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An Improved Demand Forecasting Model Using Deep Learning Approach and Proposed Decision Integration Strategy for Supply Chain
2019
Complexity
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For this purpose, historical data can be analyzed to improve demand forecasting by using various methods like machine learning techniques, time series analysis, and deep learning models. ...
This improved model is based on the analysis and interpretation of the historical data by using different forecasting methods which include time series analysis techniques, support vector regression algorithm ...
Acknowledgments
This work is supported by OBASE Research & Development Center. ...
doi:10.1155/2019/9067367
fatcat:csf36po36zfk5mzxb2l4p45soe
Demand Forecasting Model using Deep Learning Methods for Supply Chain Management 4.0
2022
International Journal of Advanced Computer Science and Applications
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This study is carried out in order to improve the performance of the demand forecasting system of the SC based on Deep Learning methods, including Auto-Regressive Integrated Moving Average (ARIMA) and ...
Artificial Intelligence (AI) can consume this data in order to allow each actor in the SC to gain in performance but also to better know and understand the customer. ...
As perspective of this study, we will propose a Hybrid forecasting model based on ARIMA and LSTM to enhance the performance of our predicting system and improve the accuracy of the results. ...
doi:10.14569/ijacsa.2022.0130581
fatcat:fweiehr4zbaafdnpasygqsalnu
Fuzzy Adaptive ELM Approach for Supply Chain Management
2020
Zenodo
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Scholar Department of Mechanical Engineering Sagar Institute of Research and Technology (SIRT) Bhopal, India ...
methods, and time series. ...
[3] this purpose, historical data can be analyzed to improve demand forecasting by using various methods like machine learning techniques, time series analysis, and deep learning models. ...
doi:10.5281/zenodo.4497780
fatcat:iwvjynkcejgktcchcacdtwrhna
Comparative analysis of short-term demand predicting models using ARIMA and deep learning
2021
International Journal of Power Electronics and Drive Systems (IJPEDS)
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As a solution to those logistics issues, this paper presents a comparative analysis of four time series demand forecasting models; namely, the autoregressive integrated moving Average (ARIMA) a statistical ...
The experimentations are carried out using a real-life dataset provided by a supermarket in Morocco. ...
Artificial neural networks (ANN) are an artificial intelligence method inspired from the functioning of the biological neural networks characterized by a circuit of interconnected neurons organized as ...
doi:10.11591/ijece.v11i4.pp3319-3328
fatcat:mfjct7mecbajvnl2r7hbaawsp4
Forecasting the Number of End-of-Life Vehicles: State of the Art Report
2022
Proceedings of the Design Society
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Therefore, a comprehensive state-of-the-art review, evaluating ELVs quantity forecasting methodologies and summarizing the main variables influencing forecasting outcomes, is conducted to throw shed light ...
AbstractAcademics and practitioners have shown a growing interest in automobile reverse supply chain (RSC) management as a result of the rise of circular economy and the development of Industry 4.0. ...
Intelligent forecasting methods (IM) Intelligent forecasting is a method for predicting the number of ELVs using artificial intelligent (AI) technology. ...
doi:10.1017/pds.2022.119
fatcat:sibrt4nwcnciboccyenkzb27e4
Review on Trends in Machine Learning Applied to Demand & Sales Forecasting
2019
SMART MOVES JOURNAL IJOSCIENCE
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In this paper a review is presented in which this problem is tried to solved by using various demand forecasting models to predict product demand for grocery items with machine learning techniques. ...
From the beginning, the main functions of SCM were the management of purchases and purchases, but subsequently SCM took the integrated form i.e. consists of sourcing, materials management, production support ...
CONCLUSION For the optimum balancing the supply chain in demand and sale better forecasting is important factor and by using Artificial Intelligence. ...
doi:10.24113/ijoscience.v5i6.244
fatcat:23zippgj3bg4rmwhjl6c4bobsy
The LSTM technique for demand forecasting of e-procurement in the hospitality industry in the UAE
2020
IAES International Journal of Artificial Intelligence (IJ-AI)
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LSTM time series analysis is considered the most suitable technique for demand forecasting to optimize e-procurement. ...
Demand forecasting for spend and quantity is done using the LSTM technique in e-procurement within the hospitality industry in the UAE. ...
The study done by [10] on forecasting economic and financial time series in 2018 depicts that the LSTM algorithm is superior and outperform traditionalbased other time series algorithm as the error reduction ...
doi:10.11591/ijai.v9.i4.pp757-765
fatcat:epcaxn2qurc7neojuunjnncmt4
Business Intelligence Design for Data Visualization and Drug Stock Forecasting
2021
Intelmatics
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While the visualization of drug stock data and forecast results is done using Microsoft Power BI (Business Intelligence) tools and for forecasting is done with Artificial Neural Network method by RStudio ...
The results of forecasting the amount of stock out of drug samples using the Artificial Neural Network method obtained an MSE value of 67.72 and RMSE 8.22 which means that this forecast has a good ability ...
The forecasting method used is the time series method. Time series is a quantitative method by utilizing historical data sets to predict future data. ...
doi:10.25105/itm.v1i1.7407
fatcat:gggbv2pk7nhpbcoy4jaf6jvnb4
Big Data Technology in the Macrodecision-Making Model of Regional Industrial Economic Information Applied Research
2022
Computational Intelligence and Neuroscience
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Using data mining technology, time series data analysis methods combined with artificial intelligence analysis, the development trend of regional industries is obtained, and then the development trend ...
and future trend of the industrial economy in a timely and effective manner. ...
Acknowledgments is work was supported by the Shengda Trade Economics and Management College of Zhengzhou. ...
doi:10.1155/2022/7400797
pmid:35898787
pmcid:PMC9313906
fatcat:o7poyrbsbzgrpibw427guhbyga
A survey of research progress and hot front of natural gas load forecasting from technical perspective
2020
IEEE Access
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With economic development and scientific and technological progress, people's requirements for the ecological environment are increasing day by day. ...
and combination prediction method, which are also the direction of development in the future. ...
ACKNOWLEDGEMENT This work was supported by The National Natural Science Foundation of China(No. 62072363). The authors are grateful for the help in writing this article by Yu Zhao and Hongbin Dai. ...
doi:10.1109/access.2020.3044052
fatcat:635dluiv5rg3dgyo3fvdqyyxzq
A Comparison of Time Series Model Forecasting Methods on Patent Groups
2015
Midwest Artificial Intelligence and Cognitive Science Conference
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Cross validation methods were used to determine the best fitting models and ultimately whether or not patent data could be modeled as a time series. ...
This paper aims to demonstrate that it may be possible to create technology forecasting models through the use of patent groups. ...
Two univariate time series forecasting models will be applied to each series of patents, Exponential Smoothing and Autoregressive Integrated Moving Averages (ARIMA). ...
dblp:conf/maics/SmithA15
fatcat:wadz2qxxwrb5rh37lnchtz5pvy
Financial modeling trends for production companies in the context of Industry 4.0
2021
Investment Management & Financial Innovations
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This paper aims to define and summarize the main financial/economic forecasting methods for production companies in the context of Industry 4.0. ...
Over the years, technological progress has accelerated highly, and the speed, flexibility, human error reduction, and the ability to manage the process in real time have become more critical and required ...
Robots and artificial intelligence integrated into systems logically suggest autonomy. ...
doi:10.21511/imfi.18(1).2021.23
fatcat:2wqvotllx5glzmmxw4qjwwkd5a
Hybrid intelligent system for Sale Forecasting using Delphi and adaptive Fuzzy Back-Propagation Neural Networks
2012
International Journal of Advanced Computer Science and Applications
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Since most sales data are non-linear in relation and complex, many studies tend to apply Hybrid models to time-series forecasting. ...
The proposed model is constructed to integrate expert judgments, using Delphi method, in enhancing the model of FCBPN. ...
Hybrid intelligent system denotes system that utilizes a parallel combination of methods and techniques from artificial intelligence. ...
doi:10.14569/ijacsa.2012.031120
fatcat:vmct3gawrfaxzcvzigjf6f2nri
A Computing Model of Artificial Intelligent Approaches to Mid-term Load Forecasting: a state-of-the-art- survey for the researcher
2010
International Journal of Engineering and Technology
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Principle, strategy and results of short term, midterm, and long term load forecasting using statistic methods and artificial intelligence technology (AI) are summaried, Which, comparison between each ...
method and the articles have difference feature input and strategy. ...
Artificial Intelligence technology methods and other method for load forecasting 1) Expert systems (Es) Article [34] , proposes neuro-expert for electric mid term load forecasting. ...
doi:10.7763/ijet.2010.v2.106
fatcat:udyawp6lcja6bbb6qt6rorpu6u
Different Applications of Artificial Intelligence to Combat Climate Change Issues
2022
International Journal of Advanced Trends in Computer Science and Engineering
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Artificial intelligence (AI) is the most famous technology in recent decades providing solutions to very complex issues facing human beings. ...
This paper discusses the several applications of AI technology to provide the solutions and monitors to protect the environment, management of wastewater, reduce air pollution, climate forecasting, and ...
CONCLUSION This article provides an overview of the various applications of artificial intelligence and machine learning techniques for the purpose of monitoring and combating climate change problems. ...
doi:10.30534/ijatcse/2022/041122022
fatcat:b465anxrt5a3jjh25aeuds22ta
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