scholarly journals Time Series Analysis to Predicting Demand of Roasted Coffee Production

2019 ◽  
Vol 10 (5) ◽  
pp. 26
Author(s):  
Agatha Rinta Suhardi ◽  
Shendy Amalia ◽  
Shinta Oktafien ◽  
Siska Ayudia Adiyanti ◽  
Siti Komariah ◽  
...  
Keyword(s):  
Mean Squared Error ◽  
Moving Average ◽  
Demand Forecasting ◽  
Moving Averages ◽  
Roasted Coffee ◽  
Coffee Production ◽  
Absolute Deviation ◽  
Forecasting Method ◽  
Excess Production ◽  
Production Demand

Consumer demand conditions for fluctuating roasted coffee and ineffective production planning often lead to excessive production. Excess production will lead to wasteful costs and maintenance of quality on roasted coffee. Production demand forecasting is the basis for making production demand decisions. The purpose of this study is to predict the number of production requests for the next period and determine the most suitable forecasting method in determining the amount of roasted coffee production demand. The object of the data taken is roasted coffee. Analysis methods use moving averages, weighted moving averages, and exponential smoothing. In determining the most suitable forecasting method based on the Mean Absolute Deviation (MAD) forecasting value and the smallest Mean Squared Error (MSE) of each method used. The results of this study indicate that the most suitable forecasting method is using a Weighted Moving Average with a three-month period and forecasting roasted coffee production for November 2016 of 38.3 kg.

PENERAPAN METODE SINGLE MOVING AVERAGE DAN EXSPONENTIAL SMOOTHING PADA USAHA ASRIE MODESTA

2020 ◽  
Vol 3 (1) ◽  
pp. 547
Author(s):  
Dirarini Sudarwadi Sudarwadi ◽  
Mila Fitriani ◽  
Nurlaela Nurlaela
Keyword(s):  
Mean Square Error ◽  
Moving Average ◽  
Secondary Data ◽  
Primary Data ◽  
Moving Averages ◽  
Mean Absolute Deviation ◽  
Mean Square ◽  
Absolute Deviation ◽  
Forecasting Method ◽  
Second Period

This study  aims to (1) analyze the number of demands for batik products in the second period of 2018. (2) To analyze the most appropriate forecasting method. (3) To analyze the forecasting of the first period in 2019 using the selected forecasting method. This reseach uses primary data and secondary data with data collection techniques using interviews, observation, and documentation. The analysis used is Single Moving Averages and Exsponential Smoothing. The results of research in forecasting demand for batik products in 2019 with the Single Moving Average method are 3,936 units with Mean Absolute Deviation (MAD) of 632.5 units and Mean Square Error (MSE) of 693,718 units. And the Exsponential Smoothing Alpha 0.05 method is 2,788,879 units, with Mean Absolute Deviation (MAD) of 694,318 units and Mean Square Error (MSE) of 960,665 units. The method suggested to company in making forecast predictions is to use the Single Moving Averages method because it has the smallest error rate that compared to the Exsponential Smoothing method with an Alpha value of 0.05.

scholarly journals Planning Production Capacity Using Time Series Forecasting Method and Linier Programming

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.

PERENCANAAN PERSEDIAAN BAHAN BAKU AKAR TANJUNG DENGAN METODE MATERIAL REQUIREMENTS PLANNING (MRP) PADA UPT. MAKARTI POMOSDA

2020 ◽  
Vol 16 (3) ◽  
pp. 1-12
Author(s):  
Khoirul Hidayah ◽  
Sukarni Sukarni ◽  
Achmad Syaichu
Keyword(s):  
Standard Error ◽  
Mean Squared Error ◽  
Moving Average ◽  
Mean Absolute Deviation ◽  
Absolute Deviation ◽  
Material Requirements Planning ◽  
Safety Stock ◽  
Mean Error ◽  
Squared Error ◽  
Material Requirement

Suatu produksi yang direncanakan dengan baik akan menghasilkan efektivitas dan efisiensi produksi bagi perusahaan. Pentingnya perencanaan material pada perusahaan diharapkan dapat menghasilkan sistem yang baik terhadap proses produksi. Tujuan dari penelitian ini adalah untuk mengetahui penerapan Material Requirement Planning (MRP) sehingga kebutuhan bahan baku selama proses produksi di UPT MAKARTI POMOSDA dapat terpenuhi dengan menggunakan metode peramalan forecasting dalam satu tahun yaitu, moving average dan weighted moving average.  Metode ini terpilih untuk mengetahui safety stock nya produk setiap bulan dan setiap tahun. Berdasarkan detail dan analisa kesalahan metode moving average dengan menggunakan program POM QM forWindows Versi 3 Basic (Mean Error) 42,455, MAD (Mean Absolute Deviation) 259,545, MSE (Mean Squared Error) 118490,6, Standard Error (denom=n-2=9) 380,555, MAPE (Mean Absolute Percent Error) 643, dan next period 480. Sedangkan detail dan analisa kesalahan metode ini dengan menggunakan program POM QM For Windows Versi 3 Basic (Mean Error) 38,827, MAD (Mean Absolute Deviation) 212,257, MSE (Mean Squared Error) 83586,58, Standard Error (denom=n-2=9) 323,239, MAPE (Mean Absolute Percent ) 495, dan next period 464,893. Berdasarkan hasil proses diatas juga diketahui (safety stock) pada UPT MAKARTI POMOSDA pada tahun 2017 yaitu sejumlah 5209 unit, setelah dilakukan penelitian mengalami kenaikan sebesar 6758 dengan prosentase sebesar 129,7%, sehingga tidak ada penumpukan barang digudang. Hal ini juga didukung dengan penurunan biaya simpan bahan baku dari Rp 120.850/Periode (bulan) menjadi Rp 109.350/Periode (bulan).

scholarly journals Fourier Analysis for Demand Forecasting in a Fashion Company

10.5772/56839 ◽  
2013 ◽  
Vol 5 ◽  
pp. 30 ◽  
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.

scholarly journals ACCRUED FORECASTING ON TOURIST’S ARRIVAL IN BANGLADESH FOR SUSTAINABLE DEVELOPMENT

2021 ◽  
Vol 36 (2spl) ◽  
pp. 708-714
Author(s):  
Sayed Mohibul HOSSEN ◽  
◽  
Mohd Tahir ISMAIL ◽  
Mosab I. TABASH ◽  
Ahmed ABOUSAMAK ◽  
...  
Keyword(s):  
Sustainable Development ◽  
Mean Squared Error ◽  
Moving Average ◽  
Critical Role ◽  
Absolute Error ◽  
Tourism Industry ◽  
Percentage Error ◽  
Absolute Deviation ◽  
Autoregressive Integrated Moving Average ◽  
Private And Public

Forecasting of potential tourists’ appearance could assume a critical role in the tourism industry, arranging at all levels in both the private and public sectors. In this study our aim to build an econometric model to forecast worldwide visitor streams to Bangladesh. For this purpose, the present investigation focuses on univariate Seasonal Autoregressive Integrated Moving Average (SARIMA) modeling. Model choice criteria were Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Mean Squared Error (RMSE). As per descriptive statistics, the mean appearances were 207012 and will be 656522 (application) every year. Mean Absolute Deviation and Mean Squared Deviation likewise concurred with MAPE, MAE, and MSE. The result reveals that for sustainable development the SARIMA model is the reasonable model for forecasting universal visitor appearances in Bangladesh.

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. 

Optimization of Forecasting Moving Average Error in Probabilistic Demand Using Genetic Algorithm Based Fuzzy Logic

2012 ◽  
Vol 576 ◽  
pp. 710-713
Author(s):  
Chairul Saleh ◽  
Muhammad Ridwan Andi Purnomo ◽  
Hayati Mukti Asih
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Moving Average ◽  
Demand Forecasting ◽  
Fuzzy Time Series ◽  
Average Error ◽  
Demand Forecast ◽  
Advantages And Disadvantages ◽  
Excess Production ◽  
Before And After

Demand forecasting is one of the most critical factors in production planning. The uncertain demand, which is the basic idea of planning the production level, nowadays is one of serious problems. The inaccurate demand forecasting could affect to excess production or shortage stocks which led to lost sales. Usually, the forecasted result is hard to represent real condition. Some studies already conducted related to fuzzy time series, each of them has its own advantages and disadvantages compared to other approaches. This research presents the combination of simple moving average forecasting and fuzzy logic model to demand forecast. Then, genetic algorithm (GA) is applied to optimize the mean square error (MSE) inside the fuzzy system. The MSE before and after GA optimization is 0,2192 and 0,1821, respectively.

Exploring a Downstream Demand Inference Strategy in a Decentralized Two-Level Supply Chain

2021 ◽  
pp. 1-65
Author(s):  
Youssef Tliche ◽  
Atour Taghipour ◽  
Béatrice Canel-Depitre
Keyword(s):  
Supply Chain ◽  
Mean Squared Error ◽  
Moving Average ◽  
Bullwhip Effect ◽  
Inventory Level ◽  
Squared Error ◽  
Forecasting Method ◽  
Demand Information ◽  
The Mean ◽  
Inference Strategy

A coordination approach for forecast operations, known as downstream demand inference, enables an upstream actor to infer the demand information at his formal downstream actor without the need for information sharing. This approach was validated if the downstream actor uses the simple moving average (SMA) forecasting method. To answer an investigative question through other forecasting methods, the authors use the weighted moving average (WMA) method, whose weights are determined in this work thanks to the Newton's optimization of the upstream average inventory level. Starting from a two-level supply chain, the simulation results confirm the ability of the approach to reduce the mean squared error and the average inventory level, compared to a decentralized approach. However, the bullwhip effect is only improved after a certain threshold of the parameter of the forecasting method. Still within the framework of the investigation, they carry out a comparison study between the adoption of the SMA method and the WMA method. Finally, they generalize their results for a multi-level supply chain.

scholarly journals Analisis Penjualan Baju Seragam Sekolah di Konveksi Hanifah Collection

2017 ◽  
Vol 3 (2) ◽  
pp. 61
Author(s):  
Ahmad Fazarudin ◽  
Ahmad Nalhadi ◽  
Gerry Anugrah Dwiputra
Keyword(s):  
Average Method ◽  
Moving Average ◽  
Demand Forecasting ◽  
Exponential Smoothing ◽  
School Uniforms ◽  
A Value ◽  
Forecasting Method ◽  
A Company

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.

scholarly journals PERAMALAN PERMINTAAN PRODUK SALE PISANG PADA INDUSTRI “SAHABAT” DI DUSUN CIJOHO DESA MARGAJAYA KECAMATAN SUKADANA KABUPATEN CIAMIS

2020 ◽  
Vol 7 (3) ◽  
pp. 634
Author(s):  
Nisa Aprilianti ◽  
Iwan Setiawan ◽  
Muhamad Nurdin Yusuf
Keyword(s):  
Mean Squared Error ◽  
Moving Average ◽  
Mean Absolute Deviation ◽  
Absolute Deviation ◽  
Squared Error

Perkembangan industri sale pisang yang ada di Desa Margajaya cukup penting dan menarik untuk diteliti, Sahabat merupakan industri yang berdiri relatif baru dengan memproduksi sale pisang yang cukup besar, jumlah penjualan produk sale pisang pada industri Sahabat belum efesien, karena produk yang dijual sering dikembalikan dalam jumlah cukup banyak oleh reseller. Penelitian ini bertujuan mengetahui ramalan permintaan produk sale pisang pada industri “Sahabat” di Dusun Cijoho Desa Margajaya Kecamatan Sukadana Kabupaten Ciamis pada bulan Maret samapai Desember tahun 2020. Penelitian ini dilaksanakan pada bulan Februari 2020. Responden dalam penelitian ini adalah pemilik agroindustri “Sahabat”. Jenis penelitian yang digunakan adalah penelitian kuantitatif  dan metode penelitian yang digunakan yaitu data primer dan data sekunder. Analisis yang digunakan adalah Single moving average (Rata-rata bergerak tunggal). Hasil penelitian menunjukkan bahwa dengan metode single moving average (rata-rata bergerak) untuk Forecast adalah 12.744 bungkus, dengan Mean Absolute Deviation sebesar 1.639 dan Mean Squared Error sebesar 7.658. Hasil ramalan pada bulan Maret dapat dihasilkan pula hasil peramalan atau perkiraan pada tahun 2020.

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