Exponential Smoothing Method Research Articles

Forecasting the Number of Sales of Mie Sedap at PT. Wings Surya Probolinggo Branch With Single Exponential Smoothing Method

PT. Wings Surya branch Probolinggo is a business engaged in trading, especially distributors of foodstuff sales, one of which is instant noodles with the brand “Mie Sedap.” As the competition level becomes more competitive, the company must be able to know the estimated sales forecast for the future. PT has not done this. Wings Surya Branch Probolinggo. So this study aims to assist the company in understanding its sales forecasting in the future. This research uses a single exponential smoothing method because it is based on the analysis of sales data in the company and the data pattern formed according to the criteria of the way to be used. The process of calculating this method is to find a single smoothing value; then, it will get the forecasting value. After going through various stages calculation process done with the amount of sales data from January 2013 to December 2015, the result of this research is forecasting sales of Mie Sedap Goreng 90 gr products in January 2016 as much as 156873 boxes. It is based on the smallest MAPE value, which is 2,22 at alpha 0,9, and the accuracy of the average forecasting with testing data from January to June 2016 then obtained the result of 92,29%. So the effects of sales forecasting received can help companies know the estimated sales and can be used in supporting decision-making appropriately.

4D BIM integrated optimization of construction steel bar procurement plan for limited storage capacity

Purpose Due to its longer length compared to other construction materials and distinctive stacking patterns, obtaining construction steel bars in congested construction sites with limited storage capacity becomes challenging. Lack of storage space in crowded places prompts the need for building steel bar storage choice optimization. Therefore, this study aims to optimize the construction steel bar procurement plan by providing when and how much rebar to order and how to stack different sizes of rebar considering limited storage capacity. Design/methodology/approach A novel approach has been presented in this paper by integrating 4D building information modelling (BIM) and mixed-integer linear programming (MILP). This technique uses BIM to retrieve material quantities, including rebar, during the design phase. Following that, activities are scheduled depending on the duration determined by crew productivity data and material quantity. Then, based on the prior price, the price of each unit of rebar is projected for the duration of construction using the exponential smoothing method. After that, the MILP approach is used to generate an optimal steel bar procurement plan for limited storage space following the scheduled rebar-related operations. Findings The developed strategy minimizes overall procurement costs and ensures the storage of rebar as per standard guidelines. An optimal rebar procurement and storage plan to construct a six-storied RC frame has been presented in this paper as a demonstrative example to show the effectiveness of the proposed method. Originality/value This work partially satisfies a long-sought research need for establishing a comprehensive construction steel bar procurement system, making it a very useful source of information for practitioners and researchers. The proposed method can be used to minimize a key performance limitation that the conventional rebar procurement practice for crowded building sites may experience.

Research On Replenishment Decision of Vegetable Commodities Based on Seasonal Decomposition Exponential Smoothing Method and Objective Programming Model

Vegetables are indispensable in people’s daily lives and represent a high proportion of the goods sold in supermarkets and bring in a large amount of profit. Therefore, the replenishment and pricing decisions on the day of sales are particularly important. In order to make the best replenishment decision, this paper firstly solves the relationship equation between the sales volume of each vegetable category and the cost-plus ratio coefficient based on the least square fitting; secondly, considering the cyclical change of the vegetable sales volume, the seasonal decomposition of the exponential smoothing method is used to predict the demand for each vegetable category in the first week of July; lastly, the profitability optimization function of the superstore is set up: the objective function is the total profit of the superstore, and the decision variables are the daily supply of each vegetable category. The objective function is the total profit of the hypermarket, the decision variables are the daily supply of each vegetable category and the cost-plus ratio coefficient, and the constraints consider the maximum inventory of the hypermarket, the supply is greater than the demand and is less than the maximum value of the historical sales volume. The maximum daily profit and the optimal cost-plus ratio of the hypermarket are finally solved. This study has reference value for vegetable commodity replenishment decision.

Analisis Peramalan Demand Produk RBL dengan Metode Double Exponensial Smoothing, Moving Avarage, dan Regresi Linear di PT Seiwa Indonesia

Based on historical data on the number of product requests and products produced in the January 2021-December 2022 period, there is a considerable discrepancy. Therefore to be able to predict demand in one or several subsequent periods based on past product demand data, the researcher conducts a forecasting analysis using the double exponential smoothing, moving average, and linear regression methods to find out the most accurate forecasting method to use. Based on the calculation results, it can be concluded that the most appropriate Forecasting method is the linear regression method because it has the lowest MSE value of 1,346,936,387. It is hoped that it will be able to assist PT Seiwa Indonesia in providing future stocks of RBL products in more accurate manner so as to reduce losses due to excessive production.

VEHICLE VOLUME FORECASTING SYSTEM ON TOLL ROADS USING DOUBLE MOVING AVERAGE AND DOUBLE EXPONENTIAL SMOOTHING METHODS

Forecasting or forecasting has been used as one of the considerations, especially in the field of business and economics. This is done so that company losses can be minimized and company profits can be maximized. Therefore we need a system that can make it easier to get accurate forecasting results. However, in real cases procurement of IT consultants requires quite expensive costs. So the research provides a solution to design a website-based vehicle volume forecasting system on toll roads conducted at PT. Jasamarga Pandaan Tol. This system is expected to facilitate the Traffic Collection team in projecting the volume of vehicles on toll roads in the future. The method used in forecasting vehicle volume is the Double Moving Average and Double Exponential Smoothing methods. The Double Moving Average method is a time series method where the results of this method are influenced by the order or time period. The Exponential Smoothing method is a time series method where the results of this method are influenced by the alpha parameter. The results of these two methods will be compared based on the accuracy of the data so that the user can easily see the results of forecasting vehicle volume in the future. Mean Absolute Percentage Error (MAPE) is the absolute (absolute) average percentage error. Definition of Mean Absolute Percentage Error is a statistical measurement of the accuracy of estimates (predictions) in forecasting methods. The results of the accuracy of applying the Double Moving Average method give an average MAPE value of 30,124% and the Double Exponential Smoothing method gives an average MAPE value of 5,368%.

Sales and Operation Analysis: A Case Study in Pt. Berkat Popok Bahagia

The purpose of this paper is to help solve one of the current existing issues of PT. Berkat Popok Bahagia which is SKUs being out of stock. It turns out that there are several reasons for this. The first reason is the amount of the stock of each SKU in the system differs from the amount of stock in the warehouse. The second reason is the demand from the distributors is not in sync with the sales and operational planning team, making it when the distributors order products (SKU) from the factory, the product is not available because it might not be in the production process. The third reason is the forecasting method that is currently being used is less suitable. Author compared the forecasting accuracy of each forecasting method in the top 2 SKUs of baby care category from simple moving average method, simple exponential smoothing method and exponential smoothing with trend method. The results of the forecasting accuracy is based on the MAPE value in which lower value indicates better method. The forecasting accuracy measurement result when comparing the 3 methods between the 2 SKUs are respectively 24.3% and 20.24% for simple moving average, 20.28% and 16.19% for simple exponential smoothing and 18.07% and 15.65% for exponential smoothing with trend. The results indicate that exponential smoothing with trend provides the best performance among the three which therefore author recommend this method compared to the current simple moving average method.

WINTERS EXPONENTIAL SMOOTHING UNTUK FORECASTING HARGA SAHAM PT ASTRA INTERNATIONAL TBK

Investing in the stock market is one of the investment options that are in great demand by investors. Investors generally invest because want to gain profits which can be seen from the stock returns. On the other hand, there is a risk of loss if the investor makes the wrong decision. Investors need accurate information, predictions and a sense of security in stock transactions. Investors or shareholders need to know the development of stock prices in the future with forecasts to anticipate large losses. This study aims to predict the share price of PT. Astra International Tbk (ASII.JK) one year ahead using the winters exponential smoothing method. Forecasting results show that ASII's share price for the next year will increase every month. This can provide information on future stock movements for investors as a consideration for investing in these stocks.

Research on Feight Volume Prediction in Sichuan and Chongqing based on Multiple Regression and Exponential Smoothing Method

As the starting point of the national "One Belt One Road" development and the important economic belt of the Yangtze River in the central and western regions of the country, the Sichuan-Chongqing region is of great significance to study the influencing factors of its logistics. Freight volume is an important indicator for measuring the level of logistics development in a country or region. This article will use SPSS software to analyze the freight volume data of Sichuan-Chongqing region from 2005 to 2019, and use the quadratic exponential smoothing method to model. It is predicted that the freight volume of Sichuan-Chongqing region will continue to grow in the next five years, with a growth rate of approximately Is 5%. In 2024, the freight volume in Sichuan and Chongqing will reach 3.6 billion tons.

Forecasting High Speed Diesel Demand in India with Econometric and Machine Learning Methods

According to International Energy Agency (IEA), India is expected to surpass China by 2024 to become the second largest consumer of oil in the world followed by the United States. High-Speed Diesel (HSD) has the biggest share in the total petroleum products consumed in India accounting for around 38% of the total consumption. Considering the volatile global oil market and an oil import dependency ratio of more than 80% during the last four years, the probability of supply disruptions is high in the Indian context. As any uncertainty about the supply of diesel can affect the smooth functioning of the economy and may create inflationary pressures. Accurate forecasting of HSD demand will be essential for appropriate supply management arrangements. Artificial Neural Networks (ANN) with Multi-Layer Perceptron (MLP) and extreme learning machines is used for forecasting diesel demand in this study. Demand forecasting has been carried out using monthly HSD demand data drawn from the “Indiastat” database for the period 1991-2022. Comparison of ANN with traditional forecasting methods of Autoregressive Integrated Moving Average(ARIMA)and Exponential Smoothing has also been undertaken in this study. This study identifies the deep learning technique of ANN with MLP as the best diesel demand forecasting technique.

Prediksi Ekspor Migas Indonesia Dengan Double Exponential Smoothing

In Indonesia, there are several forms of exports, one of which is oil and gas exports. The Double Exponential Smoothing method is a forecasting method that can be used to forecast oil and gas export data. This method uses two smoothing parameters, namely the average smoothing parameter alpha (α), the trend smoothing parameter beta (β). In this study, the population used is data on oil and gas exports in Indonesia. And the sample used is Indonesia's oil and gas export data from January 2020 to October 2021, we get 22 data sourced from the Central Statistics Agency (BPS). The Double Exponential Smoothing method is used because the data to be processed has a trend, both an up and a down trend and gas exports in Indonesia have an up and down pattern. The Double Exponential Smoothing method can also predict the prediction of oil and gas exports in Indonesia in the next months, November 2021 to February 2022. To process the data, the researcher used the Holt Double Exponential Smoothing method .

Application of Holt-Winter Exponential Smoothing Method to Design a Drug Inventory Prediction Application in Private Health Units

This research aims to see how to design a drug supply prediction application using the Holt-Winter Exponential Smoothing method. The collection methods used in the research are as follows: observation, interviews. The prototyping system development method includes several important stages in designing research applications. The first stage is communication. After that, the Quick Plan stage. Construction of prototype stages. The final stage, deployment delivery and feedback, is the phase of system use by users. In addition, the team also conducts regular system maintenance to ensure optimal performance. These stages together form an iterative process for developing research applications using the prototyping method. Based on the research results, it was found that the test results for the drugs amoxicillin, cefixime, paracetamol, and CTM showed that the MAPE value was less than 10% for these four drugs, which means that the predictive value has quite high accuracy. So it can be concluded that the Holt Winter Exponential Smoothing algorithm can be well implemented to predict drug supplies.

A Penerapan Metode Double Moving Average dan Double Exponential Smoothing pada Peramalan Nilai Impor Barang Konsumsi Tahun 2017-2022

The smoothing method is classified into two, namely the average smoothing method and the exponential smoothing method. This study examines the application of the double moving average (DMA) and double exponential smoothing (DES) methods in forecasting a data. This study uses 72 data, namely consumer goods import value data for the period January 2017 to December 2022. The method with the lowest MSE and MAPE values is used to predict the import value of consumer goods. The results obtained show that the brown double exponential smoothing method with parameter α, which is 0.1, is the best method for predicting the import value of consumer goods in 2017-2022 with an MSE value of 60374.46 and a MAPE value of 13.66%.

PENERAPAN METODE SINGLE EXPONENTIAL SMOOTHING UNTUK PREDIKSI PENGGUNAAN JASA KONSULTAN PAJAK

This research was conducted based on the low number of tax employees in helping taxpayers carry out their tax obligations. This results in some taxpayers using the services of tax consultants. Taxpayers expect tax consultants to be able to help and solve tax problems that are being pursued by taxpayers. The purpose of this study is to foresee the use of services in the coming month at the tax consultant's office Rahmat Accountax. The method used in this study is a quantitative method. The data is processed in the form of quantitative data in the form of months, years, and the number of people who use services at the Rahmat Accountax office in January 2020-March 2022. This research is to predict the development of service use with the Single Exponential Smoothing (SES) method. The Single Exponential Smoothing method is a method used in short-term forecasting that is usually only 1 month ahead which assumes that the data fluctuates around a fixed mean value without a trend or consistent growth pattern. From the analysis that has been carried out, the author can conclude that the forecasting of service use at Rahmat Accountax has decreased from the previous month, namely as many as 8 clients to 7 clients with α=0,2

Forecasting with Triple Exponential Smoothing on Data of the Number of Passengers Departing Domestic Flights at Sultan Hasanuddin International Airport

Transportation facilities and means have developed very rapidly and become the basic needs of the community from time to time. This development ultimately requires the availability of capable and adequate transportation facilities and infrastructure in the form of the availability of good facilities. In general, the movement of the number of flight passengers at Sultan Hasanudin Airport tends not to be stationary on average because in certain times the movement forms an uptrend pattern. In addition, the number of flight passengers at Sultan Hasanudin Airport has an irregular pattern with varying increases and decreases. To forecast fluctuations in the number of flight passengers at Sultan Hasanudin Airport in the coming period, it can be done by time series analysis. The Triple Exponential Smoothing method or commonly referred to as Winter Exponential Smoothing is one of the Time Series methods that is suitable for handling seasonal data such as the number of domestic passengers at Sultan Hasanuddin Airport Makassar. The analysis step of the Triple Exponential Smoothing method is model identification, parameter estimation by trial and error, then is the calculation of the initial value of data smoothing, trends, and seasonality with a length of one season L = 12 and the last is to calculate the error value using MAPE and RMSE. The best model is obtained from a combination of parameters α = 0.9; β = 0,1; and γ=0.1 which results in the smallest forecasting error using RMSE with a value of 56,674.56 and MAPE with a value of 56,674.56. Using a forecasting model:
 So the forecasting results are obtained that look close and not too far from the previous year's data so that it can be used as a reference for the management of the aircraft company to make the right decisions and anticipate a surge in the number of passengers.

Prediction of heavy metal ion distribution and Pb and Zn ion concentrations in the tailing pond area.

The pollution caused by tailings ponds has resulted in ecological damage, with soil contamination significantly impacting the daily lives of residents in the vicinity of mining areas and the future development of mining areas. This study assesses the transport status of heavy metal pollution in tailings areas and predicts its impact on future pollution levels. This study focused on lead-zinc tailing ponds, exploring the spatial and chemical distribution characteristics of heavy metals based on the distributions of Pb, Zn, As, Cu, Cr, Cd, Hg, and Ge ions. The concentrations of the major heavy metal ions Pb and Zn in tailings ponds were predicted via the exponential smoothing method. ① The total accumulation of Pb and Zn in the mine tailings ranges from 936.74~1212.61 mg/kg and 1611.85~2191.47 mg/kg, much greater than the total accumulation of the remaining six heavy metals. The total accumulation of associated heavy metal Cu was high, and the lowest total heavy metals were Hg and Ge at only 0.19 mg/kg and 1.05 mg/kg. ② The analyses of soil heavy metal chemical forms reveal that the heavy metals Pb and Zn had the highest exchangeable state content and state ratio and the strongest transport activity in the industrial plaza and village soils. Pb and Zn are the heavy metals with the greatest eco-environmental impacts in the mining area. ③ The predicted results show that the soil concentrations of the heavy metals Pb and Zn around the tailings area in 2026 are 1.335 and 1.191 times the predicted time starting values. The concentrations of the heavy metals Pb and Zn at the starting point of the forecast are already 3.34 and 3.02 times the upper limits of the environmental standard (according to environmental standards for gravelly grey calcium soils). These results have significant implications for heavy metal pollution risk management.

Research on Modeling of Influencing Factors of New Eco-tourism Management Based on Economic Fluctuation Prediction

This paper begins by analyzing the effect of market economic fluctuations on the digital economy's tourism economy. Second, this paper uses a city cluster in the Yangtze River Delta in China as an example, selects the tourism economic data of the region from 2011 to 2021, and forecasts the tourism development potential of the region from 2022 to 2026 using the conventional methods for predicting economic fluctuations. However, conventional models for predicting economic fluctuations, such as ARIMA, have large prediction errors. This paper combines the RBF neural network and exponential smoothing method and compares them to GM(1, 1), ARIMA, and other models. The findings indicate that the tourism economy in this region is spatially distinct and relatively stable. Therefore, the methodology presented in this paper is capable of accurately predicting tourism demand and growth potential.

Analysis of health state before and after fault self-healing based on a high percentage of distributed distribution grid systems

Abstract In this paper, we preprocess the equipment data using by exponential smoothing method, fit the fault information by using the distribution model, and evaluate the fitting degree of the function with the help of the K-S test to derive the equipment fault information function. In accordance with the correlation mapping between the distribution network health index and the existence of faults, the health index of the distribution network equipment in the region before and after the self-healing of faults is comprehensively calculated, and the state assessment of the equipment is carried out. Before and after the fault self-healing of the IEEE33 node system, the system loss is reduced by 58.89%, as evidenced by the results. Before and after the fault self-healing, Condition 1: the line health index ranges from 1.317 to 3.777, and the transformer health index ranges from 2.011 to 4.646. Condition 2: the line health index ranges from 2.284 to 4.059, and the transformer health index ranges from 1.653 to 4.047, respectively. And Condition 3: the health indices all increase by varying from 0.711 to 2.067. Localized equipment degradation has a significant impact on the overall health of the network, with lines affecting the system to a greater extent than transformers. To summarize, the proposed health index state assessment method can effectively determine the health state of distribution networks before and after fault self-healing.

Mathematical forecasting simulation of salinity intrusion in Chao Phraya River

At present, at two water stations along Chao Phraya River, the main river of the central region of Thailand, the salinity in the river exceeds the raw water salinity standard for tap water production that must be lower than 0.25 g/L. During December to May, since 2013, the salinity value exceeded the raw water standard, strongly affecting the raw water management of Metropolitan Waterworks Authority. Surveillance and prediction of salinity level at the river mouth at Klong Lat Pho Water Quality Monitoring Station, downriver from Sam Lae station, in advance, will be beneficial to the Metropolitan Waterworks Authority’s raw water management. The purpose of this study was to forecast the hourly salinity levels at Khlong Lat Pho Water Quality Monitoring Station. The methods used were Exponential Smoothing and Winter’s Linear and Seasonal and Exponential Smoothing (EXPOW). Both forecasting methods showed a very low mean of square error (MSE): 0.1551 when the exponential smoothing method was used with alpha= 0.9995 and 0.4869 when the EXPOW was used with alpha = 0.0618, gamma = 0.9995, and beta = 0. 0001. Nevertheless, the exponential smooth method fell out of favor since, for long-term prediction, its predicted value converged to a constant value, 0.2405, useless for predicting seasonal variation. On the other hand, the EXPOW method, even though less effective in terms of MSE, produced more reliable short and long-term forecasts for salinity at Khlong Lad Pho Water Quality Monitoring Station.

Research on sales and pricing optimization of vegetable category based on ARIMA model and exponential smoothing method

Research on sales and pricing optimization of vegetable category based on ARIMA model and exponential smoothing method

КОМПЬЮТЕРНОЕ МОДЕЛИРОВАНИЕ ДИНАМИКИ ЦЕНЫ РЕАЛИЗАЦИИ ПРОДУКЦИИ НЕФТЕПЕРЕРАБАТЫВАЮЩИХ ЗАВОДОВ

PJSC Tatneft produces highly competitive, environmentally friendly products, including Euro-5 motor gasoline grades AI-92, AI-95, Euro-5 diesel fuel and others. Petroleum products are a commodity, the prices for which in Russia represent an important indicator of socio-economic well-being. Therefore, the study of trends in the dynamics of petroleum product selling prices and the search for approaches to their forecasting is an urgent task. The purpose of this work is to study the dynamics of petroleum product selling prices using computer modelling methods to solve forecasting problems in the context of both current planning and management issues and strategic level tasks. The research was carried out for the following nomenclature of oil products: motor gasoline (AI-92, 95), diesel fuel (DT), heating oil (HO) at the selling price from the refineries of Tatarstan for the period 2018-2023. The article shows the results obtained using Statistica and MS Excel software packages. The choice of models for predicting trends in the dynamics of changes in salling prices is justified, and methods of analytical leveling and exponential smoothing are applied. It is shown that to identify the dynamics of forecast values, it is necessary to use statistics from both a long period and information from the last year. All proposed models provide results with an acceptable error. The results obtained from computer modeling of the pricing dynamics for selling prices of petroleum products are recommended for forecasting trends in the dynamics of price changes as an effective management tool in planning and making management decisions in order to gain competitive advantages in the development of a sustainable business process.