Analisis Penjualan Baju Seragam Sekolah di Konveksi Hanifah Collection

Hanifah Collection is a company engaged in the convection of school uniforms. The fluctuating number of requests each month creates its problems in determining the amount of production. This study aims to find a method that matches the data pattern as the basis for determining the amount of output in the next period. The technique used in this study is the forecasting method of Moving Average, Exponential Smoothing and Triple Exponential Smoothing with parameter level errors of each way using MAD, MSE, and MAPE. From the results of this study, there is a moving average method with the most appropriate method in determining demand forecasting in the next period with a value of MAD of 172.22, MSE of 46624.34 and MAPE 46624.34.

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Planning Production Capacity Using Time Series Forecasting Method and Linier Programming

Engineering Management Research ◽

10.5539/emr.v7n2p20 ◽

2018 ◽

Vol 7 (2) ◽

pp. 20

Author(s):

M. Tirtana Siregar ◽

S. Pandiangan ◽

Dian Anwar

Keyword(s):

Average Method ◽

Moving Average ◽

Demand Forecasting ◽

Production Capacity ◽

Mean Absolute Percentage Error ◽

Programming Method ◽

Percentage Error ◽

Absolute Percentage Error ◽

Forecasting Method ◽

Planning Production

The objectives of this research is to determine the amount of production planning capacity sow talc products in the future utilizing previous data from january to december in year 2017. This researched considered three forecasting method, there are Weight Moving Average (WMA), Moving Average (MA), and Exponential Smoothing (ES). After calculating the methods, then measuring the error value using a control chart of 3 (three) of these methods. After find the best forecasting method, then do linear programming method to obtain the exact amount of production in further. Based on the data calculated, the method of Average Moving has a size of error value of Mean Absolute Percentage Error of 0.09 or 9%, Weight Moving Average has a size error of Mean Absolute Percentage Error of 0.09 or 9% and with Exponential Method Smoothing has an error value of Mean Absolute Percentage Error of 0.12 or 12%. Moving Average and Weight Moving Average have the same MAPE amount but Weight Moving Average has the smallest amount Mean Absolute Deviation compared to other method which is 262.497 kg. Based on the result, The Weight Moving Average method is the best method as reference for utilizing in demand forecasting next year, because it has the smallest error size and has a Tracking Signal  not exceed the maximum or minimum control limit is ≤ 4. Moreover, after obtained Weight Moving Average method is the best method, then is determine value of planning production capacity in next year using linier programming method. Based on the linier programming calculation, the maximum amount of production in next year by considering the forecasting of raw materials, production volume, material composition, and production time obtained in one (1) working day is 11,217,379 pcs / year, or 934,781 pcs / month of finished product. This paper recommends the company to evaluate the demand forecasting in order to achieve higher business growth.

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

J TI UNDIP JURNAL TEKNIK INDUSTRI ◽

10.14710/jati.11.3.135-142 ◽

2017 ◽

Vol 11 (3) ◽

pp. 135 ◽

Cited By ~ 1

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.

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

Talenta Conference Series Energy and Engineering (EE) ◽

10.32734/ee.v2i4.671 ◽

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

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

Jurnal Informatika Teknologi dan Sains ◽

10.51401/jinteks.v2i1.554 ◽

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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MINIMASI BIAYA PRODUKSI PADA PRODUK WINKER RELAY MELALUI PERENCANAAN PRODUKSI AGREGAT

Jurnal Penelitan Ekonomi dan Bisnis ◽

10.33633/jpeb.v4i2.2556 ◽

2019 ◽

Vol 4 (2) ◽

pp. 143-152

Author(s):

Yevita Nursyanti

Keyword(s):

Quantitative Method ◽

Moving Average ◽

Demand Forecasting ◽

Exponential Smoothing ◽

Aggregate Production Planning ◽

Regression Methods ◽

A Value ◽

Double Exponential ◽

Double Exponential Smoothing ◽

Minimum Costs

The purpose of this research is to determine demand forecasting on relay winker products and to determine the aggregate planning of relay winker products that produce minimum costs. The method used in this study is a comparative quantitative method. The data used in conducting aggregate planning are data from forecasting calculations with the chosen method, forecasting is calculated using several methods, namely the Moving Average, Exponential Smoothing, Double Exponential Smoothing, Quadratic and Linear Regression methods. Select this method by looking for the MAPE value. The method that has the smallest MAPE value is Double exponential Smoothing with a value of 2.7%. Aggregate production planning is carried out for 2017 with the method of transportation (least cost), permanent labor and level strategy. The results of data processing show the best method for aggregate planning is transportation method (least cost) which has the lowest cost of Rp. 9,858,625,445.

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ANALISIS PERAMALAN PENJUALAN AIR MINUM DALAM KEMASAN 240ml PADA PT TRIJAYA TIRTA DARMA (GREAT) DENGAN METODE SINGLE MOVING AVARAGE DAN EXPONENTIAL SMOOTHING

Industrika: Jurnal Ilmiah Teknik Industri ◽

10.37090/indstrk.v4i2.235 ◽

2020 ◽

Vol 4 (2) ◽

Author(s):

Dita Meliana ◽

◽

Suharto Suharto ◽

Putri Endah Suwarni ◽

◽

...

Keyword(s):

Moving Average ◽

Exponential Smoothing ◽

Bottled Water ◽

A Value ◽

Forecasting Method ◽

The Future ◽

Future Events ◽

Past Data ◽

Product Sales ◽

Exponential Smoothing Method

Abstract Companies engaged in the field of product sales or distribution services, definitely want success in their activities in the future. This shows that every company always strives to continue to be able to develop in its business field in the future. Forecasting is a method for estimating a value in the future by using past data. Forecasting can also be interpreted as an art and science to predict future events. Researchers conducted a study by taking a location in PT Trijaya Tirta Dharma bottled water company. The author discusses forecasting sales of 240ml bottled water using two methods, namely Single Moving Average and Exponential Smoothing. The author compares the smallest error rate, the forecasting method chosen is Exponential Smoothing. For the Exponential Smoothing method, the results obtained using alpha 0.2 are for February of 195,767, March 197905, April 199,527, May 213,661, June 227,287, July 238,295, August 246,234, September 263,600, October 256,923, November 266,857, December 238,762. Then the forecast is MFE 22466,593, MAD 22466.593, MSE 55522225874, MAPE 2%.

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Fourier Analysis for Demand Forecasting in a Fashion Company

International Journal of Engineering Business Management ◽

10.5772/56839 ◽

2013 ◽

Vol 5 ◽

pp. 30 ◽

Cited By ~ 20

Author(s):

Andrea Fumi ◽

Arianna Pepe ◽

Laura Scarabotti ◽

Massimiliano M. Schiraldi

Keyword(s):

Moving Average ◽

Demand Forecasting ◽

Fourier Method ◽

Weather Conditions ◽

Economic Factors ◽

Fast Fourier Transform Algorithm ◽

Fashion Industry ◽

Forecasting Method ◽

Short Lifecycle ◽

Socio Economic Factors

In the fashion industry, demand forecasting is particularly complex: companies operate with a large variety of short lifecycle products, deeply influenced by seasonal sales, promotional events, weather conditions, advertising and marketing campaigns, on top of festivities and socio-economic factors. At the same time, shelf-out-of-stock phenomena must be avoided at all costs. Given the strong seasonal nature of the products that characterize the fashion sector, this paper aims to highlight how the Fourier method can represent an easy and more effective forecasting method compared to other widespread heuristics normally used. For this purpose, a comparison between the fast Fourier transform algorithm and another two techniques based on moving average and exponential smoothing was carried out on a set of 4-year historical sales data of a €60+ million turnover medium- to large-sized Italian fashion company, which operates in the women's textiles apparel and clothing sectors. The entire analysis was performed on a common spreadsheet, in order to demonstrate that accurate results exploiting advanced numerical computation techniques can be carried out without necessarily using expensive software.

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Komparasi Metode Peramalan Jumlah Permintaan Kamar Hotel

Jurnal Ilmiah Informatika Global ◽

10.36982/jig.v9i2.563 ◽

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

AbstractHotel 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%.Keywords: forecasting, linier regression, moving average, exponential smoothing.AbstrakHotel 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%.Kata kunci: peramalan, regeresi linier, moving average, exponential smoothing.

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Prediksi Tingkat Kriminalitas Menggunakan Metode Single Moving Average (Studi Kasus Polres Asahan Sumatera Utara)

Jurnal Sistim Informasi dan Teknologi ◽

10.37034/jsisfotek.v3i4.63 ◽

2021 ◽

pp. 183-188

Author(s):

Mustopa Husein Lubis ◽

S Sumijan

Keyword(s):

Sexual Harassment ◽

Crime Rate ◽

Average Method ◽

Moving Average ◽

Prediction System ◽

Rate Data ◽

Criminal Cases ◽

Accuracy Rate ◽

Know How ◽

Forecasting Method

Crime is all kinds of actions and actions that are economically and psychologically harmful that violate the laws in force in the State of Indonesia as well as social and religious norms. Ordinary criminal acts affect the security of the community and threaten their inner and outer peace. The research location is the Asahan Police which is an agency that can provide security and protection for the community, especially those in Asahan Regency. The problem that occurs in this location is that there is no prediction system in Asahan Regency, due to the lack of knowledge factor in processing crime rate data. So it is difficult to know how much the increase or decrease in criminal cases carried out at the Asahan Police. The data used are cases of murder, sexual harassment, assault, violent theft, weight theft, motorcycle theft, fraud and counterfeiting money for the last 5 years from 2016 to 2020. This study aims to predict the crime rate in Asahan Regency in order to anticipating the upcoming spike in crime. The system that will be made uses forecasting or forecasting. With the Single Moving Average forecasting method. The Single Moving Average method is a forecast for the time in the future. The results of the calculation of predictions for criminal cases in 2021 obtained 3 cases of murder, 2 cases of sexual harassment in 2021, 252 cases of maltreatment in 2021, 27 cases of violent theft in 2021, 348 thefts with a weight in 2021. cases, motorcycle theft in 2021, totaling 90 cases, fraud in 2021, amounting to 85 cases, and counterfeiting money in 2021, totaling 1 case. This result has an accuracy rate of 99% from the reality of the crime that occurred, so this study is very appropriate to be used to predict the crime rate.

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

JUDICIOUS ◽

10.37010/jdc.v2i2.463 ◽

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.

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