Single exponential smoothing method to predict sales multiple products

—Activity to predict sales multiple products intended for control of the number of existing stock, so the lack or excess stock can be minimized. When the number of sales can be accurately predicted, then the fulfilment of consumer demand can be cultivated in a timely and cooperation with suppliers maintained properly so that company can avoid losing sales and customers. This study aims to predict sales multiple products (6,877 products) using Single Exponential Smoothing (SES) approach, which is expected to improve the efficiency of the inventory system. Measurement accuracy of prediction in this study using a standard measurement Mean Absolute Percentage Error (MAPE), which is the most important criteria in analyzing the accuracy of the prediction. The results showed that the average of percentage prediction error of products using SES is high, because MAPE value obtained is 1.056% with a smoothing parameter α = 0.9

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Forecasting of Groundwater Tax Revenue Using Single Exponential Smoothing Method

E3S Web of Conferences ◽

10.1051/e3sconf/201912523006 ◽

2019 ◽

Vol 125 ◽

pp. 23006

Author(s):

Dyna Marisa Khairina ◽

Aqib Muaddam ◽

Septya Maharani ◽

Heliza Rahmania

Keyword(s):

Reference Value ◽

Exponential Smoothing ◽

Smoothing Method ◽

Percentage Error ◽

Tax Revenue ◽

Regional Office ◽

Forecasting Error ◽

The Government ◽

Exponential Smoothing Method ◽

Single Exponential

Setting the target of groundwater tax revenues for the next year is an important thing for Kutai Kartanegara Regional Office of Revenue to maximize the regional income and accelerate regional development. Process of setting the target of groundwater tax revenue for the next year still using estimation only and not using a mathematical calculation method that can generate target reference value. If the realization of groundwater tax revenue is not approaching the target, the implementation of development in the Government of Kutai Kartanegara can be disrupted. The mathematical method commonly used to predict revenue value is the Single Exponential Smoothing (SES) method, which uses alpha constant value which is randomly selected for the calculation process. Forecasting of groundwater tax revenue for 2018 using groundwater tax revenue data from 2013 to 2017. Single Exponential Smoothing method using alpha constant value consists of 0.1, 0.2, 0.3, 0.4 and 0.5. The forecasting error value of each alpha value is calculated using the Mean Absolute Percentage Error (MAPE) method. The best result is forecasting using alpha value 0.1 with MAPE error value was 45.868 and the best forecasting value of groundwater tax for 2018 is Rp 443.904.600,7192.

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Forecasting production,consumption and the food gap of rice crop in Iraq using Exponential Smoothing method

Muthanna Journal for Agricultural Sciences ◽

10.52113/mjas04/8.2/12 ◽

2021 ◽

Vol 8 (2) ◽

Author(s):

Iman K, Mustafa ◽

Osamah.K. Jbara

Keyword(s):

Exponential Smoothing ◽

Smoothing Method ◽

The Self ◽

Rice Crop ◽

Factors Affecting ◽

Production Ratio ◽

Self Sufficiency ◽

Double Exponential Smoothing ◽

Exponential Smoothing Method ◽

Single Exponential

"The aim of this research is to predict the production, consumption and food gap of the rice crop in Iraq, as well as the economic factors that affect the self-sufficiency ratio and the quantity of imports with the time series (2015-1980). Based on the statistical program (Minitab & SPSS) Is the Exponential Smoothing method for Forecasting the production, consumption, and nutritional gap of the rice crop. Two types of single and double (2016-2025) was the single Exponential Smoothing method for having the lowest MSE value of (11450.4) . As for the consumption of the rice yield for the period (2025-2016), the double Exponential Smoothing method was the most accurate (MSE), which is 87100.7. As for the food gap, the single Exponential Smoothing is the best predictor for the same period in terms of the lowest value (MSE) 84100.1. The self-sufficiency ratio was affected by five factors (cultivated area, Imports, available for consumption, import / production ratio, the dummy variable representing years of blockade), and Factors affecting the quantity of imports (rice production,available for consumption, border prices, the number of the population Al- Muthanna University All rights reserved"

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Comparison between the backpropagation and single exponential smoothing method in sugar production forecasting case

Communications in Mathematical Biology and Neuroscience ◽

10.28919/cmbn/5973 ◽

2021 ◽

Keyword(s):

Exponential Smoothing ◽

Smoothing Method ◽

Sugar Production ◽

Production Forecasting ◽

Exponential Smoothing Method ◽

Single Exponential

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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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Analysis and forecasting of wind velocity in chetumal, quintana roo, using the single exponential smoothing method

Renewable Energy ◽

10.1016/j.renene.2009.10.037 ◽

2010 ◽

Vol 35 (5) ◽

pp. 925-930 ◽

Cited By ~ 46

Author(s):

E. Cadenas ◽

O.A. Jaramillo ◽

W. Rivera

Keyword(s):

Wind Velocity ◽

Exponential Smoothing ◽

Smoothing Method ◽

Quintana Roo ◽

Exponential Smoothing Method ◽

Single Exponential

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Sistem Informasi Peramalan Persediaan Barang Menggunakan Metode SES Dan DES

Indonesian Journal on Software Engineering (IJSE) ◽

10.31294/ijse.v4i1.6228 ◽

2019 ◽

Vol 4 (1) ◽

pp. 1-6

Author(s):

Ratih Yulia Hayuningtyas

Keyword(s):

Medical Equipment ◽

Exponential Smoothing ◽

Smoothing Method ◽

Equal Weight ◽

Mean Square ◽

Double Exponential ◽

Forecasting System ◽

Double Exponential Smoothing ◽

Exponential Smoothing Method ◽

Single Exponential

Abstract: Sales is an activity in selling products that provide information about inventory. Arga Medical is a shop engaged in the sale of medical equipment, many of sales transactions in the Arga Medical will affect the inventory. Problems in the Arga Medical is predicting many of product that must available for the next month. Therefore this research makes inventory information forecasting system using Single Exponential Smoothing and Double Exponential Smoothing method. This inventory forecasting information system will result a inventory forecasting for next month. Single Exponential Smoothing Method gives equal weight to each data while Double Exponential Smoothing method is smoothing twice. The Data used in this research is the sales data during 2016. Both of these methods resulted inventory forecasting in the next month is Januari 2017 of 52 with Single Exponential Smoothing and 60 with Double Exponential Smoothing. Each method has a Mean Square Error value the smallest error value is the best method for forecasting inventory. Keywords: Forecasting, Inventory, Single Exponential Smoothing, Double Exponential Smoothing.

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PREDIKSI JUMLAH PENERIMAAN MAHASISWA BARU DENGAN METODE SINGLE EXPONENTIAL SMOOTHING (STUDI KASUS: AMIK ROYAL KISARAN)

JURTEKSI ◽

10.33330/jurteksi.v5i2.356 ◽

2019 ◽

Vol 5 (2) ◽

pp. 125-132

Author(s):

Wiwin Handoko

Keyword(s):

Mean Square Error ◽

Exponential Smoothing ◽

Smoothing Method ◽

Mean Square ◽

Computer Engineering ◽

Study Program ◽

History Of ◽

Data History ◽

Exponential Smoothing Method ◽

Single Exponential

Abstract: A problem requires a solution to solve it. One of them is by using Prediction (Forcasting). Prediction is used to assess the prediction of conditions in the future. at AMIK Royal Kisaran, when it comes to making lecture schedules often hampered because there is no estimated number of students. The data used in this study is the data history of the last 15 Academic Years, from 2003/2004 to 2017/2018. Then the data is processed with the Single Exponential Smoothing Method. Alpha value 0 <α <1. Single Exponential Smoothing makes a comparison with the alpha value until alpha is found which has the minimum error. To find the value of the error, the MSE (Mean Square Error) method is used. The results of the testing of this method are in the academic year 2018/2019 prediction of the number of students for the Informatics Management Study Program as many as 89 people and for Students for the Computer Engineering Study Program as many as 30 people. The Single Exponential Smoothing method can predict the number of students in the next period. Keywords: Prediction; Number of Students; Single Exponential Smoothing; Alpha Value; MSE Abstrak: Suatu masalah memerlukan sebuah solusi untuk menyelesaikannya. Salah satunya dengan menggunakan Prediksi (Forcasting). Prediksi digunakan untuk menilai prakiraan keadaan dimasa. di AMIK Royal Kisaran, ketika akan membuat jadwal kuliah sering terhambat karena tidak adanya perkiraan jumlah mahasiswa. Data yang digunakan pada penelitian ini adalah histori data 15 Tahun Akademik terakhir, mulai 2003/2004 sampai dengan 2017/2018. Kemudian data diolah dengan Metode Single Exponential Smoothing. Nilai alpha 0<α<1. Single Exponential Smoothing melakukan perbandingan dengan nilai alpha tersebut sampai ditemukan alpha yang memiliki error paling minimum. Untuk mencari nilai Error digunakan Metode MSE (Mean Square Error). Hasil dari pengujian terhadap metode ini adalah pada Tahun akademik 2018/2019 prediksi jumlah Mahasiswa untuk Program Studi Manajemen Informatika sebanyak 89 orang dan untuk Mahasiswa untuk Program Studi Teknik Komputer sebanyak 30 orang. Metode Single Exponential Smoothing dapat membantu prediksi jumlah mahasiwa pada satu periode kedepan Kata kunci: Prediksi; Jumlah Mahasiswa; Single Exponential Smoothing; Nilai Alpha; MSE

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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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