scholarly journals Analysis and Design of Inventory Control Information Systems With Forecasting Methods: Moving Average and Exponential Smoothing

bit-Tech ◽  
2021 ◽  
Vol 4 (1) ◽  
pp. 1-5
Author(s):  
Riki Riki ◽  
◽  
Stefanus Stefanus
Keyword(s):  
Information Systems ◽  
Inventory Control ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Analysis And Design ◽  
Control Information ◽  
Forecasting Methods ◽  
Data Corruption ◽  
Sales Forecasts ◽  
Forecasting Method

Inventory inventory on CV. Mitra Marga Sejahtera often experiences stockpiling of goods so that it wastes more costs and the manual process of recording goods using excel, so that they often experience data corruption and loss of sales data. Forecasting methods are usually used by the sales department in planning (sales planning) based on the results of sales forecasts, so that forecasting information can be useful for Production which uses Moving Average and Exponential Smoothing. the program that has been made using the forecasting method can help manage the stock of goods that will be needed in the coming months, so that store managers can save costs in stock items that are not excessive

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 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 INVENTORY CONTROL USING STATISTICS FORECASTING ON MANUFACTURE COMPANY

2014 ◽  
Vol 1 (2) ◽  
Author(s):  
Rizki Tri Prasetio
Keyword(s):  
Linear Regression ◽  
Inventory Control ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Economic Order Quantity ◽  
Economic Order ◽  
Order Quantity ◽  
Naive Method ◽  
Forecasting Method ◽  
Weighted Moving Average

Abstract - Inventory Control is a main and the most crucial factor for company that can cause an efficient production process. A lot of research use different method to support inventory control. This research use several forecasting method such as, Naïve Method, Exponential Smoothing, Exponential Smoothing with Trend, Moving Average, Weighted Moving Average and Linear Regression. Economic Order Quantity is used to calculate raw materials inventory. This research results suggest that company use Linear Regression as it has the smallest MAD and MSE of the six other methods. The company also has to implement Economic Order Quantity to minimalize loss profit due to excess inventory. Keywords : Inventory Control, Forecasting Method, Economic Order Quantity Abstrak - Pengendalian inventory merupakan salah satu faktor utama dan sangat penting bagi perusahaan karena sangat berpengaruh terhadap terciptanya proses produksi yang efisien. Banyak penelitian yang menggunakan beberapa metode guna mendukung pengendalian inventory. Penelitian ini menggunakan beberapa metode peramalan (forecasting method) diantaranya, Naïve Method, Exponential Smoothing, Exponential Smoothing with Trend, Moving Average, Weighted Moving Average dan Linear Regression. Serta Economic Order Quantity (EOQ) yang digunakan untuk menghitung persediaan bahan baku yang dibutuhkan dalam proses produksi. Hasil penelitian menghasilkan bahwa metode peramalan Linear Regression memiliki tingkat kesalahan yang dihitung menggunakan MAD dan MSE paling kecil diantara 6 metode lainnya. Serta mengimplementasikan Economic Order Quantity untuk meminimalisir kerugian akibat kelebihan persediaan. Kata Kunci : Pengendalian Persediaan, Metode Peramalan, Economic Order Quantity

Bullwhip Effect Analysis in a Supply Chain

2012 ◽  
pp. 646-665
Author(s):  
Mehdi Najafi ◽  
Reza Zanjirani Farahani
Keyword(s):  
Supply Chain ◽  
Statistical Method ◽  
Moving Average ◽  
Bullwhip Effect ◽  
Effect Analysis ◽  
Forecasting Methods ◽  
Effect Measure ◽  
Forecasting Method ◽  
Efficiency And Effectiveness ◽  
Main Factors

In today’s world, all enterprises in a supply chain are attempting to increase both their and the supply chain’s efficiency and effectiveness. Therefore, identification and consideration of factors that prevent enterprises to attain their expected/desired levels of effectiveness are very important. Since bullwhip effect is one of these main factors, being aware of its reasons help enterprises decrease the severity of bullwhip effect by opting proper decisions. Now that forecasting method is one of the most important factors in increasing or decreasing the bullwhip effect, this chapter considers and compares the effects of various forecasting methods on the bullwhip effect. In fact, in this chapter, the effects of various forecasting methods, such as Moving Average, Exponential Smoothing, and Regression, in terms of their associated bullwhip effect, in a four echelon supply chain- including retailer, wholesaler, manufacturer, and supplier- are considered. Then, the bullwhip effect measure is utilized to compare the ineffectiveness of various forecasting methods. Owing to this, the authors generate two sets of demands in the two cases where the demand is constant (no trend) and has an increasing trend, respectively. Then, the chapter ranks the forecasting methods in these two cases and utilizes a statistical method to ascertain the significance of differences among the effects of various methods.

scholarly journals ANALISIS PERAMALAN PENJUALAN PRODUK KERIPIK PISANG KEMASAN BUNGKUS (Studi Kasus : Home Industry Arwana Food Tembilahan)

2017 ◽  
Vol 11 (3) ◽  
pp. 135 ◽  
Author(s):  
Siti Wardah ◽  
Iskandar Iskandar
Keyword(s):  
Data Processing ◽  
Trend Analysis ◽  
Standard Error ◽  
Moving Average ◽  
Exponential Smoothing ◽  
A Value ◽  
Forecasting Method ◽  
Past Data ◽  
Product Sales ◽  
Exponential Smoothing Method

Peramalan adalah metode untuk memperkirakan suatu nilai dimasa depan dengan menggunakan data masa lalu. Penelitian ini dilakukan pada Home Industry Arwana Food. Pada penelitian ini, penulis membahas mengenai analisis peramalan penjualan produk kripik pisang untuk jenis kemasan bungkus. Peramalan yang dilakukan mengggunakan tiga metode yaitu metode Moving Average, metode Exponential Smoothing with Trend dan metode Trend Anayisis dengan membandingkan tingkat kesalahan (error) terkecil, maka metode peramalan yang  terpilih yaitu metode Trend Analysis, dengan nilai MAD sebesar 161,3539, MSE sebesar 55744,16, dan standar error sebesar 242,947. Dari analisis pengolahan data yang telah dilakukan berdasarkan metode peramalan yang terpilih, peramalan penjualan terhadap produk kripik pisang jenis kemasan bungkus adalah sebanyak 1121,424 atau 1122 bungkus/bulan, artinya pihak Home Industry Arwana Food Tembilahan harus menyediakan produk kripik pisang kemasan bungkus adalah sebanyak 1122 bungkus untuk tiap bulannya.      ABSTRACT Forecasting is a method to estimate a value of the future using past data. This research was conducted at the Home Industry Arowana Food. In this study, the authors discuss the analysis of product sales forecasting banana chips for this type of packaging wrap. Forecasting that do use traditional three methods are methods Moving Average, Exponential Smoothing method with Trend and Trend Anayisis method by comparing the level of errors (error) the smallest, then the selected forecasting method is the method of Trend Analysis, with a value of 161.3539 MAD, MSE of 55744 , 16, and the standard error of 242.947. From the analysis of data processing that has been carried out based on the method chosen forecasting, sales forecasting for products banana chips are as many types of packaging wrap 1121.424 or 1 122 packs / month, meaning the Home Industry Arowana Food Tembilahan must provide products banana chips wrapped packs is as much as 1122 wrap for each month.

scholarly journals Advising an Appropriate Forecasting Method for a Snacks Item (Biscuit) Manufacture Company in Bangladesh

2018 ◽  
Vol 66 (1) ◽  
pp. 55-58
Author(s):  
Nandita Barman ◽  
M Babul Hasan ◽  
Md Nayan Dhali
Keyword(s):  
Time Series ◽  
Inventory Management ◽  
Linear Trend ◽  
Exponential Smoothing ◽  
Demand And Supply ◽  
Forecasting Methods ◽  
Forecasting Method ◽  
Short Term Forecasting ◽  
Exponential Smoothing Method ◽  
Manufacture Company

In this paper, we study the most appropriate short-term forecasting methods for the newly launched biscuit factory produces different types of biscuits. One of them is nut-orange twisted biscuits. As it is a newly launched biscuit factory, it does not use any scientific method to find future demand of their products to produce for the purpose of sales. Having an error free production as well as a good inventory management we try to find an appropriate forecasting method for the sets of data we analyzed for that specific production. Several forecasting methods of time series forecasting such as the Moving Averages, Linear Regression with Time, Exponential Smoothing, Holt‘s Method, Holt-Winter‘s Method etc. can be applied to estimate the demand and supply for these companies. This paper focuses on selecting an appropriate forecasting technique for the newly launched biscuit company. For this, we analyze Exponential Smoothing method as used to time series. We observe from the empirical results of the analysis that if the data has no trend as well as seasonality, Exponential Smoothing Forecasting Method processes as the most appropriate forecasting method for the factory. If the data experiences linear trend in it then Holt’s Forecasting Method processes as the most appropriate forecasting method for the sets of data we analyzed. Dhaka Univ. J. Sci. 66(1): 55-58, 2018 (January)

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 PREDIKSI JUMLAH PENERIMAAN SISWA SMK SWASTA TAHUN AJARAN 2011/2012

2012 ◽  
Vol 3 (2) ◽  
pp. 923
Author(s):  
Haryadi Sarjono
Keyword(s):  
Mean Squared Error ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Vocational High School ◽  
Absolute Deviation ◽  
Squared Error ◽  
Forecasting Methods ◽  
Naive Method ◽  
Manual Calculation ◽  
Weighted Moving Average

This study aims to determine prediction number of modern private Vocational High School (SMK) students in a province in Borneo with the approach of six forecasting methods: Linear Regression, Exponential Smoothing with Trend, Exponential Smoothing, Weighted Moving Average, Moving Average, and the Naive Method, besides using Manual calculation, the approach of QM for windows is used as a comparison. The result will be determined by the six forecasting methods which is used as a proper basis for the next calculating based on the smallest MAD (Mean Absolute Deviation) and MSE (Mean Squared Error) approach. The data in this study were made by the writer alone. 

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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