scholarly journals ANALYSIS OF DATA FOR FORECASTING SALES BY THE MOVING AVERAGE METHOD

2021 ◽  
Author(s):  
Svetlana Evdokimova ◽  
Aleksandr Zhuravlev
Keyword(s):  
Data Analysis ◽  
Average Method ◽  
Moving Average ◽  
Calculated Data ◽  
Spare Parts ◽  
Retail Store ◽  
Relative Deviation

The paper discusses methods of data analysis for forecasting sales using the example of a BigCar retail store that sells spare parts for trucks. Based on the information on sales for the calen-dar year, using the moving average method in MS Excel, the forecast values were calculated for three periods. Analysis of the calculated data showed that the smallest relative deviation is given by a four-month period.

scholarly journals Implementasi Sistem Peramalan Persediaan Barang Menggunakan Metode Moving Average

2020 ◽  
Vol 4 (3) ◽  
pp. 707
Author(s):  
Wulandari Wulandari
Keyword(s):  
Information System ◽  
Data Analysis ◽  
Data Collection ◽  
Average Method ◽  
Moving Average ◽  
Analysis Process ◽  
The Future ◽  
Forecasting System ◽  
System Requirements ◽  
A Company

PT. XYZ is a company engaged in the sale of drugs and vitamins for livestock, each period the number of requests for goods always changes. Problems faced by PT. XYZ, which is expired goods because in a certain period the amount of stock is piling up due to the small amount of demand that causes the company to lose. The purpose of this study is to forecast the inventory process in order to minimize the company's losses against the estimates made so far. In order to minimize these problems, the authors model the forecasting information system for procurement of goods using VB.Net and MYSQL and combine the Moving Average method. The method used is the data collection and processing, and continued with the data analysis process, to model the system requirements of the writer using UML. The final result of this study is that the value of accuracy reaches 88% so that the inventory forecasting system using the moving average method can help managers in making decisions to determine the process of inventory in the future

Application of SARIMAX Model to Forecast Daily Sales in Food Retail Industry

2016 ◽  
Vol 7 (2) ◽  
pp. 1-21 ◽  
Author(s):  
Nari Sivanandam Arunraj ◽  
Diane Ahrens ◽  
Michael Fernandes
Keyword(s):  
Influencing Factors ◽  
Performance Measures ◽  
Moving Average ◽  
Food Product ◽  
Retail Store ◽  
Food Supply Chain ◽  
Sarima Model ◽  
Autoregressive Integrated Moving Average ◽  
Fresh Food ◽  
External Variables

During retail stage of food supply chain (FSC), food waste and stock-outs occur mainly due to inaccurate sales forecasting which leads to inappropriate ordering of products. The daily demand for a fresh food product is affected by external factors, such as seasonality, price reductions and holidays. In order to overcome this complexity and inaccuracy, the sales forecasting should try to consider all the possible demand influencing factors. The objective of this study is to develop a Seasonal Autoregressive Integrated Moving Average with external variables (SARIMAX) model which tries to account all the effects due to the demand influencing factors, to forecast the daily sales of perishable foods in a retail store. With respect to performance measures, it is found that the proposed SARIMAX model improves the traditional Seasonal Autoregressive Integrated Moving Average (SARIMA) model.

scholarly journals Pemisahan Anomali Regional-Residual pada Metode Gravitasi Menggunakan Metode Moving Average, Polynomial dan Inversion

2016 ◽  
Vol 3 (01) ◽  
pp. 10 ◽  
Author(s):  
Jarot Purnomo ◽  
Sorja Koesuma ◽  
Mohtar Yunianto
Keyword(s):  
Average Method ◽  
Moving Average ◽  
Continuation Method ◽  
Inversion Method ◽  
Polynomial Method ◽  
Minimum Error ◽  
Gravity Method ◽  
Upward Continuation ◽  
And Inversion ◽  
Separation Results

<span>It has been done a research about separation of regional-residual anomaly in Gravity method. <span>This research compares the result of three methods i.e. moving average method, polynomial <span>method, and inversion method. The computer program is created using a computer programming <span>Matlab 7. From three methods that have been made, the separation results are compared with<br /><span>results of separation by using Upward Continuation method. From the results of these <span>comparisons will be available an excellent program of regional-residual anomali separation. The <span>results show that in polynomial method of the order 4 obtained similar contour to the separation <span>by Upward Continuation Software. So that the output of this separation will be treated again <span>with Grav2DC software. The output of this software is the density of rock Grav2DC of the study<br /><span>area. Processing results obtained the minimum error of 1.85% for the separation by polynomial <span>method, while for the method of Upward Continuation obtained minimum error of 2.22%. The <span>results obtained show that the separation of regional-residual anomali by polynomial method is <span>similar to separation by Upward Continuation method.</span></span></span></span></span></span></span></span></span></span></span></span><br /></span>

scholarly journals FRAMA 1.0: Framework for Moving Average Operators Calculation in Data Analysis

2017 ◽  
Author(s):  
Bernabe Ortega-Tenezaca ◽  
Humbert Gonzalez-Diaz ◽  
Viviana Quevedo-Tumailli
Keyword(s):  
Data Analysis ◽  
Moving Average

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&nbsp; not exceed the maximum or minimum control limit is &le; 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.

scholarly journals ANALISIS MODEL MANAJEMEN PERMINTAAN SCM NETWORK DAN PERAMALAN PERMINTAAN PADA PENJUALAN BUSANA MUSLIM MENGGUNAKAN METODE LINEAR REGRESSION

2021 ◽  
Vol 5 (1) ◽  
pp. 28-32
Author(s):  
Vela Maghfiroh ◽  
◽  
Yusuf Amrozi ◽  
Qushoyyi Bondan Prakoso ◽  
Mochamad Adam Aliansyah
Keyword(s):  
Time Series ◽  
Supply Chain ◽  
Data Analysis ◽  
Linear Regression ◽  
Error Rate ◽  
Moving Average ◽  
Bullwhip Effect ◽  
Lifestyle Changes ◽  
Linear Regression Method ◽  
Analysis Techniques

Supply chain management is very important for a company because it will affect supply performance in the company. Doing business in this era has many challenges that must be faced, especially in the Muslim clothing business. The way to stabilize the demand diagram of the Muslim clothing business, retailers are required to manage the supply chain so that they can meet the total demand. The object of this research is Rabbani Cirebon which was obtained from a literature study published in a journal entitled "Trend of Muslim Lifestyle Changes" from Banjarmasin State Polytechnic. The journal has sales data based on product types from monthly in 2016. From this data will be processed and analyzed using data analysis techniques. This data analysis technique uses time series forecasting data analysis techniques. From this time series method, this research uses moving average and linear regression. After modeling the data, the forecast error is measured using MAD, MAPE, RMSE, and MSE. The overall MSE results were 103731.8 and RMSE 322.0743. The benefit of demand forecasting is to reduce the Bullwhip Effect, plan future resources, for example, such as stock management, place control, product distribution, and demand for raw materials so as to make the right decisions. The results showed that the linear regression method has better forecasting than the moving average because linear regression has a smaller error rate than the moving average. But even so, the error rate of this study is still very large, so it is necessary to do more research to minimize the error rate.

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