scholarly journals The smoothing methods used in assessing the influence of pollution sources on groundwater quality – a case study of metallurgical landfill in Lipówka (southern Poland)

2016 ◽  
Vol 4 (4) ◽  
pp. 61-67
Author(s):  
Marek Sołtysiak ◽  
Dominika Dąbrowska
Keyword(s):  
Groundwater Quality ◽  
Statistical Methods ◽  
Missing Values ◽  
Moving Average ◽  
Chloride Concentration ◽  
Exponential Smoothing ◽  
Pollution Sources ◽  
Smoothing Method ◽  
Southern Poland ◽  
The Impact

AbstractSeveral methods have been used to determine trends in hydrogeochemical elements. This study is concerned with the ordinary 3-period moving averages, the 3-period moving median and the exponential smoothing method. It has taken into account the chloride concentration in groundwater from the region of the landfill of the Katowice Ironworks (southern Poland). The data from two piezometers were used: T1 screened in the Triassic and Q1 screened in the Quaternary aquifers. The main aim of this article was to compare statistical methods and to choose the most appropriate method for an assessment of the impact of pollution sources on groundwater quality and determining any trends. The choice of chlorides as the analyzed indicator is connected with their chemical conservative character. They are also indicators of negative impact on groundwater of e.g. municipal, metallurgical landfill sites on groundwater. Results showed that the moving median is less sensitive to outliers than the moving average. The running median preserves sharp discontinuities in the signal but the biggest smoothing was observed in the case of exponential smoothing. All of these methods filter out the noise and transform the data into a smooth curve, which is unbiased by outliers. Application of the smoothing method allowed the generalization of the monitoring data which clearly showed trends. In the absence of reliable data for short periods, these statistical methods made it possible to fill in missing values. These methods are easier to calculate than regression models even if they ignore complex relationships in the data. Moreover, they can also be used as a component in many other indicators such as the Moving Average Convergence Divergence.

scholarly journals Pengurangan Efek Bullwhip Pada Vendor Tunggal dan Eceran Multi Di PT. XYZ

2019 ◽  
Vol 2 (4) ◽  
Author(s):  
Meilita Tryana Sembiring ◽  
Feby Sanna Sibarani
Keyword(s):  
Supply Chain ◽  
Moving Average ◽  
Linear Method ◽  
Bullwhip Effect ◽  
Exponential Smoothing ◽  
Glass Bottle ◽  
Forecasting Methods ◽  
Forecasting Method ◽  
The Impact ◽  
A Company

PT. XYZ merupakan perusahaan yang bergerak dalam produksi produk – produk olahan teh. Perusahaan telah memproduksi berbagai varian the yakni bentuk mau pun jenis teh. Objek penelitian ini ialah the dalam kemasan botol kaca dengan ukuran 220 ml. Ukuran the tersebut dipilih berdasarkan akumulasi dari penjualan the tertinggi. Terdapat perbedaan pada prediksi jumlah produksi yang akan dilakukan. Prediksi jumlah produksi dapat dilakukan dengan melakukan peramalan permintaan serta penggunaan metode yang tepat. Rantai pasok yang diteliti pada PT. XYZ terdiri atas Manufaktur (Vendor), Kantor Penjualan, dan Dister. Awalnya peramalan dilakukan pada masing – masing level rantai pasok dengan metode peramalan yang berbeda – beda. Maka, diperlukan penyeragaman metode peramalan pada masing – masing pelaku rantai pasok. Berdasarkan pengujian metode peramalan yang dilakukan yakni metode Linear, Exponential Smoothing, Moving Average, dan Winter’sMethod. Diperoleh bahwa error terkecil terdapat pada metode peramalan Winter’s Method dengan parameter Level sebesar 0,5, Trend sebesar 0,2 dan Seasonal sebesar 0,6. Parameter error yang digunakan ialah MAPE, MAD, dan MSD. Hasil penelitian menunjukkan bahwa penggunaan metode peramalan yang tepat akan mengurangi dampak dari bullwhip effect yang terjadi pada PT. XYZ.   PT. XYZ is a company engaged in the production of processed tea products. The company has produced various variants of tea, that is the shape and type of tea. The object of this research is the 220 ml glass bottle packaging. The size of the tea is chosen based on the accumulation of the highest tea sales. There is a difference in the prediction of the amount of production to be carried out. Prediction of the amount of production can be done by forecasting demand and using appropriate methods. The supply chain studied at PT. XYZ consists of Manufacturing (Vendors), Sales Offices, and Disters. Initially forecasting is done at each level of the supply chain with different forecasting methods. Therefore, uniform forecasting methods are needed for each supply chain actor. Based on testing the forecasting method that is done namely the Linear method, Exponential Smoothing, Moving Average, and Winter’s Method. Obtained that the smallest error is found in the Winter’s Method forecasting method with a Level parameter of 0.5, a Trend of 0.2 and a Seasonal of 0.6. The error parameters used are MAPE, MAD, and MSD. The results showed that the use of appropriate forecasting methods would reduce the impact of the bullwhip effect that occurred at PT. XYZ

scholarly journals The Impact of COVID-19 on Electricity Demand and Generation in Japan

2021 ◽  
Vol 13 (19) ◽  
pp. 10836
Author(s):  
Kelly D’Alessandro ◽  
Andrew Chapman ◽  
Paul Dargusch
Keyword(s):  
Electricity Generation ◽  
Energy Transition ◽  
Exponential Smoothing ◽  
Electricity Demand ◽  
Regional Level ◽  
Smoothing Method ◽  
International Studies ◽  
Domestic Energy ◽  
Exponential Smoothing Method ◽  
The Impact

This research considered changes in monthly electricity generation and demand in Japan during the COVID-19 pandemic. Observed network electricity demand and generation type for the January–June 2020 period were compared to forecast values (using a triple exponential smoothing method) based on trends established from 2016 to 2019. Regional level electricity demand data showed little variation from expected trends for domestic energy users, but lower than expected business and industrial network demand, particularly in the 50–2000 kW cohort. Electricity demand was most likely to deviate from existing trends in May 2020, which is in-line with the voluntary lockdown activities. These results are consistent with observed patterns from other international studies into the impact of COVID-19 on electricity demand. Generation was found to be reduced in May and June of 2020, without significant impacts to the generation makeup, largely due to Japan’s positioning within a broader energy transition context. These findings validate previous studies and add to the broader discussions on drivers and the rationale for electricity demand behaviors between user scales. Previous studies examined the electricity demand reductions of full and partial lockdowns. This analysis adds to this discourse by documenting the impacts of a voluntary lockdown.

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

JUDICIOUS ◽  
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.

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

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.

scholarly journals PENERAPAN METODE EXPONENTIAL SMOOTHING UNTUK PERAMALAN JUMLAH KLAIM DI BPJS KESEHATAN PAMEKASAN

2016 ◽  
Vol 2 (1) ◽  
pp. 46 ◽  
Author(s):  
Faisol Faisol ◽  
Sitti Aisah
Keyword(s):  
Time Series ◽  
Average Method ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Time Series Model ◽  
Smoothing Method ◽  
Repair Procedure ◽  
Double Exponential Smoothing ◽  
Using Data ◽  
Exponential Smoothing Method

Time series model is the model used to predict the future using past data, one example of a time series model is exponential smoothing. Exponential smoothing method is a repair procedure performed continuously at forecasting the most recent data. In this study the exponential smoothing method is applied to predict the number of claims in the health BPJS Pamekasan using data from the period January 2014 to December 2015, the measures used to obtain the output of this research there are four stages, namely 1) the identification of data, 2) Modeling, 3) forecasting, 4) Evaluation of forecasting results with RMSE and MAPE. Based on the research methodology, the result for the period 25 = 833.828, the 26 = 800.256, period 27 = 766.684, a period of 28 = 733.113, period 29 = 699.541, and the period of 30 = 655, 970. Value for RMSE = 98.865 and MAPE = 7.002, In this case the moving average method is also used to compare the results of forecasting with double exponential smoothing method. Forecasting results for the period 25 = 899.208, the 26 = 885, 792, 27 = 872.375 period, a period of 28 = 858.958, period 29 = 845.542, and the period of 30 = 832.125. Value for RMSE = 101.131 and MAPE = 7.756. Both methods together - both have very good performance because the value of MAPE is below 10%, but the method of exponential smoothing has a value of RMSE and MAPE are smaller than the moving average method.

scholarly journals Risk assessment for groundwater in the region of municipal landfill systems in Tychy-Urbanowice (Southern Poland)

2020 ◽  
Vol 8 (1) ◽  
pp. 9-17
Author(s):  
Wojciech Rykała ◽  
Dominika Dąbrowska
Keyword(s):  
Risk Assessment ◽  
Groundwater Quality ◽  
Environmental Risk ◽  
Environmental Risk Assessment ◽  
Swot Analysis ◽  
Climatic Conditions ◽  
Spatial And Temporal Variability ◽  
Southern Poland ◽  
The Impact

AbstractAn Environmental Risk Assessment is an efficient technical and analytical method for analyzing environmental impacts and it supports the decision-making process connected with projects variants by using historical data collection, identification of regional risk sources, probability and impact estimation of signal risk type. In this article, an Environmental Risk Assessment was performed for groundwater quality in the region of municipal landfills in Tychy-Urbanowice (Southern Poland) to assess the impact of various factors on the quality of groundwater in the region. The assessment used qualitative and quantitative risk analysis methods, including cause and consequence analysis, completion of an effect/probability matrix and utilization of the SWOT analysis method. The results of the assessment indicate that use of the SWOT analysis was the best method for groundwater risk assessment in the examined area. The analysis included an assessment of the spatial and temporal variability of leachate and groundwater quality (using data from a groundwater monitoring system), simulation of the longevity of both the top and bottom security system, spatial planning and an assessment of the impact of other parameters on groundwater, terrain and climatic conditions. The overall result for this analysis for the likely potential groundwater hazard was a score of -4. For the purposes of further risk analyses, it is possible to consider additional factors that are likely to affect the water quality of the area under investigation or to use other methods that will be based on a time series analysis.

scholarly journals ANALISA PERAMALAN WINTER’S EXPONENTIAL SMOOTHING BROWN DIBANDINGKAN DENGAN AUTOREGRESSIVE INTEGRATED MOVING AVERAGE PADA DATA JUMLAH KEJADIAN HIPERTENSI

2019 ◽  
Vol 4 (1) ◽  
pp. 1-10
Author(s):  
Wisoedhanie Widi .A ◽  
Nanik Dwi A.
Keyword(s):  
Community Health Centers ◽  
Moving Average ◽  
Secondary Data ◽  
Exponential Smoothing ◽  
Smoothing Method ◽  
Preventive Action ◽  
Autoregressive Integrated Moving Average ◽  
Planning And Control ◽  
Exponential Smoothing Method ◽  
And Control

In an attempt to see and examine the situation and conditions that occur in the future to do forecasting (forecasting). Hypertension is a major disease in the ten Clinics Together and almost every month new hypertension cases occur, so the incidence of hypertension is becoming the trend and forecasting needs to be done. The purpose of this research is to do forecasting on the data the number of incident hypertension in Clinics With the city of Malang with Exponential Smoothing method using winter's Brown compared to Autoregressive Integrated Moving Arima. This type of research is the study of non reactive (non reactive research) which is a type of secondary data for research.Unit samples in this research are patients who come for the medication and patients in Clinics With hypertension Malang. in 2013 to 2016. Research data using Minitab software. The results of this study showed that both methods of forecasting results shows that tend to decrease in the year 2018 with the lowest incidence in December that as many as 58 incidents on Exponential Smoothing method of winter's and some 80 events on the method of Autoregressive Integrated Moving Average. The existence of a trend of decrease in the incidence of hypertension can be supported by the growing health services at community health centers With has been doing various efforts in preventive action, promotif and collaborative in the handling of problems Hypertension.Through these research results, it is advisable to draw up a health center party planning and control and eradication programs work for transmission of diseases of hypertension (P2P) with reference to the results of the forecasting incidence of hypertension in the year 2018.

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