Grey double exponential smoothing model and its application on pig price forecasting

The file attached to this record is the authors final peer reviewed version. The version of record can be found by following the DOI link.To resolve the conflict between our desire for a good smoothing effect and desire to give additional weight to the recent change, a grey accumulating generation operator that can smooth the random interference of data is introduced into the double exponential smoothing method. The results of practical numerical examples have demonstrated that the proposed grey double exponential smoothing method outperforms the traditional double exponential smoothing method in forecasting problems

SISTEM PERAMALAN DAN PENGENDALIAN PERSEDIAAN MAKANAN PADA RUMAH SAKIT DENGAN METODE EXPONENTIAL SMOOTHING

Kepuasan pelayanan pasien merupakan indikator kinerja yang baik pada Rumah Sakit, salah satu yang berperan penting adalah pelayanan logistik makanan. Dengan jumlah pasien yang fluktuatif, Rumah Sakit harus mampu memenuhi permintaan jumlah pasien setiap hari. Penelitian ini bertujuan untuk membangun sistem peramalan dan pengendalian persediaan makanan untuk menentukan jumlah porsi makanan yang harus tersedia pada hari berikutnya. Jumlah bahan baku makanan dikendalikan dengan menggunakan model re-order point, yang bertujuan untuk mengantisipasi terjadinya kekurangan persediaan. Data diperoleh dari jumlah permintaan makanan selama 212 hari untuk tiga waktu, pagi, siang, dan malam hari. Nilai pemulusan dan peramalan menggunakan parameter alpha 0,3 dan 0,7 dengan perhitungan kesalahan peramalan minimal menggunakan MAPE untuk alpha 0,7 sebesar 12,81% untuk waktu pagi, 11,59% waktu siang, dan 10,96% waktu malam. Hasil peramalan tidak hanya dapat digunakan untuk mengalokasikan porsi makanan namun juga dapat mengendalikan persediaan bahan baku. Kata kunci : Forecasting Method, Exponential Smoothing, Re-Order Point The satisfaction of patient care is an indicator of good performance in hospitals, one of which plays a critical role is a logistic serving of food. With the fluctuating number of patients, the hospital should be able to meet the demand for the number of patients each day. This study aims to build the system of forecasting and controlling the food supplies to determine the number of servings of food supplies in the next period. The implementation of Exponential Smoothing method is used to predict the number of servings should be available for the next period. Amount of food raw material is controlled using re-orders point model, it aims to anticipate the occurrence of stockout with the minimum amount of food provides should be available. The data were obtain ed from the requested amount of food during 212 days for three times, morning, noon, and night. Forecasting values using alpha parameters 0.3 and 0.7 with a minimum forecasting error calculation using MAPE for alpha 0.7 with a value 12.81% for morning time, 11.59% during the day, and 10.96% night time. Forecasting result not only can be used to allocate food supplies but also to control stock of raw material food. Keywords : Forecasting Method, Exponential Smoothing, Re-Order Poin

Comparison of exponential smoothing and neural network method to forecast rice production in Indonesia

Rice is the most important food commodity in Indonesia. In order to achieve affordability, and the fulfillment of the national food consumption according to the Indonesia law no. 18 of 2012, Indonesia needs information to support the government's policy regarding the collection, processing, analyzing, storing, presenting and disseminating. One manifestation of the Information availability to support the government’s policy is forecasting. Exponential smoothing and neural network methods are commonly used to forecasting because it provides a satisfactory result. Our study are comparing the variants of exponential and backpropagation model as a neural network to forecast rice production. The evaluation is summarized by utilizing Mean Square Percentage Error (MAPE), Mean Square Error (MSE). The results show that neural network method is preferable than the statistics method since it has lower MSE and MAPE values than statistics method

SUGAR DEMAND FORECASTING IN PT XYZ WITH WINQSB SOFTWARE

PT XYZ is a manufacturing company engaged in the production of sugar and its by-products. Currently, the determination of the amount of production at PT XYZ has not been adjusted to meet customer demand, which may continue to decrease or increase for each period. If there is a condition that the amount of production is greater than demand, it will increase the cost of storage due to accumulation. Meanwhile, if the amount of production is smaller than demand, there will be an out-of-stock condition that can reduce consumer confidence. These problems can be solved by forecasting Tambora Sugar demand at PT XYZ to meet consumer demand using the forecasting method (forecasting) with the help of WinQsb software with input, namely sugar demand data from 2021 PT SMS. The request data will later be analyzed from the request using a scatter diagram. Furthermore, after the pattern is known, the appropriate forecasting method will be determined and inputted into the WinQsb software. Based on the calculation results, it is known that the demand pattern from last year tends to trend down so the chosen method is the Double Exponential Smoothing (DES), Single Exponential Smoothing (SES), and Linear Regression (LR) method, with the best method being Linear Regression which produces the smallest error. The output is in the form of a Master Production Schedule (MPS), namely in the 13th to 18th periods, respectively 2142; 1757; 1373; 989; 604; 220 sacks

PERAMALAN HARGA EMAS BATANGAN MENGGUNAKAN METODE GREY DOUBLE EXPONENTIAL SMOOTHING

Grey Double Exponential Smoothing (GDES) merupakan gabungan dari metode grey dan double exponential smoothing yang digunakan untuk melakukan peramalan data deret waktu yang berpola trend dengan keacakan, ketidakteraturan dan keterbatasan informasi data yang ada. Grey accumulated generating operator (r-AGO) yang dapat memuluskan gangguan acak data dimasukkan ke dalam metode double exponential smoothing sehingga kecenderungan pola data dapat dilihat dengan jelas. Hasil peramalan metode GDES diperoleh dengan cara mentransformasikan balik data transformasi r-AGO menggunakan inverse accumulated generating operator (IAGO). Penelitian ini bertujuan meramalkan harga emas batangan pada bulan Januari sampai Juni tahun 2020 menggunakan metode GDES serta mengukur kesalahan peramalan yang dihasilkan metode tersebut. Keakuratan hasil peramalan yang digunakan adalah mean absolute precentage error (MAPE). Dataset yang digunakan pada penelitian ini adalah rata-rata harga  emas batangan per gram dari bulan Januari 2016 sampai Desember 2019. Hasil peramalan harga emas yang diperoleh menggunakan metode GDES menunjukkan trend naik setiap bulannya dengan hasil peramalan terendah adalah Rp.755.340,39 pada bulan Januari 2020 dan hasil peramalan tertinggi adalah Rp.763.833,70 pada bulan Juni 2020. Nilai MAPE yang dihasilkan sebesar 1,53% yang berarti bahwa peramalan GDES untuk harga emas batangan termasuk dalam kategori peramalan yang sangat baik. Kata Kunci : peramalan, exponential smoothing, grey, r-AGO, IAGO, MAP

PERAMALAN PERMINTAAN SUKU CADANG OTOMOTIF KARET DENGAN INTEGRASI AGENT BASED MODELLING DAN DOUBLE EXPONENTIAL SMOOTHING

The objective of this study was to design an agent model for forecasting demand for rubber-based automotive parts using the Agent Based Modelling and Double Exponential Smoothing (ABMDES) approach.  Model design in rubber-based automotive spare parts forecasting using the integration of Agent Based Modelling (ABM) approach and Double Exponential Smoothing (DES) technique was done using agent design approach based on class diagram and definition function for each agent in mathematics models including DES-based forecasting. The ABM design has a structure consisting of an agent ID, an attribute to be calculated by the computer and function / process consisting of a function that has values ​​and voids (unstructured). Combining ABM and DES can guide us to see the forecast accuracy, monitoring the estimation of stock shortage and the excess of stock due to errors in DES forecast. Therefore, the Agent Based Modelling-Double Exponential Smoothing (ABMDES) approach is suitable for modelling the demand of rubber-based automotive parts in business simulation. Keywords: double exponential smoothing, function, SMEs, shortag

Parameter Optimization In Grey Holt – Winter Exponential Smoothing Using Golden Section

Grey Holt-Winter Exponential Smoothing is a combination of Grey and exponential smoothing methods used to forecast seasonal trends and seasonal time series data with randomness, irregularity and limited data information that available. The parameter values for smoothing level (α), trend (β) and seasonal (γ) in the Grey Holt - Winter method affect the performance of the forecasting model. The Grey Holt - Winter Method has not yet provided a way to select the optimal value of the smoothing parameter to minimize the size value of the forecast error. In this research, the Golden Section method is used to obtain the optimal value of the smoothing parameter.The Golden Section method has the basic concept of narrowing the interval of the origin area, so that the optimal value of the smoothing parameter is obtained in the Grey Holt-Winters forecasting. Mean Absolute Percentage Error (MAPE) is used to measure forecasting errors. The results of this research are comparison MAPE generated by the conventional method of Grey-Holt Winter Exponential Smoothing method with MAPE of Grey-Holt Winter Exponential Smoothing values using Golden Section parameter optimization. The data set used in this research is the Room Occupancy Rate of Star Hotel in Special Region of Yogyakarta from January 2008 - December 2017.Based on the test result for the data testing in amount of 96, obtained a minimum forecasting error based on the conventional method of Grey Holt Winter Exponential Smoothing is 16.06%, while the forecasting error minimum produced by the Grey Holt Winter Exponential Smoothing method with the Golden Section is 13.92% with the optimal parameter value proposed is α equal to 0.146, β is 0.010 and γ is 0.146

A prediction method for plasma concentration by using a nonlinear grey Bernoulli combined model based on a self-memory algorithm

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The goal of this work is to present and explore the application of a novel nonlinear grey Bernoulli combined model based on a self-memory algorithm, abbreviated as SA-NGBM, for modeling single-peaked sequences of time samples of acetylsalicylate plasma concentration following oral dosing. The self-memorization SA-NGBM routine reduces the dependence on a solitary initial value, as the initial state of the model utilizes multiple time samples. To test its forecasting performance, the SA-NGBM was used to extrapolate the plasma concentration predicted data, in comparison with the later time samples. The results were contrasted with those of the traditional optimized NGBM (ONGBM), exponential smoothing (ES) and simple moving average (SMA) using four popular accuracy and significance tests. That comparison showed that the SA-NGBM was much more accurate and efficient for matching the individual, nonlinear-system stochastic fluctuations than the existing ONGBM, ES and SMA models. The findings have potential applications for signal matching to similar small sample size, single-peaked, plasma concentration series