scholarly journals Rancangan Penjadwalan Produksi Brankas Dengan Metode Heijunka Di PT. Chubb Safes Indonesia

2021 ◽  
Vol 6 (1) ◽  
pp. 9
Author(s):  
Sopian Maulana ◽  
Nunung Nuhasanah
Keyword(s):  
Production Scheduling ◽  
Mean Absolute Error ◽  
Moving Average ◽  
Working Hours ◽  
Absolute Error ◽  
Exponential Smoothing ◽  
Production Plan ◽  
Daily Production ◽  
Demand Prediction ◽  
Single Exponential

<p><strong>PT. Chubb Safes Indonesia (PT. CSI) is a company engaged in manufacturing and producing safety products for the public. In carrying out safe production, there is often a difference between the production plan made by the production department and the actual production done by the operator on the production floor. From the production plan that has been made, on the week-6 to week-8 from PT. CSI is only 76% to 89% of the production plan achieved. PT. This CSI has a FIFO (First In First Out) scheduling system and implements a mixed production system. Based on the problems experienced by PT. CSI, this can be minimized by predicting demand using the single moving average and single exponential smoothing method then the best demand prediction is obtained based on the value of the smallest deviation using the Mean Absolute Error (MAE) method where the results are obtained in the next week or the 11th week. PT. CSI can produce as many as 472 units of safes in one week. And by scheduling production using the heijunka method PT. CSI can perform 472 safe productions within 75 working hours of the available 88 working hours resulting in an efficiency of 15%. Then PT. CSI can increase daily production capacity from 424 units in the week-8 to 554 units of safes or 92 units of safes per day.</strong></p><p><strong><em>Keywords</em></strong> – <em>Demand prediction, Production scheduling, Heijunka method, Single moving average, Single exponential smoothing, Mean absolute error </em></p>

scholarly journals Peramalan Penjualan dengan Metode Exponential Smoothing (Studi Kasus : Penjualan Bakso Kemasaan/Kiloan Rumah Bakso Bang Ipul)

2019 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Hisyam Ihsan ◽  
Rahmat Syam ◽  
Fahrul Ahmad
Keyword(s):  
Mean Squared Error ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Exponential Smoothing ◽  
Trial And Error ◽  
Squared Error ◽  
Forecasting Method ◽  
Operational Activities ◽  
Exponential Smoothing Method ◽  
Single Exponential

Abstrak. Peramalan penjualan memungkinkan sebuah perusahan memilih kebijakan yang optimal untuk membuat keputusan yang sesuai dan mempertahankan efisiensi dari kegiatan operasional. Rumah Bakso Bang Ipul adalah salah satu usaha yang melakukan penjualan yakni penjualan bakso kemasaan/kiloan. Oleh sebab itu,. Rumah Bakso Bang Ipul sangat memerlukan peramalan penjualan untuk meningkatkan keuntungan dan menghindari terjadinya kelebihan atau kekurangan persedian bakso kemasaan/kiloan. Penelitian ini dilakukan peramalan dengan metode exponential smoothing. Adapun parameter atau a yang digunakan dalam meramalkan penjualan adalah a = 0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8, dan 0.9. Singel exponential smoothing melakukan perbandingan dalam menentukan nilai a, dengan mencari nilai a tersebut secara trial and error sampai menemukan a yang memiliki error minimum dengan pencarian menggunakan metode mean absolute error (MAE) dan metode Mean Squaered error (MSE). Sehingga dipilih a = 0.1 dengan nilai MAE = 6.23 dan nilai MSE = 58.32. berdasarkan hasil ini, dengan menggunakan metode singel exponential smoothing dan a =0.1 diperoleh hasil peramalan penjualan bakso bang ipul pada bulan juni 2018 sebanyak 48 kilogram.Kata Kunci: Peramalan, Metode Exponential Smoothing, Metode Singel Exponential SmoothingAbstract. Sales forecasting enables an optimal policy of the company had to make the appropriate decision and maintain the efficiency of operational activities. Rumah Bakso Bang Ipul is a business that sells packaged meatballs. Therefore, Rumah Bakso Bang Ipul is in need of sales forecasting to increase profit and avoid the occurrence or lack of supply of packaged meatballs. This research was conducted by the method of exponential smoothing forecasting. As for parameter or a used predicting sales is a = 0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8, and 0.9. single exponential smoothing do a comparison in determining the value of a, by searching for the value of such a trial and error to find a that has minimum error with search method using the mean absolute error (MAE) and mean squared error (MSE). So that selected a = 0.1 with MAE value = 6.23 and MSE Value = 58.32. Based on  these results, using the method of single exponential smoothing and retrieved results forecasting Rumah Bakso Bang Ipul in July 2018 as much as 48 kilograms.Keywords: Forecasting, Method of exponential smoothing, Method of single exponential smoothing.

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 Detection of Tea Export Potentiality from Bangladesh in the World Market: An Autoregressive Integrated Moving Average (ARIMA) Approach

2020 ◽  
pp. 21-29
Author(s):  
A. U. Noman ◽  
S. Majumder ◽  
M. F. Imam ◽  
M. J. Hossain ◽  
F. Elahi ◽  
...  
Keyword(s):  
Mean Absolute Error ◽  
Moving Average ◽  
Arima Model ◽  
Absolute Error ◽  
World Market ◽  
Information Criteria ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Autoregressive Integrated Moving Average ◽  
The World

Export plays an important role in promoting economic growth and development. The study is conducted to make an efficient forecasting of tea export from Bangladesh for mitigating the risk of export in the world market. Forecasting has been done by fitting Box-Jenkins type autoregressive integrated moving average (ARIMA) model. The best ARIMA model is selected by comparing the criteria- coefficient of determination (R2), root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE) and Bayesian information criteria (BIC). Among the Box-Jenkins ARIMA type models for tea export the ARIMA (1,1,3) model is the most appropriate one for forecasting and the forecast values in thousand kilogram for the year 2017-18, 2018-19, 2019-20, 2020-21 and 2021-22, are 1096.48, 812.83, 1122.02, 776.25 and 794.33 with upper limit 1819.70, 1348.96, 1862.09, 1288.25, 1318.26 and lower limit 660.69, 489.78, 676.08, 467.74, 478.63, respectively. So, the result of this model may be helpful for the policymaker to make an export development plan for the country.

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%.

scholarly journals Forecasting incidence of tuberculosis cases in Brazil based on various univariate time-series models

2019 ◽  
Vol 7 (10) ◽  
pp. 894-909
Author(s):  
Rhuan Carlos Martins Ribeiro ◽  
Thaynara Araújo Quadros ◽  
John Jairo Saldarriaga Ausique ◽  
Otavio Andre Chase ◽  
Pedro Silvestre da Silva Campos ◽  
...  
Keyword(s):  
Public Health ◽  
Time Series ◽  
Mean Square Error ◽  
High Performance ◽  
Moving Average ◽  
Arima Model ◽  
Public Health Problem ◽  
Absolute Error ◽  
Exponential Smoothing ◽  
Mean Square

Tuberculosis (TB) remains the world's deadliest infectious disease and is a serious public health problem. Control for this disease still presents several difficulties, requiring strategies for the execution of immediate combat and intervention actions. Given that changes through the decision-making process are guided by current information and future prognoses, it is critical that a country's public health managers rely on accurate predictions that can detect the evolving incidence phenomena. of TB. Thus, this study aims to analyze the accuracy of predictions of three univariate models based on time series of diagnosed TB cases in Brazil, from January 2001 to June 2018, in order to establish which model presents better performance. For the second half of 2018. From this, data were collected from the Department of Informatics of the Unified Health System (DATASUS), which were submitted to the methods of Simple Exponential Smoothing (SES), Holt-Winters Exponential Smoothing (HWES) and the Integrated Autoregressive Moving Average (ARIMA) model. In the performance analysis and model selection, six criteria based on precision errors were established: Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percent Error (MAPE) and Theil's U statistic (U1 and U2). According to the results obtained, the HWES (0.2, 0.1, 0.1) presented a high performance in relation to the error metrics, consisting of the best model compared to the other two methodologies compared here.

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 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

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