scholarly journals Improved time series electricity sales forecast based on economic prosperity method

2019 ◽  
Vol 267 ◽  
pp. 062042
Author(s):  
Zhilong Yang ◽  
Yaming Liu ◽  
Guanghua Wu ◽  
Xuejun Li ◽  
Qinghua Xu
Keyword(s):  
Time Series ◽  
Economic Prosperity ◽  
Sales Forecast

scholarly journals Sales forecasting in a mechanical component manufacturer: comparison between monte carlo simulation and time series analysis

2019 ◽  
Vol 10 (4) ◽  
pp. 1324
Author(s):  
Kevin William Matos Paixão ◽  
Adriano Maniçoba da Silva
Keyword(s):  
Decision Making ◽  
Time Series ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Moving Average ◽  
Prediction Methods ◽  
Future Scenario ◽  
Sales Forecast ◽  
Forecasting Methods ◽  
Weighted Moving Average

Organizations today are required to be prepared for future situations. This preparation can generate a significant competitive advantage. In order to maximize benefits, several companies are investing more in techniques that simulate a future scenario and enable more precise and assertive decision making. Among these techniques are the sales forecasting methods. The comparison between the known techniques is an important factor to increase the assertiveness of the forecast. The objective of this study was to compare the sales forecast results of a mechanical components manufacturing company obtained through five different techniques, divided into two groups, the first one, which uses the fundamentals of the time series, and the second one is the Monte Carlo simulation. The following prediction methods were compared: moving average, weighted moving average, least squares, holt winter and Monte Carlo simulation. The results indicated that the methods that obtained the best performance were the moving average and the weighted moving average attaining 94% accuracy.

scholarly journals Model Hybrid for Sales Forecast for the Housing Market of São Paulo

2020 ◽  
Vol 28 (3) ◽  
pp. 45-64
Author(s):  
Matheus Fernando Moro ◽  
Andreas Dittmar Weise ◽  
Antonio Cezar Bornia
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Geometric Mean ◽  
Principal Component ◽  
Sao Paulo ◽  
Forecast Combination ◽  
São Paulo ◽  
Forecast Performance ◽  
Sales Forecast ◽  
Residential Units

AbstractThis research proposes a combined model of time series for forecasting housing sales in the city of São Paulo. We used data referring to the time series of sales of residential units provided by SECOVI-SP. The Exponential Softening, Box-Jenkins and Artificial Neural Networks models are individually modelled, later these are combined through five forecast combination techniques.The techniques used are Arithmetic Mean, Geometric Mean, Harmonic Mean, Linear Regression and Principal Component Analysis. The measures of accuracy to measure the results obtained and to select the best model are the RMSE, MAPE and UTheil of forecast. The results showed that Linear Regression with an independent variable, being a combination of the SARIMA model (2,0,0)(2,0,0)12 and MLP/RNA (12,10,1), provided a satisfactory performance, with an RMSE of 368.74, MAPE of 19.2% and UTheil of 0.315.The combination of time series models allowed a significant increase in forecast performance. Finally, the model was validated, using it to predict housing sales. The results show that the model has a good fit, thus demonstrating that using a housing sales forecasting model helps industry professionals minimize error and make sales and launch decisions.

scholarly journals Combining Qualitative and Time Series Forecasting to Increase the Forecasting Accuracy for Instant Noodle Sales in Thailand

2016 ◽  
Vol 3 (3) ◽  
pp. 1
Author(s):  
Teerada Khamphinit ◽  
Pornthipa Ongkunaruk
Keyword(s):  
Time Series ◽  
Demand Forecasting ◽  
Forecast Accuracy ◽  
Time Series Forecasting ◽  
Forecasting Accuracy ◽  
Sales Data ◽  
Average Percentage ◽  
Sales Forecast ◽  
Instant Noodle ◽  
Instant Noodles

<p>Demand forecasting is very important for the planning process. The forecast accuracy affects the efficiency of the procurement, production and delivery processes. Our research has the objective of increasing the sales forecasting accuracy of instant noodles for a case study company in Thailand. Many factors affect the sales of instant noodles, such as promotion, other commodities’ prices, national disaster and production capacity. Thus, we collected historical monthly sales data, analysed the data and their pattern and considered whether the data were irregular due to those factors. After obtaining the forecast data, data intervention by adjustment of the irregular effects was performed in accordance with our experience and judgement. Next, we used the predictor function in the Crystal Ball software to determine the best time series forecasting method for actual and adjusted sales data. Then, we verified the result with the actual sales data for one year. The result showed that the adjustment could increase the sales forecast accuracy by 46.14%, 22.53% and 56.42% for products A, B and C, respectively. In summary, the mean average percentage sales forecast error after adjustment was 6.48%–11.62%, which is better than the current method of forecasting based on experts.  </p><p><strong>Keywords</strong>: Instant Noodle; Intervention; Qualitative Forecasting; Sales Adjustment; Time Ser ies Forecasting </p>

A conceptual model for automation of product dynamic pricing and sales promotion for a retail organization

Kybernetes ◽  
2016 ◽  
Vol 45 (3) ◽  
pp. 490-507 ◽  
Author(s):  
Anup Kumar
Keyword(s):  
Time Series ◽  
Fuzzy Logic ◽  
Time Series Data ◽  
Lot Sizing ◽  
Product Category ◽  
Sales Promotion ◽  
Series Data ◽  
Valuable Insight ◽  
Content Type ◽  
Sales Forecast

Purpose – The purpose of this paper is to capture the dynamic variations in sales of a product based upon the dynamic estimation of the time series data and propose a model that imitates the price discounting and promotion strategy for a product category in a retail organization. A modest attempt has been made in the study to capture the relationship between the sales promotion, price discount and the batch procurement strategy of a particular product category to maximize sales volume and profitability. Design/methodology/approach – Time series data relating to sales have been used to model the sales estimates using moving average and proportional and derivative control; thereafter a sales forecast is generated to estimate the sales of a particular product category. This provides valuable inputs for taking lot sizing decisions regarding procurement of the products that considerably impact the sales promotion and intelligent pricing decisions. A conceptual framework is developed for modeling the dynamic price discounting strategy in retail using fuzzy logic. Findings – The model captures the lag effect of sales promotion and price discounting strategy; other strategies have been formulated based upon the sales forecast that was done for taking the lot sizing decisions regarding procurement of products in the selected category. This has helped minimize the inventory cost thereby keeping the profitability of the retail organization intact. Research limitations/implications – There is no appropriate empirical data to verify the models. In light of the research approach (modeling based upon historical time series data of a particular product category) that was undertaken, there is a possibility that the research results may be valid for the product category that was selected. Therefore, the researchers are advised to test the proposed propositions further for other product categories. Originality/value – The study provides valuable insight on how to use the real-time sales data for designing a dynamic automated model for product sales promotion and price discounting strategy using fuzzy logic for a retail organization.

scholarly journals Application of the Varma Model for Sales Forecast: Case of Urmia Gray Cement Factory

2014 ◽  
Vol 7 (1) ◽  
pp. 89-101
Author(s):  
Ramin Bashir Khodaparasti ◽  
Samad Moslehi
Keyword(s):  
Time Series ◽  
Direct Relationship ◽  
Multivariate Time Series ◽  
Time Series Model ◽  
Sales Volume ◽  
Sales Forecast ◽  
Independent Variables ◽  
Study Results ◽  
Cement Factories ◽  
Two Independent Variables

Abstract To forecast sales as reliably as possible is one of the most important issues in every business trade. Therefore, in recent years different models have been suggested to deal with this issue. One efficient model is the time series model. This study applies a multivariate time series model to forecast Urmia Gray Cement Factory's sales volume and more importantly, to propose an effective model to be used by other cement factories to predict their sales volume. The two independent variables of costs and revenues and the dependent variable of sales were used in the present study. Results of the study indicated the two independent variables had a positive and direct relationship with sales volume forecast.

scholarly journals AKURANSI ARIMA DALAM PERAMALAN INFLASI KOTA BANDUNG

2017 ◽  
Vol 7 (2) ◽  
Author(s):  
Muhamad Nawawi
Keyword(s):  
Time Series ◽  
Economic Development ◽  
Inflation Rate ◽  
Time Series Data ◽  
Series Data ◽  
Percentage Error ◽  
Economic Prosperity ◽  
Economic Stability ◽  
Forecast Period ◽  
Development Indicator

Inflation is as one of economic development indicator has an important role to public’s economic prosperity in each country. Inflation rate controlling should be running in order to economic stability .This research aims to forecast inflation of Bandung City using ARIMA Method is suitable for time series data such as inflation rate. Researcher taken data as much as 10 years of inflation rate of Bandung City from January 2006 to December 2015 to forecast inflation rate on next 12 months. The tool using Minitab version 15.0.Result of research shows that sum of data did not influence on accuracy of forecast. It seen on Mean Absolut Percentage Error (MAPE) number tend to not patterned. It is showed on the smallest MAPE value (0,0082) in Forecasting number IV (using 84 data). Moreover, ARIMA shows that longer forecast period is linear to more deviated forecast.

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