The Analysis of Forecasting Demand Method of Linear Exponential Smoothing

The purpose of this study was to evaluate the accuracy of forecasting demand for Batik Fendy using MAPE techniques. The type of this research is a quantitative descriptive study, using secondary data from the sales of the company Batik Fendy in the period November 2018 - Onkoter 2019. There are 5 stages in data analysis, namely 1) tabulating sales data and production data of Fendy batik, 2) evaluating sales forecasting methods conducted by the Batik Fendy company with the MAPE technique, 3) projecting alpha and beta values as the basis for forecasting linear exponential smoothing , 4) forecasting requests for Batik Fendy with the Linear Exponential Smoothing method , and 5) evaluating the forecasting method with the MAPE technique. The results of this study are the MAPE value of demand forecasts made by the Batik Fendy company is 17.5%; This figure shows the highest percentage of error in the variant of the Sarimbit Long Sleeve Batik, then with the sales data this variant is forecasted by using the Linear Exponential Smoothing method and obtained a MAPE of 9.21%. So it can be concluded that the use of the Linear Exponential Smoothing method in predicting sales of the Sarimbit Arm Long variant Batik is more accurate.

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ANALISIS FORECASTING DEMAND DENGAN METODE LINEAR EXPONENTIAL SMOOTHING (STUDI PADA: PRODUK BATIK FENDY, KLATEN)

Jurnal Ekonomi dan Pendidikan ◽

10.21831/jep.v16i2.33714 ◽

2020 ◽

Vol 16 (2) ◽

pp. 81-89

Author(s):

Nita Kusuma Wardani ◽

Muhammad Roestam Afandi ◽

Lilia Pasca Riani

Keyword(s):

Secondary Data ◽

Descriptive Study ◽

Exponential Smoothing ◽

Smoothing Method ◽

Production Data ◽

Sales Data ◽

Forecasting Methods ◽

Forecasting Method ◽

Quantitative Descriptive ◽

Exponential Smoothing Method

Abstrak: Tujuan dari penelitian ini adalah untuk mengevaluasi tingkat akurasi peramalan permintaan Batik Fendy menggunakan teknik MAPE. Adapun jenis penelitian ini merupakan penelitian deskriptif kuantitatif, menggunakan data sekunder dari penjualan perusahaan Batik Fendy periode bulan November 2018 - Onkoter 2019. Terdapat 5 tahapan dalam analisis data, yaitu 1) mentabulasikan data penjualan dan data produksi batik Fendy, 2) mengevaluasi metode peramalan penjualan yang dilakukan oleh perusahaan Batik Fendy dengan teknik MAPE, 3) memproyeksikan nilai alpha dan beta sebagai dasar peramalan linear exponential smoothing, 4) melakukan peramalan permintaan Batik Fendy dengan metode Linear Exponential Smoothing, dan 5) melakukan evaluasi metode peramalan dengan teknik MAPE. Hasil penelitian ini adalah nilai MAPE dari peramalan permintaan yang dilakukan oeh perusahaan Batik Fendy adalah sebesar 17,5%; angka ini menunjukkan tingkat persentase kesalahan paling tinggi pada varian Batik Sarimbit Lengan Panjang, kemudian dengan data penjualan varian ini dilakukan peramalan penjualan dengan metode Linear Exponential Smoothing dan diperoleh MAPE sebesar 9,21%. Sehingga dapat disimpulkan bahwa penggunaan metode Linear Exponential Smoothing dalam memprediksi penjualan Batik Fendy varian Sarimbit Lengan Panjang lebih akurat.Abstract: The purpose of this study was to evaluate the accuracy of forecasting demand for Batik Fendy using MAPE techniques. The type of this research is a quantitative descriptive study, using secondary data from the sales of the company Batik Fendy in the period November 2018 - October 2019. There are five stages in data analysis, namely 1) tabulating sales data and production data of Fendy batik, 2) evaluating sales forecasting methods conducted by the Batik Fendy company with the MAPE technique, 3) projecting alpha and beta values as the basis for forecasting linear, exponential smoothing, 4) forecasting requests for Batik Fendy with the Linear Exponential Smoothing method, and 5) evaluating the forecasting method with the MAPE technique. The results of this study are the MAPE value of demand forecasts made by the Batik Fendy company is 17.5%. This figure shows the highest percentage of error in the variant of the Sarimbit Long Sleeve Batik. With the sales data, this variant is forecasted by using the Linear Exponential Smoothing method and obtained a MAPE of 9.21%. So it can be concluded that the use of the Linear Exponential Smoothing method in predicting sales of the Sarimbit Arm Long Variant Batik is accurate.

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APPLICATION OF THE HOLT-WINTERS EXPONENTIAL SMOOTHING METHOD ON THE AIR POLLUTION STANDARD INDEX IN SURABAYA

Jurnal Biometrika dan Kependudukan ◽

10.20473/jbk.v10i1.2021.53-60 ◽

2021 ◽

Vol 10 (1) ◽

pp. 53

Author(s):

Silmi Muna ◽

Kuntoro Kuntoro

Keyword(s):

Air Pollution ◽

Secondary Data ◽

Exponential Smoothing ◽

Smoothing Method ◽

City Environment ◽

The People ◽

The Government ◽

Environment Agency ◽

Policies And Programs ◽

Exponential Smoothing Method

The Air Pollution Standards Index (APSI) is an indicator that shows how clean or polluted the air is in a city. It also portrays the health impacts towards the people who breathe it in. Based on the Indonesian Ministry of Environment monitoring through the Air Quality Monitoring Station (AQMS), the city of Surabaya only had 22 up to 62 days of air categorized as good in a year. The purpose of this study was to forecast APSI as a scientific-based reference for making decisions and policies that were appropriate in tackling the effects of air pollution on health. This study was non-obstructive or non-reactive research. The research method used was time series to identify the time relationship. The data used were secondary data taken from the APSI documents from 2014 to 2019 at the Surabaya City Environment Agency. The results of this study obtained the best model through α (0.8), γ (0.5), and δ (0.6) with the values of MAPE (0.104355), MAD (0.00842), and MSD (0.001050) calculated with the Holt-Winters exponential smoothing method. The highest produced forecast value of APSI was in September 2020, and the smallest was in January 2020. This study suggests the government of Surabaya to create policies and programs to suppress the number within APSI.

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Komparasi Metode Peramalan Jumlah Permintaan Kamar Hotel

Jurnal Ilmiah Informatika Global ◽

10.36982/jig.v9i2.563 ◽

2019 ◽

Vol 9 (2) ◽

Author(s):

Rendra Gustriansyah ◽

Wilza Nadia ◽

Mitha Sofiana

Keyword(s):

Moving Average ◽

Exponential Smoothing ◽

Standard Type ◽

The Public ◽

Forecasting Methods ◽

Forecasting Method ◽

Double Exponential ◽

Double Exponential Smoothing ◽

Exponential Smoothing Method ◽

Single Exponential

AbstractHotel is a type of accommodation that uses most or all of the buildings to provide lodging, dining and drinking services, and other services for the public, which are managed commercially so that each hotel will strive to optimize its functions in order to obtain maximum profits. One such effort is to have the ability to forecast the number of requests for hotel rooms in the coming period. Therefore, this study aims to forecast the number of requests for hotel rooms in the future by using five forecasting methods, namely linear regression, single moving average, double moving average, single exponential smoothing, and double exponential smoothing, as well as to compare forecasting results with these five methods so that the best forecasting method is obtained. The data used in this study is data on the number of requests for standard type rooms from January to November in 2018, which were obtained from the Bestskip hotel in Palembang. The results showed that the single exponential smoothing method was the best forecasting method for data patterns as in this study because it produced the smallest MAPE value of 41.2%.Keywords: forecasting, linier regression, moving average, exponential smoothing.AbstrakHotel merupakan jenis akomodasi yang mempergunakan sebagian besar atau seluruh bangunan untuk menyediakan jasa penginapan, makan dan minum serta jasa lainnya bagi umum, yang dikelola secara komersial, sehingga setiap hotel akan berupaya untuk mengoptimalkan fungsinya agar memperoleh keuntungan maksimum. Salah satu upaya tersebut adalah memiliki kemampuan untuk meramalkan jumlah permintaan terhadap kamar hotel pada periode mendatang. Oleh karena itu, penelitian ini bertujuan untuk meramalkan jumlah permintaan terhadap kamar hotel di masa mendatang dengan menggunakan lima metode peramalan, yaitu regresi linier, single moving average, double moving average, single exponential smoothing, dan double exponential smoothing, serta untuk mengetahui perbandingan hasil peramalan dengan kelima metode tersebut sehingga diperoleh metode peramalan terbaik. Adapun data yang digunakan dalam penelitian ini merupakan data jumlah permintaan kamar tipe standar dari bulan Januari hingga November tahun 2018, yang diperoleh dari hotel Bestskip Palembang. Hasil penelitian menunjukkan bahwa metode single exponential smoothing merupakan metode peramalan terbaik untuk pola data seperti pada penelitian ini karena menghasilkan nilai MAPE paling kecil sebesar 41.2%.Kata kunci: peramalan, regeresi linier, moving average, exponential smoothing.

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Advising an Appropriate Forecasting Method for a Snacks Item (Biscuit) Manufacture Company in Bangladesh

Dhaka University Journal of Science ◽

10.3329/dujs.v66i1.54545 ◽

2018 ◽

Vol 66 (1) ◽

pp. 55-58

Author(s):

Nandita Barman ◽

M Babul Hasan ◽

Md Nayan Dhali

Keyword(s):

Time Series ◽

Inventory Management ◽

Linear Trend ◽

Exponential Smoothing ◽

Demand And Supply ◽

Forecasting Methods ◽

Forecasting Method ◽

Short Term Forecasting ◽

Exponential Smoothing Method ◽

Manufacture Company

In this paper, we study the most appropriate short-term forecasting methods for the newly launched biscuit factory produces different types of biscuits. One of them is nut-orange twisted biscuits. As it is a newly launched biscuit factory, it does not use any scientific method to find future demand of their products to produce for the purpose of sales. Having an error free production as well as a good inventory management we try to find an appropriate forecasting method for the sets of data we analyzed for that specific production. Several forecasting methods of time series forecasting such as the Moving Averages, Linear Regression with Time, Exponential Smoothing, Holt‘s Method, Holt-Winter‘s Method etc. can be applied to estimate the demand and supply for these companies. This paper focuses on selecting an appropriate forecasting technique for the newly launched biscuit company. For this, we analyze Exponential Smoothing method as used to time series. We observe from the empirical results of the analysis that if the data has no trend as well as seasonality, Exponential Smoothing Forecasting Method processes as the most appropriate forecasting method for the factory. If the data experiences linear trend in it then Holt’s Forecasting Method processes as the most appropriate forecasting method for the sets of data we analyzed. Dhaka Univ. J. Sci. 66(1): 55-58, 2018 (January)

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Forecasting Shoe Sales Volume with Exponential Smoothing Method at PT Kujang Utama Antasena - Bogor

JUDICIOUS ◽

10.37010/jdc.v2i2.463 ◽

2021 ◽

Vol 2 (2) ◽

pp. 134-137

Author(s):

Siti Juriah

Keyword(s):

Moving Average ◽

Control Process ◽

Exponential Smoothing ◽

Smoothing Method ◽

Smoothing Factor ◽

Planning And Control ◽

Shoe Industry ◽

Forecasting Method ◽

Exponential Smoothing Method ◽

And Control

PT Kujang Utama Antasena is a shoe industry company specifically for security. The purpose of this study is to forecast or predict sales. This study uses a quantitative method with exponential smoothing, smoothing factor/constant (?) of 0.2. In production activities, forecasting is carried out to determine the amount of demand for a product and is the first step of the production planning and control process to reduce uncertainty so that an estimate that is close to the actual situation is obtained. The exponential smoothing method is a moving average forecasting method that gives exponential or graded weights to the latest data so that the latest data will get a greater weight. In other words, the newer or more current the data, the greater the weight.

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IMPLEMENTASI METODE EXPONENTIAL SMOOTHING UNTUK PERAMALAN KEDATANGAN WISATAWAN MANCANEGARA PADA PULAU BALI

Media Mahardhika ◽

10.29062/mahardika.v18i2.145 ◽

2020 ◽

Vol 18 (2) ◽

pp. 171

Author(s):

Nafis Sulthan ◽

Nurafni Eltivia ◽

Nur Indah Riwajanti

Keyword(s):

Decision Making ◽

Error Rate ◽

Exponential Smoothing ◽

Smoothing Method ◽

Seasonal Patterns ◽

Microsoft Excel ◽

Foreign Tourist ◽

Tourism Sector ◽

Quantitative Descriptive ◽

Exponential Smoothing Method

The purpose of this study is to predict the arrival of foreign tourists on the island of Bali by using the Exponential Smoothing method. This research is a quantitative descriptive. The data used in the study are data on foreign tourist arrivals from the air and sea routes taken from the Central Statistics Agency. Data is managed through the Microsoft Excel application. In determining the RMSE, the Solver Parameters help listed in Microsoft Excel is used to determine the lowest error rate. The data used in this study indicate that there are trend and seasonal patterns so that the most suitable Exponential Smoothing method is the Triple Exponential Smoothing method. The results of this study indicate that foreign tourist arrivals on the island of Bali are predicted to increase in 2020 although not too significant. The results of this study are expected to help the Bali Island government and related agencies in terms of planning and decision making to overcome the crisis on the island of Bali caused by the tourism sector.

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PERBANDINGAN METODE SINGLE EXPONENTIAL SMOOTHING DAN MOVING AVERAGE PADA PERAMALAN PENJUALAN PRODUK MINYAK GORENG DI PT TUNAS BARU LAMPUNG

JURNAL REKAYASA SISTEM INDUSTRI ◽

10.33884/jrsi.v6i2.3694 ◽

2021 ◽

Vol 6 (2) ◽

pp. 101

Author(s):

Niken Chaerunnisa ◽

Ade Momon

Keyword(s):

Moving Average ◽

Exponential Smoothing ◽

Smoothing Method ◽

Oil Products ◽

Cooking Oil ◽

Forecasting Method ◽

Double Exponential Smoothing ◽

Product Sales ◽

Exponential Smoothing Method ◽

Single Exponential

PT Tunas Baru Lampung is a company that produces palm cooking oil products under the Rose Brand brand. In product sales, companies sometimes experience ups and downs. Based on the sales data from Rose Brand Cooking Oil, the size of 1 L has fluctuated or in each period it changes and is not always boarding. Even though product sales are one of the important things to be evaluated from time to time on an ongoing basis. To predict future sales, forecasting is done. The forecasting method used is Double Exponential Smoothing and Moving Average. The method of accuracy will be compared using MSE, MAD, and MAPE. The results showed a comparison of the accuracy and the smallest error value in each method. By using the weight values 0.1, 0.3, 0.4, 0.5, 0.6, 0.7, and 0.8 on the Single Exponential Smoothing method the weight value is 0.8 or α = 0.8, namely MSE of 250,570,764.80, MAD of 12,922.32 and MAPE of 33.55 Then, using the movement value n = 3 in the Moving Average method has an accuracy of 438,980,942.75 MSE, 18,142.14 MAD, and 41.37 MAPE. After comparing the accuracy of the two methods, the Single Exponential Smoothing method is the best method to predict sales of Rose Brand 1 L Cooking Oil products.

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FORECASTING USES DOUBLE EXPONENTIAL SMOOTHING METHOD AND FORECASTING VERIFICATION USES TRACKING SIGNAL CONTROL CHART (CASE STUDY: IHK DATA OF EAST KALIMANTAN PROVINCE)

BAREKENG JURNAL ILMU MATEMATIKA DAN TERAPAN ◽

10.30598/barekengvol14iss1pp013-022 ◽

2020 ◽

Vol 14 (1) ◽

pp. 013-022

Author(s):

Humairo Dyah Puji Habsari ◽

Ika Purnamasari ◽

Desi Yuniarti

Keyword(s):

Control Chart ◽

Exponential Smoothing ◽

Smoothing Method ◽

Signal Control ◽

East Kalimantan ◽

Forecasting Method ◽

Double Exponential ◽

Double Exponential Smoothing ◽

Exponential Smoothing Method ◽

Tracking Signal

Abstrak Peramalan merupakan suatu teknik untuk memperkirakan suatu nilai pada masa yang akan datang dengan memperhatikan data masa lalu maupun data saat ini. Data yang menunjukan suatu trend, cocok dengan metode peramalan double exponential smoothing dari Brown atau metode double exponential smoothing dari Holt. Peramalan metode double exponential smoothing pada penelitian ini diaplikasikan pada data IHK Provinsi Kalimantan Timur periode Bulan Januari Tahun 2016 hingga Bulan Februari Tahun 2019 yang berpola trend. Tujuan dari penelitian ini adalah memperoleh hasil perbandingan akurasi metode peramalan double exponential smoothing berdasarkan nilai MAPE terkecil, memperoleh hasil verifikasi metode peramalan double exponential smoothing terbaik berdasarkan grafik pengendali tracking signal, dan memperoleh hasil peramalan menggunakan metode double exponential smoothing terbaik. Hasil penelitian menunjukkan metode peramalan terbaik adalah metode double exponential smoothing dari Holt dengan parameter dan berdasarkan nilai MAPE terkecil sebesar 0,361% dan nilai tracking signal yang keseluruhan terkendali pada grafik pengendali tracking signal. Kata kunci: Double Exponential Smoothing, IHK, MAPE, Tracking signal. Abstract Forecasting is a technique for estimating a value in the future by looking at past and current data. Data that shows a trend, matches the Brown’s exponential smoothing forecasting method or Holt's double exponential smoothing method. Forecasting of double exponential smoothing method in this study was applied to the IHK data of East Kalimantan Province for the period of January 2016 to February of 2019 which has a trend pattern. The purpose of this study was to obtain the results of the accuracy comparison of the double exponential smoothing forecasting method based on the smallest MAPE value, obtain the best verification results of the double exponential smoothing forecasting method based on tracking signal control charts, and obtain the best forecasting results using the double exponential smoothing method. The results showed that the best forecasting method was Holt's double exponential smoothing method with parameters and based on the smallest MAPE value of 0.361% and the overall tracking signal value was controlled on the tracking signal control chart. Keywords: Double Exponential Smoothing , IHK, MAPE, Tracking signal.

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GOOD SALES FORECASTING INFORMATION SYSTEM USING SINGLE EXPONENTIAL SMOOTHING METHOD

Jurnal Teknologi Informasi dan Pendidikan ◽

10.24036/tip.v14i1.453 ◽

2021 ◽

Vol 14 (1) ◽

pp. 77-82

Author(s):

Rahmadini Darwas ◽

Rahimullaily Rahimullaily ◽

Naufal Abdi

Keyword(s):

Information System ◽

Exponential Smoothing ◽

Mean Value ◽

Smoothing Method ◽

Sales Data ◽

Cooking Oil ◽

The Mean ◽

Exponential Smoothing Method ◽

Single Exponential

This study aims to determine the estimated number of items sold at one of the mini market, namely the Tita shop, especially Sari Murni cooking oil, 2 liter packs for the next one month based on sales data for January 2016 to December 2017. The problems that occur at Tita`s shop are is difficult to estimate the amount of stock of goods and calculate the estimated cost required for sales in the next month period, so it is necessary to build a forecasting information system using the single exponential smoothing method which assumes that the data fluctuates around the mean value without any trend or seasonal elements. This study resulted in the amount of 2 liter packaged sari murni cooking oil in Januari 2018, which was 42 pcs. Meanwhile, the estimated cost required to buy 2 liter packaged cooking oil stock in that period is Rp. 609.000,00 with a capital price unit of goods Rp. 14.500,00.

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