Application of Artificial Neural Network and SARIMA in Portland Cement Supply Chain to Forecast Demand

Liu, Pei; Chen, Shih-Huang; Yang, Hui Hua; Hung, Ching-Tsung; Tsai, Mei-Rong

doi:10.1109/icnc.2008.893

Cited by 11 publications

(5 citation statements)

References 4 publications

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“…The topic is "Comparative ANN and SARIMA in Portland cement supply chain to forecast demand". From this research the researchers had the conclusion of ANN was better than SARIMA they took the single demand data from period January 2004 to March 2005, [10]. The title of "A decision support system to forecast cement demand".…”

Section: Discussionmentioning

confidence: 99%

“…The ANN usually used for specific application in pattern recognition or data classification by process of learning system. ANN is able to solve linearity and nonlinearities, discontinues which capability of adaptively and learning process inside, [9] Application in Portland cement for forecast demand as well, [10]. The purpose of this research is to explore some objectives, such as predict some months in the future situation and its performance.…”

Section: Introductionmentioning

confidence: 99%

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Forecasting Determinant of Cement Demand in Indonesia with Artificial Neural Network

Fradinata

Suthummanon

Suntiamorntut

2015

Journal of Asian Scientific Research

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This paper was presented Artificial Neural Network (ANN) as one of the predicting methods to obtain more accurate predicted data. Several methods have been applied to this purpose but still not gave a better accuracy. The method could be used in linear and nonlinear characteristic of data. Back propagation neural network was implemented in this experiment to solve the predicting problem. This research proposed to predict some future points to get the advantages from it. The data was demonstrated in generate from determinant of cement demand in Indonesia region. The data was used 6 variables which influenced the demand factors. The contribution of this experiment was an exploring the accuracy of predicted and the future points value of data. The result of this experiment was: data A, B, C, D, E and F data had the range of MSE 6.23e-9 until 1.34e-7. The MSE for the actual data was 2.39e-6 and the predicted was 1.99e-6. The predicting points has resulted on months 91 until 97 were 0.5854, 0.8448, 0.510, 0.6462, 0.8528, 0.516 and 0.5074 respectively. The delta between predicted and actual data were 0

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Forecasting Determinant of Cement Demand in Indonesia with Artificial Neural Network

Fradinata

Suthummanon

Suntiamorntut

2015

Journal of Asian Scientific Research

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“…In Malaysia a backpropagation neural network was used to forecast the cement price index [16]. Also, different forecasting models are described in [17], favoring the use of artificial neural networks (ANN) based models. By using the multilayer perceptron (MLP) neural network model, derived from real plant data of Iranian cement factory, a kiln process simulator has been developed to track a desired future reference trajectory and to minimize the cost function [18].…”

Section: Methodsmentioning

confidence: 99%

Net fitting based production planning and decision support system for energy intensive industries

Pušnik

Sučić

Podgornik

et al. 2014

2014 IEEE International Energy Conference (ENERGYCON)

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In recent years significant research efforts have been related with the energy efficiency in energy intensive industries. It is promising that EU has already demonstrated how much can be done in reducing the energy intensity of manufacturing processes through energy efficiency and sustainable production processes. The overall objective of any energy efficiency related program or project in industry is reduction of costs, energy consumption, environmental impacts and raw material use. The research work presented in this paper is related to long term production planning and decision support system based on the artificial neural network net fitting model. To tackle these challenges a system for simulation and analysis of future situations relevant for production planning in energy intensive industries has been developed. It enables optimal response on excessive energy consumption and costs in relation to fuel changes and environmental impacts. The results presented in this paper confirm that the addressed system has the potential to be applied in energy intensive industries, taking into account plant specific input parameters and context variables.

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“…Based on the previous study in [6], the exchange rate was built a model using neural networks (NN). Combine methodology in time series using ARIMA and ANN conducted by [7] and [8]. They compare both models to know the best one.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Model of Seasonal ARIMA-ANN to Forecast Tourist Arrivals through Minangkabau International Airport

Yollanda¹,

Devianto²

2020

Proceedings of the Proceedings of the 1st International Conference on Statistics and Analytics, ICSA 2019, 2-3 August 2019, Bog

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The number of tourist arrivals forecasting is required for the future development of tourism industry to improve the economic growth. The number tourist arrivals data can be analyzed by building a model so that it will help to find out the number of tourist arrivals in the next period which is through Minangkabau International Airport. The linear model that is used is Seasonal Autoregressive Integrated Moving Average (SARIMA) used and continued to build a nonlinear model of the residual SARIMA model using Artificial Neural Network (ANN). In this research, SARIMA model which obtained is SARIMA (1, 0, 1) (1, 1, 0) 12. But, residual of SARIMA model has not been fulfilled an autocorrelation assumption so that it isproposed a new model of SARIMA-ANN. The residual model of SARIMA is built using the ANN model architecture with 2-2-2-1 network topology. The performance rate of time series model of tourist arrivals which is the data started on January 2012 until March 2019 is measured using Mean Absolute Percentage Error (MAPE). Based on the MAPE value of 17.1770% indicates that the model obtained having good performance to forecast the number of tourist arrivals through Minangkabau International Airport in the future.

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Application of Artificial Neural Network and SARIMA in Portland Cement Supply Chain to Forecast Demand

Cited by 11 publications

References 4 publications

Forecasting Determinant of Cement Demand in Indonesia with Artificial Neural Network

Forecasting Determinant of Cement Demand in Indonesia with Artificial Neural Network

Net fitting based production planning and decision support system for energy intensive industries

Hybrid Model of Seasonal ARIMA-ANN to Forecast Tourist Arrivals through Minangkabau International Airport

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