scholarly journals Aplikasi Auto Sales Forecasting Berbasis Computational Intelligence Website untuk Mengoptimalisasi Manajemen Strategi Pemasaran Produk

2019 ◽  
Vol 9 (2) ◽  
pp. 244
Author(s):  
Rizal Bakri ◽  
Umar Data ◽  
Niken Probondani Astuti
Keyword(s):  
Computational Intelligence ◽  
Moving Average ◽  
Capital Adequacy ◽  
Business Analytics ◽  
Product Price ◽  
Product Availability ◽  
Forecasting Method ◽  
Waterfall Model ◽  
R Programming ◽  
Analytical Tools

Business analytics plays an important role in optimizing the management of product marketing strategies. One of the most popular analytical tools in business analytics is sales forecasting. Businesses need to conduct sales forecasting to optimize marketing management in the form of product availability predictions, predictions of capital adequacy, consumer interest, and product price governance. However, the problem that is often encountered in forecasting is the number of forecasting methods available so that it makes it difficult for business people to choose the best forecasting method. The aims of this research is to develop a forecasting software tha can be accessed online based on computational intelligence, which is a software that can make forececasting with various methods and then intelligently choose the best forecasting method. The software development method used in this study is the SDLC with waterfall model. The result of this research is the Auto sales forecasting software was developed using the R programming language by combining various package and can be accessed online through the page Http://bakrizal.com/AutoSalesForecasting. This software can be used to conduct forecast analysis with various methods such as Simple Moving Average, Robust Exponential Smoothing, Auto ARIMA, Artificial Neural Network, Holt-Winters, and Hybrid Forecast. This software contains intelligence computing to choose the best forecasting method based on the smallest RMSE value. After testing the sales transaction data at the Futry Bakery & Cake Shop in Makassar, the results show that the Robust Exponantial Smoothing method is the best forecasting method with an RMSE value of 0.829

scholarly journals Autoregressive Integrated Moving Average (ARIMA) Sebagai Model Peramalan Kasus Demam Berdarah Dengue

2020 ◽  
Vol 10 (2) ◽  
pp. 76-80
Author(s):  
Roro Kushartanti ◽  
Maulina Latifah
Keyword(s):  
Community Health ◽  
Health Center ◽  
Moving Average ◽  
Arima Model ◽  
Community Health Center ◽  
Percentage Error ◽  
Dengue Case ◽  
Monthly Data ◽  
Autoregressive Integrated Moving Average ◽  
Forecasting Method

ARIMA is a forecasting method time series that does not require a specific data pattern. This study aims to analyze the forecasting of Semarang City DHF cases specifically in the Rowosari Community Health Center. The study used monthly data on DHF cases in the Rowosari Community Health Center in 2016, 2017, and 2019 as many as 36 dengue case data. The best ARIMA model for forecasting is a model that meets the requirements for parameter significance, white noise and has the MAPE (Mean Absolute Percentage Error Smallest) value. The results of the analysis show that the best model for predicting the number of dengue cases in the Rowosari Public Health Center Semarang is the ARIMA model (1,0,0) with a MAPE value of 43.98% and a significance coefficient of 0.353, meaning that this model is suitable and feasible to be used as a forecasting model. DHF cases in the Rowosari Community Health Center in Semarang City.

scholarly journals Sales Analysis Using the Forecasting Method

bit-Tech ◽  
2019 ◽  
Vol 1 (3) ◽  
pp. 146-149
Author(s):  
Amesanggeng Pataropura ◽  
Riki Riki ◽  
Ariadi Saputra
Keyword(s):  
Current Flow ◽  
Current System ◽  
Moving Average ◽  
Scientific Work ◽  
Transaction Data ◽  
Forecasting Method ◽  
Effectiveness And Efficiency ◽  
To Come ◽  
New System ◽  
Weighted Moving Average

Sales Analysis Using Forecasting Method aims to improve effectiveness and efficiency that facilitates companies in business transaction processes, improve the delivery of information quickly, accurately, and transaction data well and minimize errors. The method used in the presentation of this scientific work is by using a forecasting method which helps determine the approximate stock of goods to come. With 3 forecasting modules are: Moving Average, Weighted Moving Average, Trend Projection is used to perform the forecasting process of the upcoming stock of goods. Can solve problems that exist in the current system so that it can help in improving its services by calculating the stock and helping by determining the average data that has been linked to the forecasting module whose results can be concluded through reports per period. It can be concluded that the results of implementing this new system can help companies in recording each transaction that occurs becomes more efficient and effective, so that it can overcome the problems that exist in the current system. With this we can predict the current flow of goods that have been calculated based on 3 (three) modules that have connections with the system

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.

scholarly journals CREDIT POLICY OF A COMMERCIAL BANK IN CONDITIONS OF UNCERTAINTY OF THE ECONOMIC ENVIRONMENT

2021 ◽  
Vol 5 (40) ◽  
pp. 65-75
Author(s):  
О. Kovalova ◽  
M. Iorgachova
Keyword(s):  
Commercial Bank ◽  
Financial Services ◽  
Economic Security ◽  
Capital Adequacy ◽  
Economic Environment ◽  
Credit Policy ◽  
Holistic View ◽  
General Decline ◽  
Banking Institutions ◽  
Analytical Tools

Abstract. The article examines the concept of «credit policy» through the prism of the functions outlined in the development strategy of the banking institution, which is primarily aimed at maximizing resources for rapid response to uncertainties of the external and internal environment. The complex of identified key characteristics allowed to form a holistic view of the nature and specific features of the commercial bank’s credit policy. The article underlines that in conditions of uncertainty, the bank should take into account external and internal environmental factors that have a direct impact on the dynamics of lending to individuals and businesses, and need to be considered at the stage of strategic financial planning, monitoring the implementation of set tasks and in order to timely adjust the policy according to the banking institution’s needs for financial and economic security. The systematised factors are interrelated and interdependent and can have a multifaceted impact on the current state of the credit services of banking institutions’ market and such market’s trends. The article establishes the role of analysis and monitoring of banking institutions’ credit operations in the context of ensuring the effective results of the commercial bank’s credit policy. It is identified that credit risk is now in the spotlight of banking institutions along with the risks of capital adequacy and legal risks, due to the general decline in economic activity of consumers of financial services as a result of the epidemiological crisis and the introduction of quarantine restrictions, which reduced household incomes and negatively affected the financial situation of enterprises. The dynamics of changes in the share of NPLs by groups of banks is analysed, revealing foreign banks’ active work with NPLs. The study pays attention to the use of analytical tools of the commercial bank’s credit policy as a means of managing its financial and economic security in conditions of uncertainty, which concerns constant monitoring of loan portfolio quality and timely detection of NPLs. It also suggests the sequence of lending steps specifying practical aspects of use of analytical tools of credit policy. Keywords: credit policy, commercial bank, management, uncertainty, economic environment, analytical tools, financial market. JEL Classification E51, G21 Formulas: 0; fig.: 7; tabl.: 0; bibl.: 14.

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.

A Forecasting Method for Fertilizers Consumption in Brazil

2013 ◽  
Vol 4 (2) ◽  
pp. 23-36 ◽  
Author(s):  
Eduardo Ogasawara ◽  
Daniel de Oliveira ◽  
Fabio Paschoal Junior ◽  
Rafael Castaneda ◽  
Myrna Amorim ◽  
...  
Keyword(s):  
Large Scale ◽  
Moving Average ◽  
Logistic Function ◽  
Autoregressive Integrated Moving Average ◽  
Forecasting Method ◽  
The World ◽  
Entire Country ◽  
Function Models ◽  
Nitrogen Phosphorus

Tracking information about fertilizers consumption in the world is very important since they are used to produce agriculture commodities. Brazil consumes a large amount of fertilizers due to its large-scale agriculture fields. Most of these fertilizers are currently imported. The analysis of consumption of major fertilizers, such as Nitrogen-Phosphorus-Potassium (NPK), Sulfur, Phosphate Rock, Potash, and Nitrogen become critical for long-term government decisions. In this paper we present a method for fertilizers consumption forecasting based on both Autoregressive Integrated Moving Average (ARIMA) and logistic function models. Our method was used to forecast fertilizers consumption in Brazil for the next 20 years considering different economic growth for the entire country.

scholarly journals Seasonal Time Series Forecasting by F1-Fuzzy Transform

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3611 ◽  
Author(s):  
Di Martino ◽  
Sessa
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Prediction Method ◽  
Heat Index ◽  
Weather Data ◽  
Fuzzy Transform ◽  
Campania Region ◽  
Seasonal Time Series ◽  
Forecasting Method ◽  
Daily Weather Data

We present a new seasonal forecasting method based on F1-transform (fuzzy transform of order 1) applied on weather datasets. The objective of this research is to improve the performances of the fuzzy transform-based prediction method applied to seasonal time series. The time series’ trend is obtained via polynomial fitting: then, the dataset is partitioned in S seasonal subsets and the direct F1-transform components for each seasonal subset are calculated as well. The inverse F1-transforms are used to predict the value of the weather parameter in the future. We test our method on heat index datasets obtained from daily weather data measured from weather stations of the Campania Region (Italy) during the months of July and August from 2003 to 2017. We compare the results obtained with the statistics Autoregressive Integrated Moving Average (ARIMA), Automatic Design of Artificial Neural Networks (ADANN), and the seasonal F-transform methods, showing that the best results are just given by our approach.

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 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.

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