scholarly journals Intermittent demand forecasting for aircraft inventories: a study of Brazilian’s Boeing 737NG aircraft´s spare parts management

TRANSPORTES ◽  
2019 ◽  
Vol 27 (2) ◽  
pp. 102-116
Author(s):  
Jersone Tasso Moreira Silva ◽  
Luiz Henrique Santos ◽  
Alexandre Teixeira Dias ◽  
Hugo Ferreira Braga Tadeu
Keyword(s):  
Moving Average ◽  
Demand Forecasting ◽  
Exponential Smoothing ◽  
Spare Parts ◽  
Next Generation ◽  
Intermittent Demand ◽  
Spare Parts Management ◽  
Single Exponential ◽  
Airline Company

Este estudo tem como objetivo avaliar cinco métodos de previsão para demanda intermitente usando uma série histórica de consumo de peças sobressalentes da aeronave 737 Next Generation, fabricado pela Boeing, da maior frota aérea brasileira gerenciada pela VRG Airline Company S/A. Os métodos de Winter, Croston, Single Exponential Smoothing, Weight Moving Average e Método de Distribuição de Poisson foram testados em um histórico de 53 peças sobressalentes e cada uma delas possui um histórico de demanda de trinta e seis meses (janeiro de 2013 a dezembro de 2015). Os resultados mostraram que os métodos Weight Moving Average, Distribuição de Poisson e Croston apresentaram os melhores ajustes. Além disso, observou-se que a maior parte das demandas por peças sobressalentes apresentaram um padrão smooth ao contrário do resultado obtido pelo estudo de Ghobbar and Friend (2003) que apresentou um padrão lumpy. Por outro lado, tem-se que o Método de Winter apresentou-se como o de pior ajuste em ambos os estudos. Conclui-se que os métodos de Weight Moving Average e Distribuição de Poisson são os mais adequados para avaliar a demanda intermitente para o caso da VRG Airline Company S/A.

scholarly journals Improving intermittent demand forecasting based on data structure

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
S. Fatemeh Faghidian ◽  
◽  
Mehdi Khashei ◽  
Mohammad Khalilzadeh ◽  
◽  
...  
Keyword(s):  
Hybrid Model ◽  
Prediction Models ◽  
Moving Average ◽  
Demand Forecasting ◽  
Spare Parts ◽  
Percentage Error ◽  
Complex Nature ◽  
Intermittent Demand ◽  
Classical Models ◽  
The Cost

Forecasting spare parts requirements is a challenging problem, because the normally intermittent demand has a complex nature in patterns and associated uncertainties, and classical forecasting approaches are incapable of modeling these complexities. The present study introduces a hybrid model that can impressively overcome the limitations of classical models while simultaneously using their unique advantages in dealing with the complexities in intermittent demand. The strategy of the proposed hybrid model is to use the three individual autoregressive moving average (ARMA), single exponential smoothing (SES), and multilayer perceptron (MLP) models simultaneously. Each of them has the potential of modeling a different structure and patterns of behavior among the data. The accuracy in forecasting ability is also increased by the suitable examination of these in the intermittent data. Croston’s method is the backbone of the suggested model. The proposed hybrid model is based on CV2 and ADI criteria, which improve its efficacy in examining inappropriate structures by reducing the cost of inappropriate modeling while increasing the prediction model accuracy. Using these results prevents the hybrid model from being confused or weakened in the modeling of all groups and reduces the risk of choosing the disproportionate model. The accuracy of prediction models was evaluated and compared using mean absolute percentage error (MAPE) by implementing an example, and promising results were achieved.

scholarly journals PERBANDINGAN PERAMALAN PENJUALAN PRODUK AKNIL PT.SUNTHI SEPURIMENGGUANAKAN METODE SINGLE MOVING AVERAGE DAN SINGLE EXPONENTIAL SMOOTING

2020 ◽  
Vol 2 (1) ◽  
pp. 15-22
Author(s):  
Nurul Hudaningsih ◽  
Silvia Firda Utami ◽  
Wari Ammar Abdul Jabbar
Keyword(s):  
Average Method ◽  
Moving Average ◽  
Demand Forecasting ◽  
Exponential Smoothing ◽  
Small Error ◽  
Employee Productivity ◽  
Smoothing Methods ◽  
Forecasting Methods ◽  
Product Sales ◽  
Single Exponential

Forecasting in the company is forecasting product sales to consumers. By knowing product sales can assist the company to provide materials to be produced and determine the production process itself. PT. Sunthi Sepuri is a pharmaceutical company. PT. Sunthi Sepuri often experiences marketing forecasting errors. This causes uncertainty in the amount of production so that it can cause employee productivity to decrease due to the increasing amount of production at any time. In this study demand forecasting will be held at PT. Sunthi Sepuri. This research apply the Single Moving Average and Single Exponential Smoothing methods, with the sample to be used is Aknil product, this product is a pain-relieving drug. Use the two methods to compare the most accurate forecasting methods and close to the actual value. The research methods start from gathering historical data, determining forecasting methods, forecasting calculations, determining the best method, and withdrawing conclusions. Based on the test results that the method that can be used to analyze data that has a small error rate is the Single Moving Average method. Forecasting results for July 2019 with the Single Exponential Smoothing method using ?: 0.8 are 408,488 caplets. As for July 2019, the Single Moving Average method is 466

On the empirical performance of some new neural network methods for forecasting intermittent demand

2020 ◽  
Vol 31 (3) ◽  
pp. 281-305
Author(s):  
M Z Babai ◽  
A Tsadiras ◽  
C Papadopoulos
Keyword(s):  
Neural Network ◽  
Empirical Investigation ◽  
Forecast Accuracy ◽  
Spare Parts ◽  
Intermittent Demand ◽  
Network Methods ◽  
Empirical Performance ◽  
Single Exponential ◽  
Better Than ◽  
Airline Company

Abstract In this paper, new neural network (NN) methods are proposed to forecast intermittent demand and we empirically study their performance as compared to parametric and non-parametric forecasting methods proposed in the literature. The empirical investigation uses demand data for 5,135 spare parts for the fleet of aircrafts of an airline company. Three parametric benchmark methods are examined: single exponential smoothing (SES), Croston’s method and Syntetos–Boylan approximation, along with two bootstrapping methods: Willemain’s method and Zhou and Viswanathan’s method. The benchmark NN method considered in this paper is that proposed by Gutierrez et al. (2008) The paper shows the outperformance of SES and the NN methods for (a) their forecast accuracy and (b) their inventory efficiency (trade-off between holding volumes and backordering volumes) when compared to the other methods. Moreover, among the NN methods, a new proposed method is shown to be better than that proposed by Gutierrez et al. in terms of forecast accuracy and inventory efficiency.

scholarly journals AIRCRAFT SPARE PARTS INVENTORY MANAGEMENT ANALYSIS ON AIRFRAME PRODUCT USING CONTINUOUS REVIEW METHODS

2020 ◽  
Vol 2 (1) ◽  
pp. 81-90
Author(s):  
Faikar Ridwan Harimansyah ◽  
Tukhas Shilul Imaroh
Keyword(s):  
Inventory Management ◽  
Cost Efficiency ◽  
Moving Average ◽  
Demand Forecasting ◽  
Service Level ◽  
Spare Parts ◽  
Continuous Review ◽  
Planning Methods ◽  
Abc Analysis ◽  
Single Exponential

The research  aims to find the factors that cause high inventory value, increase the value of forecasting precision, service level and cost efficiency with fishbone diagrams and proposed methods. The research sample is 9 spare parts included in classification A in the ABC analysis and maintenance list 2018. Forecasting methods use Moving Average, Single Exponential Smoothing and Syntetos-Boylan Approximation as well as Mean Square Error calculation, deterministic inventory calculation and Continuous Review Method. The results of this study are an increase in logistics costs by $ 808.71 in the inventory management proposal. An increase in service level from 95% to 99% and the error value in the calculation of the proposal becomes smaller using the proposed method. This study also found that the factor causing the high inventory value was due to inaccurate planning methods so that other comparative methods were needed that could increase the precision of demand forecasting.

scholarly journals IMPLEMENTASI MATERIAL REQUIREMENTS PLANNING (MRP) PADA PERENCANAAN PERSEDIAAN MATERIAL PANEL LISTRIK DI PT.TIS

SINERGI ◽  
2016 ◽  
Vol 20 (1) ◽  
pp. 36
Author(s):  
Putri Sari Dewi ◽  
Dana Santoso Saroso
Keyword(s):  
Moving Average ◽  
Exponential Smoothing ◽  
Order Quantity ◽  
Material Requirements Planning ◽  
Fixed Order ◽  
Single Exponential ◽  
Material Requirements

Semakin berkembangnya dunia industri perusahaan manufaktur membuat semakin ketatnya  persaingan pasar untuk mencukupi kebutuhan konsumen. Selain itu perusahaan juga dituntut untuk dapat memuaskan konsumen dengan cara  menyelesaikan pesanan konsumen tepat pada waktunya. Sehingga perlu ditunjang oleh sistem produksi yag efisien. Untuk dapat menciptakan sistem produksi yang efisien maka diperlukan suatu perencanaan yang baik. Peramalan dan perencanaan material untuk box panel menjadi alasan yang kuat untuk meminimalkan stok gudang, khususnya PT. TIS.  Adapun untuk perencanaan persediaan material box panel tersebut memerlukan peramalan yang optimal dengan memafaatkan metode Simple Moving Average (SMA) dan Single Exponential Smoothing (SES). Dengan membandingkan kedua metode tersebut dihasilkan data bahwa dengan metode Simple Moving Average menghasilkan nilai eror (MAD dan MSE) paling kecil, yaitu sebesar MAD 7,3 dan MSE 72. Sedangkan untuk perencanaan material menggunakan metode MRP Lot for Lot (LFL) dan Fixed Order Quantity (FOQ). Hasil perbandingan kedua metode tersebut menghasilan sistem Lot for Lot lebih efisien dan sesuai diterapkan pada PT. TIS karena total biaya persediaan minimum, yaitu sebesar Rp 199.692.470.

scholarly journals USULAN PERMINTAAN PRODUK SN 5 ML DI PT. XYZ DENGAN METODE TIME SERIES

2021 ◽  
Vol 8 (2) ◽  
pp. 117-122
Author(s):  
Sambas Sundana ◽  
Destri Zahra Al Gufronny
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Double Exponential ◽  
Double Exponential Smoothing ◽  
Single Exponential ◽  
Weighted Moving Average

Permasalahan yang dihadapi PT. XYZ yaitu kesulitan dalam menentukan jumlah permintaan produk yang harus tersedia untuk periode berikutnya agar tetap dapat memenuhi kebutuhan pelanggan dan tidak menyebabkan penumpukan barang dalam jangka waktu yang lama terutama produk SN 5 ML yang memiliki permintaan jumlah paling besar dari produk lainnya. Tujuan dari penelitian ini yaitu menentukan metode peramalan yang tepat untuk meramalkan jumlah permintaan produk SN 5 ml periode Januari sampai dengan Desember 2021 Metode yang digunakan dalam penelitian ini yaitu metode peramalan Moving Average (MA), Weighted Moving Average (WMA), Single Exponential Smoothing (SES), dan Double Exponential Smoothing (DES). Adapun langkah langkah peramalan yang dilakukan yaitu menentukan tujuan peramalan,memilih unsur apa yang akan diramal, menentukan horizon waktu peramalan (pendek, menengah, atau panjang), memilih tipe model peramalan, mengumpulkan data yang di perlukan untuk melakukan peramalan, memvalidasi dan menerapkan hasil peramalan Berdasarkan perhitungan didapat metode peramalan dengan persentase tingkat kesalahan terkecil dibandingkan dengan metode lainnya yaitu  metode Moving Average (MA) dengan hasil yang diperoleh permintaan produk SN 5 ML pada bulan Januari sampai dengan Desember 2021 yaitu sebanyak 22.844.583 unit

scholarly journals CAPACITY AVAILABILITY ANALYSIS TO HP SERVER PROLIANT DL380P GEN8 AT PT. AERO SYSTEM INDONESIA

2020 ◽  
Vol 2 (1) ◽  
pp. 141-148
Author(s):  
Naufal Rizki Rinditayoga ◽  
Dewi Nusraningrum
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Time Series Forecasting ◽  
Flight Data ◽  
Availability Analysis ◽  
Capacity Limit ◽  
Single Exponential ◽  
Weighted Moving Average

There has Servers who used for Keeping some domestic flight data at Soekarno-Hatta airport and its often experience downtime or servers inconnected, because these server capacity exceeds those maximum server limit. This research aims to examine and analyze capacity from HP Proliant DL380P Gen8 server that used for domestic flight data at PT. Aero Systems Indonesia. The population here used 3 servers with research sample is 1 server, HP Proliant DL380P Gen8 server. Data analysis exert time series forecasting used comparison from Moving Average, Single Exponential Smoothing and Weighted Moving Average methods. These results which using Moving Average shows that the use of server capacity exceeds those server capacity limit with highest usage up to 3,568 GB from total available capacity of 2,930 GB, so it needs to change immediately by other server capacity which more balanced with usage at PT. Aero Systems Indonesia.

scholarly journals Komparasi Metode Peramalan Jumlah Permintaan Kamar Hotel

2019 ◽  
Vol 9 (2) ◽  
Author(s):  
Rendra Gustriansyah ◽  
Wilza Nadia ◽  
Mitha Sofiana
Keyword(s):  
Moving Average ◽  
Exponential Smoothing ◽  
Standard Type ◽  
The Public ◽  
Forecasting Methods ◽  
Forecasting Method ◽  
Double Exponential ◽  
Double Exponential Smoothing ◽  
Exponential Smoothing Method ◽  
Single Exponential

<p class="SammaryHeader" align="center"><strong><em>Abstract</em></strong></p><p><em>Hotel is  a type of accommodation that uses most or all of the buildings to provide lodging, dining and drinking services, and other services for the public, which are managed commercially so that each hotel will strive to optimize its functions in order to obtain maximum profits. One such effort is to have the ability to forecast the number of requests for hotel rooms in the coming period. Therefore, this study aims to forecast the number of requests for hotel rooms in the future by using five forecasting methods, namely linear regression, single moving average, double moving average, single exponential smoothing, and double exponential smoothing, as well as to compare forecasting results with these five methods so that the best forecasting method is obtained. The data used in this study is data on the number of requests for standard type rooms from January to November in 2018, which were obtained from the Bestskip hotel in Palembang. The results showed that the single exponential smoothing method was the best forecasting method for data patterns as in this study because it produced the smallest MAPE value of 41.2%.</em></p><p><strong><em>Keywords</em></strong><em>: forecasting, linier regression, moving average, exponential smoothing.</em></p><p align="center"><strong><em>Abstrak</em></strong></p><p><em>Hotel merupakan jenis akomodasi yang mempergunakan sebagian besar atau seluruh bangunan untuk menyediakan jasa penginapan, makan dan minum serta jasa lainnya bagi umum, yang dikelola secara komersial, sehingga setiap hotel akan berupaya untuk mengoptimalkan fungsinya agar memperoleh keuntungan maksimum. Salah satu upaya tersebut adalah memiliki kemampuan untuk meramalkan jumlah permintaan terhadap kamar hotel pada periode mendatang. Oleh karena itu, penelitian ini bertujuan untuk meramalkan jumlah permintaan terhadap kamar hotel di  masa mendatang dengan menggunakan lima metode peramalan, yaitu regresi linier, single moving average, double moving average, single exponential smoothing, dan double exponential smoothing, serta untuk mengetahui perbandingan hasil peramalan dengan kelima metode tersebut sehingga diperoleh metode peramalan terbaik. Adapun data yang digunakan dalam penelitian ini merupakan data jumlah permintaan kamar tipe standar dari bulan Januari hingga November tahun 2018, yang diperoleh dari hotel Bestskip Palembang. Hasil penelitian menunjukkan bahwa metode single exponential smoothing merupakan metode peramalan terbaik untuk pola data seperti pada penelitian ini karena menghasilkan nilai MAPE paling kecil sebesar 41.2%.</em></p><strong><em>Kata kunci</em></strong><em>: peramalan, regeresi linier, moving average, exponential smoothing.</em>

scholarly journals PREDIKSI INFLASI DI INDONESIA MENGGUNAKAN METODE MOVING AVERAGE, SINGLE EXPONENTIAL SMOOTHING DAN DOUBLE EXPONENTIAL SMOOTHING

2020 ◽  
Vol 1 (2) ◽  
pp. 123-129
Author(s):  
Nugroho Arif Sudibyo ◽  
Ardymulya Iswardani ◽  
Arif Wicaksono Septyanto ◽  
Tyan Ganang Wicaksono
Keyword(s):  
Moving Average ◽  
Exponential Smoothing ◽  
Double Exponential ◽  
Double Exponential Smoothing ◽  
Single Exponential

Tujuan dari penelitian ini adalah untuk mengetahui model peramalan yang paling baik digunakan untuk meramalkan inflasi di Indonesia dengan data inflasi Januari 2015 sampai dengan Mei 2020. Penelitian ini menggunakan beberapa metode peramalan. Berdasarkan metode peramalan yang dilakukan didapatkan hasil peramalan yang paling baik dilihat dari MAPE, MAD dan MSD adalah single exponential smoothing. Selanjutnya, hasil peramalan menunjukkan bahwa tingkat inflasi di Indonesia pada Agustus 2020 sebesar  1,41746%.

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