Demand Forecasting in Downstream Supply Chain …2)(4).pdf · · 2014-04-30Demand Forecasting in...

1

Demand Forecasting in Downstream Supply Chain Telco Product

Ratih Hendayani

1 and Adrian Darmanda

2

1,2

Telkom University

[email protected]

[email protected]

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

International Journal of Basic and Applied Science,

Vol. 03, No. 03, April 2014, pp. 1-15

Hendayani and Darmanda

2 Insan Akademika Publications

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

Hendayani and Darmanda International Journal of Basic and Applied Science,

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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


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


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


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


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


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



Table 8. Forecasting calculated result outlet Moranza Cell

No Outlet Histories


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



Table 9. Forecasting calculated result outlet Rumah Poncell

No Outlet Histories


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]

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