FORECASTING SALES OF SIM CARD “SIMPATI” IN PT. SIMPATINDO MULTIMEDIA BRANCH OFFICE PENGGILINGAN CAKUNG, EAST JAKARTA”

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Aliyaturrohmah                    10212651

Desy Purnamasari                 11212910

Endang Suliswati                  12212347

Laila Majda                           14212153

Lelly Octavia                          14212168

Munawarah Zulhijah           15212158

Nurilita Wiguna                    15212500

Puput Novel                           1A213858

Wahyu Kusuma Dewi          17212638

 

 

 

 

 

GUNADARMA UNIVERSITY

2016

CHAPTER I

PRELIMINARY

 

• Background

Currently, the development of technology in Indonesia is very rapid. One of them is her first cell phone use cell phones are expensive items that can only be used by certain circles, in Indonesia the use of mobile phones very rapidly in the last five years. Before 2008 mobile phone users in Indonesia is less than 80% of the population, and continues rapidly developing States from 2009 until today.

With the increasing use of mobile phones increases then also use SIM cards or also commonly known as the sim card. Along with the increasing use of SIM cards in Indonesia resulted in the level of competition in the mobile carrier companies are becoming increasingly stringent. With so many mobile phone operators for market share in Indonesia.

Therefore the company should be able to take the right decision for the company to keep it running properly. One of them know how much product sales. So with that picture, the company can menentukkan various decisions in order to anticipate the situation is not expected in the foreseeable future.

To be able to take a decision, we need a careful planning in order to set objectives will be achieved. This is consistent with the role of planning is a basic process management in taking a decision and action. In a company selling a product is a very important aspect for the survival of the company. Therefore the preparation of sales planning needs to be done carefully and accurately to determine the development of the company. So in this case need to do a sales forecast that can help the company in terms of product sellers in the future.

Proper use of quantitative methods is important, because not all methods can be used for every issue. Therefore, we must choose a suitable method. Based on these descriptions, have prompted the authors to take the title “FORECASTING SALES OF SIM CARD “SIMPATI” IN PT. SIMPATINDO MULTIMEDIA BRANCH OFFICE PENGGILINGAN CAKUNG, EAST JAKARTA”

 

• Formulation and Limitations

 

• Problem Formulation

The formulation of the problem in this research is: What is forecasting sales of SIM Card Simpati for the month of August 2015 and with Which method is better used by enterprises in the forecast sales of SIM cards?

 

• Limitations

Scientific writing is restricted to sales forecasting calculation problem SIM cards Sympathy from the date of January 1, 2013 – July 15, 2015 and the data was taken on July 16, 2015.

 

• Research Purposes

The purpose of this paper is to determine the sales forecasting Prime Card Sympathy for the month of August 2015 using the Moving Average Weight Moving Average, Exponential Smoothing and with Which method is better used by enterprises in the forecast sales of SIM cards in order to minimize human error.

 

• Benefits of Research

Benefits of this paper is:

1. Academic Benefits

Adding to the experience and conduct scientific research and be able to apply the theories that have been the authors obtained in college and compared in the field.

2. Practical Benefits

As a means for the development of knowledge and insight into the thinking as a scientific information and the development of knowledge about the product sales forecasting.

• Research Methods

 

• Reseach Object

The object of this study is PT.SIMPATINDO MULTIMEDIA grinding branch is located at Jalan Raya Milling rt 04 / rw 011 Cakung, East Jakarta.

 

• Data /Variable

The data used in this paper is secondary data obtained from PT.Simpatindo Multimedia SIM Card sales data in the form of sympathy from the date of January 1, 2013 – July 15, 2015 will be used as research material.

• Data Collection Methods

In an effort to obtain the data used to answer the problems that have been identified, the authors tried to collect data through various means:

1. Research Library, using references from a variety of books related to the material that will be discussed in scientific writing.
2. Field Research, the research used direct observation by reviewing the company in order to obtain data that is more objective. Data scientific writing is obtained by conducting interviews with the authorities in providing an explanation.

 

• Analysis Tools Used

Analysis tools used in scientific writing is forecasting using Moving Average Weight Moving Average, Exponential Smoothing

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

CHAPTER II

THEORETICAL BASIS

 

• Theoretical Framework
  • Definition of Forecasting

Definition of demand forecasting for its products is vital in the planning and monitoring of a business entity.

According to Pangestu Subagyo (2002: 25): “Forecasting is a prediction or forecast that has not happened. In the social sciences everything is uncertain, it is difficult to accurately predict, therefore the use of forecasting which aims to forecast or prediction that can be made to minimize the effect of this uncertainty on the company “.

According to Jay Heizer and Barry Render (2006: 136): “Forecasting is the art or science to predict the future.”

 

• Definition of Sales

According to Mulyadi (2008: 202),: “Sales is an activity performed by the seller sells goods or services in the hope of getting profit from the existence of such transactions and sales can be interpreted as a diversion or transfer of ownership of goods or services from the seller to the buyer. ”

According to Kotler (2006: 457): “Sales is a process whereby the needs of buyers and sales requirements are met, through an exchange of information and interests”.

 

• Definition of Sales Forecasting

According to Sofyan Assauri (1991; 108): “Sales forecasting is an estimate of quantitative traits, including the price of the development of the market of a product produced by the company at a certain period in the future”.

From the above definition can be concluded that the sales forecasting is to estimate the needs of buyers who will come to extend to a maximum benefit.

 

• Forecasting Horizon Time

Forecasting is usually classified by the future time horizon that is divided into several categories according to Jay Heizer and Barry Render (2006: 137) :

1. Short-term Forecasting

This forecasting includes a period of up to one year, but generally less than 3 months. Forecasting is used to plan purchasing, work scheduling, workforce, and production levels.

2. Medium-term Forecasting

This forecasting generally covers the monthly count to 3 years. This forecasting is used to plan sales, planning and production budgets, the cash budget, and analyze a variety of operations plan.

3. Long-term Forecasting

This forecasting is generally for the planning period of 3 years or more. This forecasting is used to plan new products, capital expenditures, location or facility development, and research and development.

 

Medium-term and long-term forecasting can be distinguished from short-term forecasting with see three things :

1. Medium-term and long-term forecasting deals with the issues more thoroughly and to support management decisions related to product design, manufacturing, and process. Setting a decision will be facilities, such as the decision of a general manager to open a new manufacturing plant in Brazil, it can take 5-8 years from the beginning to really – really finished completely.
2. Short-term Forecasting usually apply a different methodology than the long-term forecasting. Mathematical techniques, such as average – moving average, exponential smoothing, and trend extrapolation is generally known for short-term forecasting.
3. Short-term Forecasting tend to be more precise than the long-term forecasting. Affecting factors changes in demand changed daily. Thus, in line with the longer the time horizon, the forecasting accuracy of a person tends to wane. Sales forecasting must be updated regularly to maintain the value and integrity. Forecasting should always be reviewed and revised at each end of the sales period.

Forecasting consists of seven basic steps:

1. Establish the purpose of forecasting
2. Choose what elements would be predictable.
3. Determine the time horizon of the forecast.
4. Choosing the type of forecasting models.
5. Collect the data needed to conduct forecasting.
6. Make forecasting.
7. Implement forecasting results

 

• Type of Forecasting

Organizations typically use three main types of forecasting in the planning of future operations by Hery Prasetya and Fitri Lukiastuti (2009: 44):

1. Economic Forecasting

Is forecasting that explain the business cycle to predict the rate of inflation, the availability of money, the funds needed to build housing and other planning indicators. This forecasting is planning a useful indicator helps organizations to prepare a medium-term forecasting to long term.

2. Technology Forecasting

Is forecasting that takes into account the rate of technological progress can launch exciting new products, which require new factories and equipment. This forecasting This usually requires a long period of time by observing the rate of technological progress.

3. Demand Forecasting

Is the projection of demand for the products or services of a company. This forecasting is also called sales forecasting, which controls production, capacity and scheduling system and becomes an input for financial planning, marketing and human resources. This forecasting is predicting sales of a company at any period in time horizon.

 

• Forecasting Process

Forecasting process according T.Hani Handoko (2000: 260) usually consists of steps – steps as follows:

1. Determination of Interest

The first step consists of determining the estimate of the desired kind. Instead, depending on the destination information needs of managers. Analysis discuss with decision makers to know what their needs and determine:

1. Variables – what variables to be estimated.
2. Who will use the results of forecasting.
3. For the purpose – what purpose forecasting results will be used.
4. Estimated long-term or short-term desired.
5. Desired degree of permanence estimates.
6. When is the estimated required.
7. Part – part of forecasting is desired, such as forecasting for the buyer group, product group or geographical area.
8. Development Model

A more modest presentation systems studied. In forecasting, the model is an analytical framework which when inserted input data will generate an estimated sales in the future (or what variables are considered. For example, if companies want to predict sales of the “behavior” of her be linear, the chosen model possible: sales = A + BX, where X represents the unit time, A and B are the parameters that describe the position and the slope of the line on the graph.

3. Testing Model

Models are usually tested to determine the level of accuracy, validity and reliability expected. This often includes historic application to the data, and preparation of estimates for years now with real data available. The value of the model is determined by the degree of accuracy of forecasting results with reality. In other words, the test models intended to determine the validity or logic predictive ability of the model.

4. Application Model

Analysis of applying the model in this phase, historic data is included in the sales model = A + BX, analysts apply techniques – mathematical techniques in order to obtain A and B.

5. Revisions and Evaluations

Predictions that have been made must be constantly revised and revisited. Repairs may be necessary because of changes in the company or the environment, such as the price level of enterprise products, the characteristics of the product, advertising expenditures, the level of government expenditures, monetary policy and technological advances. Evaluation on the other hand is a comparison predictions with actual results to assess the provision or use of a methode forecasting techniques. This step is necessary to maintain the quality of the estimates of time when that will come.

 

• Forecasting Techniques

The techniques of forecasting by T.Hani handoko (2000: 262):

1. Qualitative Techniques.

Qualitative techniques are subjective or “judgmental” or based on estimates and opinions of. Various sources of income for forecasting business conditions are as follows:

1. The Executive

The executives have the ability to provide useful input forecasting, especially of the managers who have experience in the industry long enough or in similar companies.

1. People Sales.

The members of this group are still in touch with the customer, so it will be able to estimate the purchasing plans, attitudes and their needs. The sales people are also a source that can provide about tactics on a competitor now and forecasts in the future.

1. Clients

Subscriptions (customers) that outputs (product or service) companies are sometimes willing and eager to express their purchase plans. It is often found, especially for companies that sell products to the market industry, and the information provided the subscription is feedback for the company. Subscriptions may convey this information personally to the executor and sales people, through letters and phone charging a consumer survey questionnaires or private interviews.

1. Etc

These specialists are experts in various fields giving opinions are highly valued.

 

While a variety of qualitative forecasting techniques that can be used briefly be described as follows:

1. Delphi method

Is a technique that uses a systematic procedure to obtain a consensus of the opinions of a group of experts. Delphi process is done by asking the members of the group to give a series of predictions through their responses to a questionnaire. Then the moderator collects and formulating list of new questions and dealt to the group. So there is a process of “Learning” for the group because they receive new information and no influence on the pressure group or individual domination.

1. Market research

Forecasting technique is useful especially when there is a shortage of data or historical data is not reliabel. This technique is typically used to predict long-term demand and sales of new products. Market research requires a series of steps as follows:

• Ensure that the information sought.
• Ensuring information sources.
• Establish a method of procurement or collection of data, with personal interviews, telephone surveys, mail surveys of observation, interviews and group panel, or test the market.
• Develop and conduct a preliminary test measurement equipment.
• Formulate a sample.
• Getting information.
• Perform tabulation and analysis.

1. Analogy History

Forecasting is done by using practice-historical experiences of a similar product. Forecasting new products could be linked to the stages in the life cycle of similar products.

1. Consensus Panel

The idea was discussed by the group will produce better predictions than done by someone. The discussion is conducted in meetings open exchange of ideas. Participants may comprise executive, sales people, experts or subscription.

2. Quantitative Techniques

Quantitative techniques are commonly used forecasting method to predict events in the future by looking at the data in the past, this data series is a series of observations of various variables according to the time which is usually tabulated. Quantitative forecasting results is preferred because it provides a view that is more real and more objective in the magnitude of the value of forecasting. Various quantitative forecasting techniques are as follows:

1. Freehand

With this method the trend line is made freely without using a mathematical formula. The curve trend “freehand” is depicted through data points and is the easiest way of presenting. Forecast can be obtained simply by drawing a trend line for the period of forecast. However, what seems to be adequate for a company does not necessarily apply to another company, or this method has a very high subjectivity so rarely used.

1. Least Squares (Least Squares)

The method most often used to determine the equation of trend data because this method produces what is mathematically described as a “line of best fit”. This trend line has the properties:

• The sum of the whole vertical deviation of the data points to the line is zero.
• The sum of the squared deviations entire vertical historical data of the line is the minimum.
• The line through the average X and Y.

The formula in use:

For linear equations, trend lines sought by the simultaneous settlement of the value of a and b in normal following two equations:

Σ Y = a + b n Σ X

Σ XY = a + b X Σ Σ X2

When the midpoint of the data as the basis years, then Σ X = 0 & retrievable eliminated from equation above, so that it becomes:

Σ Y

Σ Y = n aa =n

Σ XY

Σ Σ X2 XY = b b =Σ X2

1. Moving Average

Forecasting methods by combining data from several periods of the latest / last. The moving average is obtained by summing and finding the average value of a specified period, each time eliminating the value of the longest and add new value.

The formula used is:

Information :
Ft = Forecasting (Forecasting)

X = Data Period

n = Duration

 

1. Weight Moving Average

A method similar to the method of moving average, differing only in the addition of weight to each data. The latest data are included in the calculation of the average period given greater weight. The formula used is:

Information :

A = Weight of Largest

B = Weight of Largest 2

C = Weight of Largest 3, etc.

n = Data Period

n-1 = Data 1st period before the last period

n 2 = Data of the 2nd period before the last period

 

1. Single Exponential Smoothing

A method of moving average forecasts are weighing in on the past data with an exponential manner. In this method of forecasting is done by other means forecast last period plus the portion of the difference or error rate (denoted by α) between the last period of real demand and forecast the most recent period, the equation is:

Information :

Ft = Forecasting

Ft – 1 = forecast for the previous period (t – 1)

α = Smoothing constant (the portion of the difference)

At – 1 = real inquiry earlier period.

 

• Study Research Similar

This kind of study is taken from research that have similar topics / variables that are and will be examined by the author.

1. Name: Angga Dwi Yuni Anto

NPM: 10207119

Title: Forecasting Sales Starter Pack Xl Free On PT.Excelcomindo (Xl) Branch Kalimas Bekasi

Supervisor: Sri Kurniasih Agustin, SE., MM

Conclusion: Based on sales forecasting XL card on PT.Exelcomindo Non Bekasi branch Kalimas east for the month August 2010 the most precise method used is a method Weight Moving Average, with great Weight Moving Average method of forecasting pedana XL card for the month of August 2010 resulted in the value of forecasting 24,999 cards with a value of 188 card error while using Moving Average yield value forecasting error value 189 24 849 with the card, and Exponential Smoothing generate value forecasting error value 242 25 232 with the card.

2. Name: Eddie Wibowo

NPM: 10205387

Title: Motorcycle Sales Forecasting Vario In PT.Tunggul Mitra Sejati Bekasi

Supervisor: Lies Handrijaningsih, SE, MM

Conclusion: After forecasting conducted on the value of sales Motorcycle Vario In PT.TunggulMitraSejati Bekasi Average, Weight Moving Average and Exponential Smoothing. Forecasting using Exponential Smoothing is a method of the smallest error or mistake by the error value of 7 units of value forecasting 141 units, compared with the Moving Average method that generates an error value of 14 units of value forecasting 140 units and Weight Moving Average which generates the error value of 14 units of the value of forecasting 141 units.

• Analysis Tool

The analysis tool used is the Moving Average and Moving Average Weight. Some of the assumptions of both of these methods are:

1. Data from consecutive time.
2. Using the data of the past.
3. Do not have equality.
4. Not suitable for data that are no symptoms trend.
5. Can not keep up with changes drastically.

 

1. Moving Average

Forecasting methods by combining data from several periods of the latest / last. The goal is to make the data into data fluctuates relatively stable so that the fluctuation of the pattern or data to be smooth and relatively evenly (Hani Handoko, 200: 157) Steps forecasting using Moving Average:

1. Specifies the number of periods to get the average price.
2. Make a calculation table
3. Finding the value of total mobile
4. Finding value in forecasting

 

The formula used is:

Information :

Ft = Forecasting (Forecasting)

X = Data period

n = Timed

2. Weight Moving Average

A method similar to the method of moving average, differing only in the addition of weight to each data. The latest data are included in the calculation of the average period given greater weight. The weakness of this method is the response can not be easily changed without changing each of the weights.

The formula used is:

Information :

A = Weight of Largest

B = Weight of Largest 2

C = Weight of Largest 3, etc.

n = Data Period

n-1 = Data 1st period before the last period

n 2 = Data of the 2nd period before the last period

3. Single Exponential Smoothing (ES)

Exponential Smoothing is a meode moving average forecasting that conduct declining exponential weighting of the values of the older observations (Makridakis, 1993: 79).

In this method of forecasting is done by other means forecast last period plus the portion of the difference or error rate (denoted by α) between the last period of real demand and forecast the most recent period, the equation is:

Information :

Ft = Forecasting

Ft – 1 = forecast for the previous period (t – 1)

a = Smoothing constant (the portion of the difference)

At – 1 = real inquiry earlier period

4. Forecasting Errors

Forecasting error has two elements that must be considered:

1. The difference between the real demand forecasting (error)
2. Directions mistake, that is, whether real demand are above or below forecast. There is a mistake commonly used measure is the Mean Absolute Deviation (MAD), which is the size of finding the difference between real and forecast demand with the average rate for a predicted error is:

MAD = Error

N – n

Information :

N = Number of sales data

n = number of periods

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

CHAPTER IV

DISCUSSION

 

• Data and Research Object
  • A Brief History of the Company

Simpatindo Multimedia is a subsidiary of Sarindo Group established since October 29, 2002 with the legal form of a Limited Liability Company. PT. Simpatindo Multimedia is a company engaged in trading and distributing. At this time PT. Simpatindo Multimedia act as Authorized Dealer Telkomsel. PT. Simpatindo MULTIMEDIA has carried on business that is from the year 2002 – 2015 and has distributed four prime cards telkomsel products and has a superior product that Simpati Sim Card.

Since 2002 PT. Simpatindo MULTIMEDIA already distributes SIM cards Sympathy enough consistency, because the product was distributed in accordance with its marketing channels. In doing marketing in PT.SIMPATINDO MULTIMEDIA opened branches spread all over Indonesia with marketing personnel experienced in the field. In this case the issuing PT.TELKOMSEL Simpati Sim Card products that have been adjusted to the needs of consumers and distributed directly by PT. MULTIMEDIA Simpatindo.

In this case there are four types of card products that have been distributed by the prime PT.SIMPATINDO MULTIMEDIA, but only 1 products in featured, Simpati Sim card products. Because this product is preferred by network facilities PT.TELKOMSEL by offering a strong signal and spread throughout Indonesia. It can be seen from the number of products sold more than other types of products. In other words that the Simpati Sim Card products have a distinctive position for PT.SIMPATINDO MULTIMEDIA.

 

• Organizational Structure

1. Director
2. As head of the company.
3. Formulating objectives and determining overall company policy
4. Lead and oversee the development of the company through the report – a report received and take the necessary decisions.
5. Coordinate all the existing sections in the company so as to create a harmonious cooperation and the achievement of company objectives
6. Develop and establish plans, objectives and strategies for the sale of short-term and long-term.
7. Financial Section
8. Accept and dispense cash for corporate purposes
9. Making financial report
10. The company’s financial control
11. Parts Warehouse

Responsible to the leading companies on the availability of goods.

4. Part Sales
5. Responsible to corporate leaders on sales transactions
6. Organize sales activities and promotions in order to achieve maximum benefit.
7. Responsible for the delivery of goods to the place of purchase orders.
8. Create sales reports.
9. The Public Service
10. Section in charge of providing services to consumers products company.
11. Give information in the service to users of the company’s products.

 

• Discussion and Research

For each company forecasting have an important role to market the products offered, precise sales forecasting whether or not to become a reference for evaluating the targets that have been implemented by the company.

At this writing, the author uses the method of moving average, weight moving average and exponential methods. The data used by sales 5 different SIM cards sympathy card with an internet mania prime sympathy, sympathy starter pack talk mania, mania of smartphones sympathy card is prime, prime cards sympathy sms mania, and starter pack sympathy nelpon packages home. Here is a prime sympathy card sales data on PT. Multimedia Simpatindo Cakung East Jakarta branch mill for 31 months from the date of 01 January 2013-15 July 2015.

 

 

 

 

 

 

Table 4.1

Starter Pack of Simpati Sim Card Sales Data

The period January 2013 – July 2015

Month	Sales ( Unit )
January 2013 February March April May June July August September October November December	972 988 990 1050 1010 1030 1080 1110 1125 1120 1300 1315
January 2014 February March April May June July August September October November December	1350 1415 1380 1400 1390 1420 1460 1500 1530 1600 1580 1620
January 2015 February March April May June July	1615 1720 1744 1812 1822 1848 1860

 

Figure 4.2 Sales Chart PT.Simpatindo Multimedia

Year 2013

 

Figure 4.3 Sales Chart PT.Simpatindo Multimedia

Year 2014

Figure 4.4 Sales Chart PT.Simpatindo Multimedia

2015

Based on the above data, it can be seen selling Simpati Sim Card PT.Simpatindo Multimedia whenever changes. To avoid instability in the company should be able to predict exactly in sales in the coming year.

 

• Calculation of Sales Data
  • Sale Forecasting PT. Simpatindo Multimedia Branch Office Penggilingan Jakarta Timur used Metode Moving Average with 3 period.

 

Forecasting is done to assist the planning and supervision of the prime Simpati card sales , it helps the company’s internal decision-making and is very useful for the task of Top Managers . With the sales forecasting the company aware of the possibility of activities in the future , so managers can seek redress in order to efficient sales service . Application of forecasting with Moving Average Method on PT.Simpatindo Multimedia Branch Penggilingan Cakung, East Jakarta Branch intended to solicit sales forecasting Simpati Sim Card.

By using the method of Moving Average 3 -month period , then the result was obtained by using the formula :

 

 

 

 

 

Description :

Ft         = Forecasting

X          = Data Period

n          = Time Period

 

Table 4.2

Calculation of Sales Forecasting Simpati Sim Card August 2015 Method with Moving Average 3 period

Month	Sale	Forecasting	Error (e)
January 2013	972	–	–
February	988	–	–
March	990	–	–
April	1050	983,3	66,7
May	1010	1009,3	0,7
June	1030	1016,7	13,3
July	1080	1030	50
August	1110	1040	70
September	1125	1073,3	51,7
October	1120	1105	15
November	1300	1118,3	181,7
December	1315	1181,7	133,3
January2014	1350	1245	105
February	1415	1320	95
March	1380	1358,3	21,7
April	1400	1381,3	18,7
May	1390	1398,3	-8,3
June	1420	1390	30
July	1460	1403,3	56,7
August	1500	1423,3	76,7
September	1530	1460	70
October	1600	1496,7	103,3
November	1580	1543,3	36,7
December	1620	1570	50
January2015	1615	1600	15
February	1720	1605	115
March	1744	1651,7	92,3
April	1812	1693	119
May	1822	1758,7	63,3
Juny	1848	1792,7	55,3
July	1860	1827,3	62,8
August	–	1843,3	–
Total			1760,6

 

Calculations Forecasting Monthly:

2013

1. April = 972 + 988 + 990                  = 983,3

3

2. May = 988 + 990 + 1050                = 1009,33

3

3. June = 990 + 1050 + 1010             = 1016,7

3

4. July = 1050 + 1010 + 1030                       = 1030

3

5. August = 1010 + 1030 + 1080 = 1040

3

6. September = 1030 + 1080+ 1110 = 1073,3

3

7. October = 1080 + 1110 + 1125             = 1105

3

8. November = 1110 + 1125+ 1120 = 1118,3

3

9. December = 1125 + 1120+ 1300 = 1181,7

3

2014

1. January = 1120 + 1300+ 1315             = 1245

3

2. February = 1300 + 1310+ 1350           = 1320

3

3. March = 1310 + 1350+ 1415             = 1358,3

3

4. April = 1350 + 1415+ 1380                        = 1381,3

3

5. May = 1415 + 1380+ 1400            = 1398,3

3

6. June = 1380 + 1400+ 1390                        = 1390

3

7. July = 1400 + 1390+ 1420                         = 1403,3

3

8. August = 1390 + 1420+ 1460             = 1423,3

3

9. September = 1420 + 1460+ 1500 = 1460

3

10. October = 1460 + 1500+ 1530             = 1496,7

3

11. November = 1500 + 1530+ 1600 = 1543,3

3

12. December = 1530 + 1600+ 1580 = 1570

3

2015

1. January = 1600 + 1580+ 1620             = 1600

3

2. February = 1580 + 1620+ 1615             = 1605

3

3. March   = 1620 + 1615+ 1720             = 1651,7

3

4. April = 1615 + 1720+ 1744                         = 1693

3

5. May = 1720 + 1744+ 1812                       = 1758,7

3

6. June =1744 + 1812+ 1822               = 1792,7

3

7. July =1812 + 1822+ 1848               = 1827,3

3

8. August =1822+1848+1860                  = 1843,3

3

MAD     = ∑ Error

N – n

= 1760,6

31 – 3

= 62,8

 

From the above calculation using the method in the know Moving Average sales forecasting results in August 2015 is as much as 1843 card and forecasting error ( Mean Absolute Deviation ) was as much as 62.8 or 63 prime Simpati Sim Card.

The deviation can be calculated from the amount of the sales forecast range as follows :

 

The Range         = Ft – MAD     < X <   Ft + MAD

= 1843 – 63     < X <   1843 + 63

= 1780             < X <   1906

(Minimum Sales          )                      (MaximumSales)

 

From the above calculation is unknown if the company will sell , it should be the number of sales ranged from 1780 to 1906 Simpati Sim Card is prime .

 

• Sales forecasting Simpati Sim Card August 2015 at PT . Simpatindo Multimedia Branch Milling East Jakarta . Method Using Moving Average Weight By Weight 70 % and 30 %

 

To simplify the calculation in use Weight Moving Average method used here considered that the two month period is forecast was best achieved by using a weighting of 70% and 30 % . With Weight Moving Average method with a weight of 70% and 30 % , then the result was obtained by using the formula :

 

 

 

 

 

Description :

A                        = Bobot Terbesar

B             = Bobot Terbesar Ke-2

n            = Data Period

n-1          = Data 1 period before last period

n-2          = Data 2 period before last period

 

Table 4.3

Calculation of Sales Forecasting Psimapti Sim Card August 2015 With Weight Moving Average Method 2 periods .

Month	Sales	Forecasting	Error (e)
January2013	972	–	–
February	988	–	–
March	990	983,2	6,8
April	1050	989,4	60,6
May	1010	1032	-22
June	1030	1022	8
July	1080	1024	56
August	1110	1065	45
September	1125	1101	24
October	1120	1120,5	-0,5
November	1300	1121,5	178,5
December	1315	1246	69
January2014	1350	1310,5	39,5
February	1415	1339,5	75,5
March	1380	1395,5	-15,5
April	1400	1390,5	9,5
May	1390	1394	– 4
June	1420	1393	27
July	1460	1411	49
August	1500	1448	52
September	1530	1488	42
October	1600	1521	79
November	1580	1579	1
December	1620	1586	34
January2015	1615	1608	7
February	1720	1616,5	103,5
March	1744	1688,5	55,5
April	1812	1736,8	75,2
May	1822	1791,6	30,4
June	1848	1819	29
July	1860	1840,2	39,8
August	–	1856,4	–
Total			1155,1

 

Calculations Forecasting Monthly

2013

1. March = ((0,7*988) + (0,3*972))                   =983,2
2. April = ((0,7*990) + (0,3*988))                   = 989,4

 

3. May = ((0,7*1050) + (0,3*990))                 = 1032

 

4. June = ((0,7*1010) + (0,3*1050))               = 1022

 

5. July = ((0,7*1030) + (0,3*1010))               =1024
6. August = ((0,7*1080) + (0,3*1030)) =1065
7. September = ((0,7*1110) + (0,3*1080))             =1101
8. October = ((0,7*1125) + (0,3*1110) =1120,5
9. November = ((0,7*1120) + (0,3*1125))             = 1121,5

 

10. December = ((0,7*1300) + (0,3*1120))             = 1246

 

2014

1. January = ((0,7*1315) + (0,3*1300)) = 1310,5

 

2. February i = ((0,7*1350 + (0,3*1315))              = 1339,5

 

3. March = ((0,7*1415) + (0,3*1350))               = 1395,5

 

4. April = ((0,7*1380) + (0,3*1415)   )           = 1390,5

 

5. May = ((0,7*1400) + (0,3*1380))               = 1394

 

6. June = ((0,7*1390) + (0,3*1400))              = 1393

 

7. July = ((0,7*1420) + (0,3*1390))               = 1411

 

8. August = ((0,7*1460) + (0,3*1420))             = 1448

 

9. September = ((0,7*1500) + (0,3*1460))             = 1488

 

10. October = ((0,7*1530) + (0,3*1500)) = 1521

 

11. November = ((0,7*1600) + (0,3*1530))             =1579
12. December = ((0,7*1580) + (0,3*1600))             = 1586

 

2015

1. January   = ((0,7*1620) + (0,3*1580)) = 1608

 

2. February = ((0,7*1615) + (0,3*1620)) = 1616,5

 

3. March   = ((0,7*1720) + (0,3*1615)) =1688,5

 

4. April = ((0,7*1744) + (0,3*1720))               =1736,8
5. May = ((0,7*1812) + (0,3*1744))              = 1791,6

 

6. June = ((0,7*1822) + (0,3*1812))                = 1819

 

7. July = ((0,7*1848) + (0,3*1822))                = 1840,2

 

8. August = ((0,7*1860) + (0,3*1848))                = 1856,4

 

MAD     = ∑ Error

N – n

= 1155,1

31 – 2

= 39,8

From the above calculation using the Weight Moving Average to know the results of forecasting sales in August 2015 is as much as 1856 card and forecasting error ( Mean Absolute Deviation ) was as much as 39.8 or 40 prime Simpati Sim Card .

The deviation can be calculated from the amount of the sales forecast range as follows :

The Range         = Ft – MAD     < X <   Ft + MAD

= 1856 – 40     < X <   1856+ 40

= 1816             < X <   1896

(Minimum Sales     )                              (Maximum Sales)

 

From the above calculation is unknown if the company will sell , it should be the number of sales ranging from 1816 up to 1896 Simpati card is prime .

 

• Sales forecasting Prime Card Sympathy on PT . Multimedia Simpatindo East Jakarta Branch Milling Using Methods Exponential Smoothing (ES) = 0,05

Ft = { ( Ft– 1)+     α [ ( At – 1 ) – ( Ft – 1 ) ] }

 

 
To simplify the calculation in use Method Exponential Smoothing .by using Method Exponential Smoothing, the result was obtained by using the formula :

 

Explanation :

Ft                     = Forecasting

Ft – 1               = The forecast For the Previos Period ( t – 1)

Α                      = Smoothing Constant (Portion of different )

At – 1               = Real demand before period

 

Table 4.4

Calculation of Sales Forecasting Simpati Sim CardAugust 2015 using the ES α = 0.05

Monthly	Sales	Forecast	Error (e)
January 2013	972	–	–
February	988	–	–
March	990	972,8	17,2
April	1050	973,7	76,3
May	1010	977,5	32,5
June	1030	979,2	50,8
July	1080	981,7	98,3
August	1110	986,6	123,4
September	1125	992,7	132,3
October	1120	999,3	120,7
November	1300	1005,3	294,7
December	1315	1020	295
January 2014	1350	1034,7	315,3
February	1415	1050,2	364,8
March	1380	1068,4	311,6
April	1400	1084	316
May	1390	1099,8	290,2
June	1420	1114,3	305,7
July	1460	1129,6	330,4
August	1500	1146,1	353,9
September	1530	1163,8	366,2
October	1600	1182,1	417,9
November	1580	1203	377
December	1620	1221,9	398,1
January 2015	1615	1241,8	373,2
February	1720	1260,5	459,5
March	1744	1283,5	460,5
April	1812	1306,5	505,5
May	1822	1331,8	490,2
June	1848	1356,3	491,7
July	1860	1380,9	291,7
August	–	1404,9	–
Calculations			8460,3

 

Calculations Forecasting Monthly :

2013

1. March = 972 + 0.05 (988 – 972 )             =972,8
2. April = 972,8 + 0.05 (990 – 972,8 )             = 973,7

 

3. May = 973,7 + 0.05 (1050 – 973,7 )                       = 977,5

 

4. June = 977,5+ 0.05 (1010 – 977,5 )                        = 979,2

 

5. July = 979,2 + 0.05 (1030 – 979,2 )                       =981,7
6. August = 981,7 + 0.05 (1080 – 981,7 )             =986,6
7. September = 986,6 + 0.05 (1110– 986,6 ) =992,7
8. October = 992,7 + 0.05 (1125– 992,7)             =999,3
9. November = 999,3 + 0.05 (1120 – 999,3)             =1005,3
10. December = 1005,3 + 0.05 (1300 – 10005,3 ) = 1020

 

2014

1. January = 1020+ 0.05 (1315– 1020) = 1034,7
2. February = 1034,7+ 0.05 (1350 – 1034,7)        = 1050 ,2

 

3. March = 1050,2 + 0.05 (1415– 1050,2 )        =1068,4

 

4. April = 1068,4+ 0.05 (1380– 1068,4)         =1084

 

5. May = 1084 + 0.05 (1400– 1084)               =1099,8

 

6. June = 1099,8 + 0.05 (1390 – 1099,8)       = 1114,3

 

7. July = (1114,3 + 0.05 (1420 – 1114,3)      = 1129,6

 

8. August = 1129,6 + 0.05 (1460 – 1129,6) = 1146,1

 

9. September = 1146,1+ 0.05 (1500 – 1146,1) =1163.8
10. October = 1163,8 + 0.05 (1530 – 1163.8) =1182,1
11. November = 1182,1 + 0.05 (1600– 1182,1) =1203
12. December = 1203 + 0.05 (1580 – 1203) = 1221,9

 

2015

1. January   = 1221,9 + 0.05 (1620 – 1221,9) =1241,8

 

2. February = 1241,8 + 0.05 (1615 – 1241,8) = 1260,5

 

3. March = 1260,5 + 0.05 (1720 – 1260,5)        = 1283,5

 

4. April = 1283,5 + 0.05 (1744– 1283,5)                     =1306,5
5. May = 1306,5 + 0.05 (1812– 1306,5)        =1331,8

 

6. June = 1331,8 + 0.05 (1822– 1331,8)                      = 1356,3

 

7. July = 1356,3 + 0.05 (1848 – 1356,3)                     = 1380,9

 

8. August= 1380,9 + 0.05 (1860 – 1380,9) = 1404,9

 

MAD     = ∑ Error

N – n

= 8460,3

31 – 2

= 291,7

From the above calculation using the Exponential Smoothing to know the results of forecasting sales in August 2015 was as much as 1404.9 or 1405 card and forecasting error ( Mean Absolute Deviation ) was as much as 291.7 or 292 prime Sympathy cards .

The deviation can be calculated from the range of the amount of the sales forecast as follows :

Forecasting        = Ft – MAD     < X <   Ft + MAD

= 1405 – 292   < X <   1405+ 292

= 1113             < X <   1697

(Minimum Sales)         (Maksimum Sales)

 

From the above calculation is unknown if the company will sell , it should be the number of sales ranged from 1113 until 1697 SIM Card Sympathy.

 

• Summary Calculation Results

 

Table 4.5

Summary Calculation Results

Forecasting Method	Moving Average	Weight Moving Average	Eksponential Smoothing
Sales ( August 2015)	1843	1856	1405
Mean Absolut Deviation	63	40	292
sales forecast	1780< X <1906	1816< X<1896	1113< X <1697

Explanation :

Moving Average method, forecasting sales that may occur in August 2015 as many as 1843 cards and Mean Absolute Deviation much as 63 prime Sympathy cards. While the range of sales of 1780sampai dengan1906 card. If the range of the sale of the cards in 1780 under prime Simpati Sim Card sales said to be bad, and if the sale of Simpati Sim Card above 1906 then the sale is said to be good.               Weight Moving Average Method method, forecasting sales that may occur in August 2015 as many as 1856 cards and Mean Absolute Deviation 40 prime Simpati Sim Card. While the range of sales of 1816 until 1896 card. If the range of the sale of the cards in 1816 under prime Sympathy card sales said to be bad, and if the sale of SIM cards Sympathy cards above 1896 then the sale is said to be good.               Methods methods Exponential Smoothing, forecasting sales that may occur in August 2015 as many as 1405 cards and Mean Absolute Deviation 292 prime Sympathy cards. While the range of sales of 1113sampai dengan1697 card. If the range of the sale under the sale of starter packs 1113kartu Sympathy is deplorable, and if the sale of SIM cards Sympathy cards above 1697 then the sale is said to be good.               So we can conclude that the method closest to the truth or the smallest error rate is the Moving Average Weight. Weight Moving Average method is better than the method Moving Average and Exponential Smoothing Methods. So if the forecast sales of the Method of Weight Moving Average forecasting error is relatively small so the results of this forecasting method will guarantee its accuracy. Methods Weight Moving Average forecast sales of cards for the month of August 2015 as many as 1856 cards and Mean Absolute Deviation 40 prime Sympathy cards. While the range of sales of 1816sampai dengan1896 card. If the range of the sale of the cards in 1816 under prime Sympathy card sales said to be bad, and if the sale of SIM cards Sympathy cards above 1896 then the sale is said to be good.               From the results that have been obtained to minimize the risk of loss / excess demand, then the company should be able to sell at least 1816 and maximum prime cards Sympathy Sympathy 1896 SIM Card. It can be seen from the sales range Weight Moving Average Method.               Of the three methods used to perform sales forecasting acquired method is the best method Moving Average Weight. Because the error rate is smaller than the Moving Average Method and Exponential Smoothing Method.

CHAPTER V
CONCLUSION

 

5.1       Conclusion

Based on the results of the Third calculation method, the authors draw the following conclusion:

The method closest to the truth or the smallest error rate is the Moving Average Weight. Weight Moving Average method is better than the method Moving Average and Exponential Smoothing Methods. So, if the forecast sales of the method of moving average error in forecasting relatife smaller, so the results of this forecasting method will guarantee its accuracy. Methods Weight Moving Average forecast sales of cards for the month of August 2015 as many as 1856 cards and Mean Absolute Deviation 40 prime Simpati Sim Cards. While the range of total sales in 1816 up to 1896 cards. If the range of the sale of the cards in 1816 under prime Simpati Sim Card sales said to be bad, and if the sale of Simpati Sim cards above 1896 then the sale is said to be good. From the results that have been obtained to minimize the risk of loss / excess demand, then the company should be able to sell at least 1816 and maximum 1896 SIM Card. It can be seen from the sales range Weight Moving Average Method.Of the three methods used to perform sales forecasting acquired method is the best method Moving Average Weight. Because the error rate is smaller than the Moving Average Method and Exponential Smoothing Methods.

 

5.2       Suggestions

After researching, calculating, analyzing and conclusions of forecasting sales at PT.Simpatindo Multimedia Branch Penggilingan East Jakarta, the author tries to give advice, if someday the company wants to do sales forecasting, you should use Method for Weight Moving Average error rate is smaller than method of Moving Average and Exponential Smoothing Methods. Although the results are not precise forecasting up to 100% but can be considered to make the planning and the target company’s activities further in increasing sales strategy.