Single Exponential Smoothing Research Articles

Aplikasi Prediksi Penjualan Kopi dengan Metode Single Exponential Smoothing untuk Mengetahui Produk Kopi Terlaris

Indonesia is the fourth-largest coffee exporter in the world. for the Indonesian state, it only focuses on quantity, in fact, the quality of Indonesian coffee has been recognized by various parties in various countries. The majority of the Indonesian population likes coffee, especially the people of Lampung, but only with certain types of coffee such as Arabica, Robusta, etc. Therefore, coffee sellers must provide a stock of various types of coffee products. In the supply of coffee products with various types, it can cause difficulties in providing coffee products and effective storage so that the existing products are preserved and the quality of taste is maintained. The provision of coffee products is carried out using estimates without any definite calculations and also the seller has not been able to predict the coffee products that are often in demand by consumers. Based on the problems experienced, we need a method that is able to predict the coffee products that will be sold a lot, so that there is no accumulation of product stock. One method that can help solve this problem is to use the single exponential smoothing method. Single exponential smoothing is one of the existing methods in the forecasting (prediction) system. By looking at the problems and methods that have been determined to solve the problems, this research will build a forecasting application (prediction). This system can help sellers in predicting coffee product sales. So that sellers can find out which coffee products will be selling well and can prepare the amount of coffee availability that customers are interested in.

The Importance of Ambarlı Port in Container Handling in Turkey; Forecasting Analysis for the Near Future and the Impact of COVID 19 Pandemic

The global economic crisis that increased due to the COVID-19 Pandemic which deeply affects maritime trade, is expected to grow. According to the United Nations Conference on Trade and Development, the COVID-19 pandemic caused a 4.1 percent decline in maritime trade in 2020. While the COVID-19 Pandemic caused increases in flight cancellations in all cargo groups on a global scale, it brought along falling container demands. The negative effects in container transportation were also reflected in the container ports, and there was a decrease in the amount of cargo handling at the container ports. With the impact of COVID 19, the financial problems of container global freight operators have increased. Despite the decrease in all TEUs handled in Turkey, Ambarlı has been the largest port in terms of TEUs handled in Turkey for the last 10 consecutive years. Therefore, in this study, Ambarlı Port has been chosen for analysis in examining the impact of the COVID-19 Pandemic. The Single Exponential Smoothing Method has been used as the analysis method in the study. In this article, due to the importance of container transportation, a future estimation analysis has been carried out in terms of container transportation of Ambarlı port, which has an important place in container handling in Turkey in a constantly changing competitive environment. As a result, there will not be any significant change in TEUs handled by Ambarlı Port in next years.

Demand Forecasting of The Automobile Sales Using Least Square, Single Exponential Smoothing and Double Exponential Smoothing

Sales strategies require the right managerial in marketing products with the development of technology and communication, the decision making in product sales supported by complete data and can be analyzed into intelligence business solutions. The research discussed and provided solutions about how to forecast future demand targets from a set of data history by making a predictive model of product demand in the real case. The research study was obtained from automobile sales, which the company probably set the strategy from the forecast result of automobile sales by the system in the future. The research used forecasting methods such as Least Square, Single Exponential Smoothing, and Double Exponential Smoothing to achieve a small percentage of prediction error. The dataset was collected from Mitsubishi Motors Corporation which obtained 60 samples of popular product types such as Pajero, FE and L300 from 2014 to 2018 over a period of months. The experimental results reported that Double Exponential Smoothing has given a better performance than other methods. The forecasting result of Pajero reached the MAPE of 3.26%, FE reached the MAPE of 3.24%, and L300 reached the MAPE of 3.37%. This study indicates that the selection of the forecasting method depends on the actual data pattern and the adjustment of the parameters in predicting future points.

ANALISA BULLWHIP EFFECT PENGADAAN STOCK FRANCHISE AYAM SABANA

The COVID-19 pandemic made some companies must pay attention to all their business needs appropriately and measurably.Strategic use in some companies there are maing the limited on the stock supply, it is expected to reduce the losses that will be experienced by business people. The Sabana franchise has problems in determining the optimal safety stock so that there is no bullwhip effect in the supply chain for these raw materials. The losses caused by the bullwhip effect are not only on product availability but also on storage costs in manufacturers or retailers. The purpose of this study is to determine the value, causes, and proposed solutions to reduce the occurrence of the Bullwhip Effect and can optimally forecast inventory requirements using the Single Exponential Smoothing method.

PERAMALAN GARIS KEMISKINAN DI KABUPATEN PURBALINGGA TAHUN 2021-2023 DENGAN METODE DOUBLE EXPONENTIAL SMOOTHING LINIER SATU PARAMETER DARI BROWN

Poverty is a major problem in a country. The Indonesian government has made various efforts to tackle the problem of poverty. The main problem faced in poverty alleviation is the large number of people living below the poverty line. Therefore, this study aims to predict the poverty line in Purbalingga Regency for the next three periods as one of the efforts that can be made by the government in poverty alleviation. The method used in this study is a one-parameter linear double exponential smoothing from Brown. The software used in this research is Zaitun Time Series and Microsoft Excel. The steps taken are determining the forecasting objectives, plotting time series data, determining the appropriate method, determining the optimum parameter value, calculating the single exponential smoothing value, calculating double exponential smoothing value, calculate the smoothing constant value, calculate the trend coefficient value and perform forecasting. Based on the calculation results, the optimum alpha parameter value is 0.7 with MAPE value of 1.67866%, which means that this forecasting model has a very good performance. The forecast value of the poverty line in Purbalingga Regency for 2021 is Rp. 396,516, in 2022 it is Rp. 417,818, and in 2023 it is Rp. 439,120.

Negative Binomial Time Series Regression – Random Forest Ensemble in Intermittent Data

Intermittent dataset is a unique data that will be challenging to forecast. Because the data is containing a lot of zeros. The kind of intermittent data can be sales data and rainfall data. Because both sometimes no data recorded in a certain period. In this research, the model is created to overcome the problem. The approach that is used in this research is the ensemble method. Mostly the intermittent data comes from the Negative Binomial because the variance is over the mean. We use two datasets, which are rainfall and sales data. So, our approach is creating the base model from the time series regression with Negative Binomial based, and then we augmented the base model with a tree-based model which is random forest. Furthermore, we compare the result with the benchmark method which is The Croston method and Single Exponential Smoothing (SES). As the result, our approach can overcome the benchmark based on metric value by 1.79 and 7.18.

Implementasi Metode Penghalusan Ekponensial Tunggal Dalam Prediksi Penjualan Buku

The prediction of the quantity of product sales in the future is intended to control the amount of existing product stock, so that product shortages or excess stock can be minimized. When the quantity of sales can be predicted accurately, the fulfillment of consumer demand can be sought on time and the cooperation of the store with the relationship is maintained well so that the store can avoid losing both sales and consumers. The purpose of this study is to compare the effectiveness of the use of the Single Exponential Smoothing method and methods Double Exponential Smoothing with a smoothing parameter value a = 0.5 for forecasting sales by comparing the error values in the two methods using the Mean Squared Error (MSE) method, the MSE results of the Single Exponential Smoothing method is 4967.75 while the MSE Double Exponential Smoothing is 5113.03. Thus, the Single Exponential Smoothing method is more accurate than Double Exponential Smoothing in calculating book sales forecasting because it has a low MSE value.

Trend Analysis and Short-Term Forecasting of Goat and Sheep Populations and their Meat Production in Sri Lanka using Single and Double Exponential Smoothing Models

Trend Analysis and Short-Term Forecasting of Goat and Sheep Populations and their Meat Production in Sri Lanka using Single and Double Exponential Smoothing Models

PERBANDINGAN MODEL SIR (SUSCEPTIBLE, INFECTIOUS, RECOVERED), EXPONENTIAL MOVING AVERAGE DAN SINGLE EXPONENTIAL SMOOTHING PADA PERAMALAN COVID-19

Novel Coronavirus 2019 (COVID-19) is a disease caused by SARS-CoV-2, COVID-19 is a new type of coronavirus that can be transmitted from human to human. This virus can cause pneumonia, which is inflammation of the lung tissue that causes impaired oxygen exchange, resulting in shortness of breath. Currently it is not known when the Covid-19 pandemic will end, therefore a forecast is needed to predict the spread of Covid-19. This forecasting uses the SIR (Susceptible, Infectious, Recovered), Exponential Moving Average and Single Exponential Smoothing algorithm. Of the three algorithms, which data will be most suitable for forecasting the spread of covid-19 in Indonesia will be compared. The conclusion of the SIR model test results with the PSBB variable inhibits the spread of the virus, the exponential moving average test gets an error value of 24.28% and exponential smoothing gets an error value of 40.07%. So the suitable algorithms used for covid-19 data are the sir model and the exponential moving average.

GOOD SALES FORECASTING INFORMATION SYSTEM USING SINGLE EXPONENTIAL SMOOTHING METHOD

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.

Peramalan Penjualan Barang Single Variant Menggunakan Metode ARIMA, Trend Analysis dan Single Exponential Smoothing


 
 
 The availability of goods in a store is very important. Forecasting is a tool that is used to help predict data needed by an organization or company. The purpose of this study is to predict the sale of a product that has a high risk of damage and fast expiration time by using existing techniques in forecasting. Forecasting can also be used to make product stock safety at the XYZ Supermarket. The results of this study are in the form of forecasting the sale of a product in a store by using the existing methods of forecasting that are adjusted to the sales data of one product. The method used in forecasting is the ARIMA method, Trend Analysis, and Single Exponential Smoothing. Trend Analysis Method has the highest accuracy with MAPE 9.91%, which means that forecasting is very good, compared to ARIMA with MAPE 37.21% and Single Exponential Smoothing with MAPE 10%. So that the results of the Trend Analysis forecasting will be used for the decision-making process about forecasting stockpiles and stock safety in the future.
 
 

Centralized - Decentralized Demand Management And The Bullwhip Effect

Information distortion tends to inflate from downstream to upstream, and results in demand planning errors and this is followed by inaccurate forecasting. This situation is referred to as the bullwhip effect phenomenon. Demand management is also determined based on either a centralized or decentralized distribution strategy. Both strategies will influence the accuracy of demand planning and its effect on the bullwhip phenomenon. Unfortunately, research investigating the relationship between centralization and decentralization strategies for demand and the bullwhip effect is still limited. To answer this shortcoming, this paper has two scenarios for its analysis. First, forecasting is done to determine the accuracy of demand planning, indicated by the smallest forecast error value. Based on the results of the analysis, it is known that single exponential smoothing with alpha 0.5 is the best forecasting method. The second step is to measure the bullwhip effect; the results show that the coefficient is less than one. This coefficient indicated that the company underperformed in the fulfillment of its customers’ needs. Theoretically, this paper extends the literature on demand management in the supply chain by considering centralized and decentralized strategies.

Forecasting Malaysian Ringgit Using Exponential Smoothing Techniques

Forecasting the foreign currency exchange is a challenging task since it is influenced by political, economic and psychological factors. This paper focuses on the forecasting Malaysian Ringgit (MYR) exchange rate against the United States Dollar (USD) using Exponential Smoothing Techniques which are Single Exponential Smoothing, Double Exponential Smoothing, and Holt’s method. The objectives of this paper are to identify the best Exponential Smoothing Technique that describes MYR for 5 years period and to forecast MYR 12 months ahead by using the best Exponential Smoothing Technique. The comparison between these techniques is also made and the best one will be selected to forecast the MYR exchange rate against USD. The result showed that Holt’s method has the smallest value of error measure which depending on the Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE) for the evaluation part. The MSE is 1.43915x10-14 and MAPE is 2.5413 x 10-6. Meanwhile, the forecast value of MYR in August 2019 is RM 4.30226.

지수평활법과 ARIMA를 이용한 전남 김 위판가격 예측모형 비교

This study conducted a price forecast of major Purple laver production areas, Jeollanam-do, in South Korea. Based on the reasoning that price forecast of Purple laver production regions could assist in controlling supply and demand, through the Exponential smoothing technique(Single exponential smoothing, Holt-Winters Additive and Multiplicative, Exponential smoothing method) and Auto regressive integrated moving average model(ARIMA), Purple laver producer price of Jeollanam-do, the largest wet Purple laver producing area, has been predicted. Moreover, the accuracy and stability of the relevant forecasting model have been reviewed through the Historical simulation method. Concerning Jeollanam-do Purple laver producer price forecast results, among the total models adopted through the utilization of the Historical simulation method, the Simple exponential smoothing with DRIFT forecast model, which uses a DRIFT forecast, was ultimately selected after going through stationary Time series and forecast model accuracy tests.

Dissipativity-based filter design for Markov jump systems with packet loss compensation

This paper is concerned with the dissipativity-based filtering problem for a class of discrete-time Markov jump systems with mode-dependent time-varying delays and random packet losses. The phenomenon of packet losses occurs between the plant and the filter, which is characterized by introducing a random variable. Based on the single exponential smoothing method, the prediction of the missing measurement is used as the packet loss compensation when a packet is lost. Then, a partially mode-dependent filter is established in view of the fact that the mode signals of the original system may not be completely accessible to the filter. By employing a mode-dependent Lyapunov-Krasovskii functional, some novel sufficient conditions are obtained and the filter parameters are derived to ensure that the filtering error system is stochastically stable with a strictly (U,V,W)-γ-dissipative performance. Finally, a numerical example is utilized to demonstrate the effectiveness of the proposed approach.

Penerapan Metode Penghalusan Eksponensial Tunggal pada Prediksi Penjualan Air Minum dalam Kemasan

The Bottled Drinking Water Industry has grown rapidly to various provinces and cities throughout Indonesia now. The development of this industry is caused by high demand from consumers and the lower quality of raw water in springs and wells. PT. Cahaya Bumi Intanpari (CBI) is a company that produces gallons, bottles, and glasses bottled drinking water under the brand “AirMu”. Sales of bottled water every month always fluctuate, the management of PT. Cahaya Bumi Intanpari requires an estimate of the amount of production of each type of bottled water to market demand in the future. Based on the description of the background, in this study, a desktop-based application was designed and built. The research method used includes data collection and data analysis. Data collection includes observation, interviews, and literature study. While data analysis includes making data flow diagrams. The forecasting model is used in forecasting the sales of bottled drinking water at PT. Cahaya Bumi Intanpari is a time series or it can be called a time series, and the forecasting method used is single exponential smoothing. From the results of calculation testing, it is proven that the Single Exponential Smoothing method can be implemented in the AMDK sales forecasting system. Based on the validity test, it is found that the prediction test results of bottled drinking water sales below 20% are included in the good criteria.

Forecasting intermittent demand for inventory management by retailers: A new approach

The forecasting of intermittent demand is a complex task owing to demand fluctuations and interval uncertainty. Intermittent demand is essentially random demand with a high percentage of zero values. In the retail industry, there are many products which face intermittent demand and this poses a problem of inventory management. This study proposes a Markov-combined method (MCM) for forecasting intermittent demand, which takes into account the inventory status and historical sales of products. We divide the prediction process into two stages. In the first stage, the transition probabilities of the four basic states of demand and inventory are calculated. In the second stage, the corresponding and appropriate prediction method is selected according to the predicted state. Further, using two large datasets from the two biggest e-commerce companies in China, we verify our results and show that the MCM forecasts more accurately than the Single Exponential Smoothing (SES), Syntetos-Boylan Approximation (SBA), and Croston (CR) methods. The MCM can be as an alternative method for forecasting intermittent demand because it is easy to compute and typically more accurate than the classical forecasting methods.

Forecasting With Time Series Method at PT. RSM in Bekasi Jawa Barat

This study aims to determine the most valid forecasting method based on the time series method. This research uses a quantitative descriptive method, the research variable is sales data of MT products belonging to PT. RSM period August 2018 to January 2021. Data processing using Microsoft excel and Minitab 19 software. ABC analysis results show product codes RSM020, RSM021, and RSM017 occupy the three highest ranks in class A by contributing 26.16% sales figures. Based on the forecasting results using various time series methods (linear trend, decomposition, moving average, single exponential smoothing, Holt Method, and Winter Method) it is found that the Winter Method produces the lowest MAPE value, which is below 20%. Product code RSM020 with an alpha value of 0.06; beta 0.09; and 0.07 gamma produces 17.2% MAPE. Product code RSM021 with an alpha value of 0.01; beta 0.01; and 0.01 gamma produces a 15.3% MAPE. Product code RSM017 with an alpha value of 0.01; beta 0.02; and 0.02 gamma produces 18.1% MAPE.

DETERMINATION OF THE BEST FORECASTING METHOD FROM MOVING AVERAGE, EXPONENTIAL SMOOTHING, LINEAR REGRESSION, CYCLIC, QUADRATIC, DECOMPOSITION AND ARTIFICIAL NEURAL NETWORK AT PACKAGING COMPANY

PT. Peace Industrial Packaging is a company that produces Styrofoam and plastic bottles that are used as containers or places or commonly referred to as packaging used by other companies to place their finished products. After getting data on the number of requests and production results obtained every month then the data will be processed using several forecasting methods such as Single Moving Average; Double Moving Average; Weight Moving Average; Single Exponential Smoothing; Double Exponential Smoothing; Linear Regression; Quadratic Method, Method Cyclic; Decomposition Method; and Artificial Neural Network (ANN) Method. After conducting the research calculation, the following conclusions can be drawn. The right forecasting method used for the HBL 100 ML product is the ANN (Artificial Neural Network) method because it has the smallest error value, namely the MAD error method of 760.583554, the MSE error method of 863,032.834043, the SDE error method of 970.304264, the MAPE error method is 0.112530, and the MPE error method is 0.112530

Analisis Perbandingan Metode Single Exponential Smoothing dan Single Moving Average dalam Peramalan Pemesanan

ACK Fried Chicken (Ayam Crispy Kriuk) is a local fast food business that provides food and beverages. The competition for chicken franchises in Bali and the number of orders made by customers through online applications, namely the Gofood and Grabfood applications on ACK Fried Chicken, has resulted in the importance of implementing the SCM strategy in ACK Fried Chicken, to maintain customer trust and satisfaction. By providing an overview to make estimates in the present for future production, it is necessary to forecast demand which is carried out using the forecasting method. The forecasting method used is the time series method, namely the Single Exponential Smoothing method and the Single Moving Average method. Forecasting using the time series method, namely SES and SMA, the forecasting model is obtained from the actual data used in this case study, the forecasting model using the SMA method is declared very good because it has a MAPE value <10% while the SES method is declared good because it has a MAPE value. = 11% (10-20%). The SMA method has an error rate that is more sophisticated than the SES method. For forecasting order requests in this study, the SMA method is better used because it has the smallest error rate in forecasting.