical data. The plot of the data shows that the sales of all outlets the data is
random conditions. Because the pattern formed is the random data pattern matching method for
the pattern is Simple Average (SA), Weighted Moving Average (WMA), Simple Exponential
Smoothing (SES) and the Double Exponential Smoothing ( DES). Election forecasting model
that is used to reduce the uncertainty of a condition that will occur in the future is the Mean
Square Error (MSE). And the forecasting with the smallest MSE value is taken as the smallest
MSE, but the approach used in Signal Tracking, tracking signal value is called good if it has a
positive value or negative value error error close to zero in order to ensure the accuracy /
reliability of the forecasting method. Of election forecasting model with MSE, PT . Pioneer
Citra Pratama uses SA method with MSE value of 2375755, for BM outlets using the SA
method with a value of MSE of 916.7228, to the Popular outlet using DES method with the
value of MSE of 503.4013, for Minang outlet using the DES with the value of MSE of
122.5066, for outlet D & R using SA method with a value of MSE of 105.4285, for Aito Mobile
outlets using DES with a MSE value of 25.69649, for your Megajaya outlet using SA with a
MSE value of 18, 09 498, for Moranza outlet using SA with a MSE value of 6.842336, while for
outlets Poncell SA method with a value of MSE of 2.812885.
After the selection of forecasting methods for each distributor and outlets, the forecasting results
obtained in each subsequent year the demand for distributors and outlets , can be seen in Table 10.
Recommendation
Research to be conducted include the supply chain in a company that is very broad and complex. It is
therefore necessary boundary problem in this study.
Limitations
The extent of the problem to be studied, include:
1. Items are only focused on product starterpack or starter pack consisting of Indosat GSM:
Matrix, IM3, Mentari, IndosatM2 and CDMA: StarOne.
2. The data taken in 2012.
3. The Supply chain is studied indirect distribution channels.
4. Measurements only on demand management in the PT. Image Pioneer Primary and eight outlets
in West Sumatra that have a high demand frequency.
5. The data in the unit though.
References
Annual Report PT. Indosat Tbk 2012 [Online]
http://www.indosat.com/template/media/editor/files/INDOSAT%20Public%20Expose%202012.
pdf [20 April 2013]
Anatan, Lina and Ellitan, Lena. (2008). Supply Chain Management Teori and Aplikasi. Bandung:
Alfabeta
Chopra, S., and Meindl, P, (2012). Supply chain management: Strategy, Planning, and Operations.
New Jersey:Prentice-Hall
Barung, Marcelinus Mada’. (2011). Pengurangan Bullwhip Effect Pada Rantai Pasok di Level
Hendayani and Darmanda International Journal of Basic and Applied Science,
Vol. 02, No. 04, April 2014, pp. 1-15
www.insikapub.com 15
Distributor Y. Makassar: Universitas Hasanuddin
Fathur, Muhammad. (2009). Penurunan Bullwhip Effect pada Supply Chain LPG12 kg dengan
Centralized Demand Information. Malang: Universitas Muhammadiyah
Indosat. (2012). Indosat, [Online]. www.indosat.com [20 April 2013]
Indrajit, R,E and Djokopranoto, R. (2006). Konsep Manajemen Supply Chain. Jakarta: PT. Grasindo
Hadiguna, Rika Ampuh. (2007). Alokasi Pasokan Berdasarkan Produk Unggulan untuk Rantai Pasok
Sayuran Segar. Volume 9 No. 2, hal 85 – 101, Universitas Andalas Paandg
Handayani, Yuanita. (2007). Analisis Dilema dalam Kolaborasi Rantai Pasok. The 1st PPM National
Conference Research
Hastuti, Dwi. (2010). Pengaruh Perkembangan Telekomunikasi Seluler di Indonesia, [Online].
http://revisi.joglosemar.co/berita/pengaruh-perkembangan-telekomunikasi-seluler-di-indonesia-
32746.html [10 Juni 2013]
Heizer, Jay and Render, Barry (2008). Operation Management, Edisi kesembilan: Salemba Empat
Hussain, Matloub. (2011). Analysis of the Bullwhip Effect with Order Batching in Multi-echelon
Supply Chains. Volume 41, Emerald Group Publishing Limited
Ishak, Aulia (2010), Manajemen Operasi, Meand : Graha Ilmu
Lee, Dong Myung. (2011). Quantifying The Impact of a Supply Chain’s Design Parameters on the
Bullwhip Effect Using Simulation and Taguchi Design of Experiments. Volume 42, Emerald
Group Publishing Limited
Malhotra, Naresh. (2009). Riset Pemasaran Pendekatan Terapan, Edisi keempat. Jakarta: Indeks
Perintis Citra Pratama. (2013). Profil Perusahaan, [Online]. http://www.perintiscitrapratama.com [20
April 2013]
Pujawan, I Nyoman. (2005). Supply Chain Management, Edisi kedua. Surabaya: Guna Widya
Redaksi. (2013). Strategi Operator Modrenisasi Jaringan untuk Kelancaran Akses Data [Online].
http://koran-jakarta.com/index.php/detail/view01/112043 [2 Juni 2013]
Sekaran, Uma. (2010). Research Methods for Business (Edisi Keempat). Jakarta: Salemba Empat
Simchi-Levi, Kaminsky. (2011). Designing and Managing the Supply chain : Concepts, Strategies and
Case Studies, Third edition. Mc Graw. Hill
Sugesti, Hesti. (2012). Pengaruh Kualitas Jasa and Switching Barriers Terhadap Loyalitas Pelanggan
(Studi di PT. Telkomsel Purbalingga). Bandung: Universitas Pendidikan Indonesia
Sugiyono. (2010). Method Penelitian Kombinasi (Mixed Methods) (Cetakan ke-1). Bandung: Alfabeta
Sugiyono. (2010). Method Penelitian Kuantitatif Kualitatif and R&D (Cetakan ke-12). Bandung:
Alfabeta
Susilo, Tri. (2008). Analisis Bullwhip Effect Pada Supply Chain. Volume 8 No. 2, hal 64 – 73, UPN
Jawa Timur
Syafrianita. (2009). Pengukuran Nilai Bullwhip Effect Pada Elemen Eselon Supply Chain Yang
Dipengaruhi Permintaan and Penjualan Fluktuatif dengan Simulasi Arena. Volume 11 No. 2,
Infomatek
Talitha, Tita. (2010). Pengukuran Bullwhip Effect dengan Model Autoregressive. Semarang:
Universitas Wahid Hasyim
Wilrison, Januar. (2010) Penerapan Model Persediaan
Produk Musiman untuk Meminimasi Efek Bullwhip Pada PT. FNG. Jakarta: Universitas Bina
Nusantara
Zikmund, W. G., Babin, B. J., Carr, Jon. C., Griffin, Mitch (2010). Bussiness Research Methods (Eight
Edition). South-Western, Canada: Cengage Learning.