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An Improved Demand Forecasting Model Using Deep Learning Approach and Proposed Decision Integration Strategy for Supply Chain
12 references·15 citing·40 second hop
An Improved Demand Forecasting Model Using Deep Learning Approach and Proposed Decision Integration Strategy for Supply Chain (2019)An Improved Demand Forecasting Model Usin…The Role of Big Data and Predictive Analytics in Retailing (2017)The Role of Big Data and Pred…The Future of Retailing (2017)The Future of RetailingOptimal combination forecasts for hierarchical time series (2011)Optimal combination forecasts…Intermittent demand: Linking forecasting to inventory obsolescence (2011)Intermittent demand: Linking …A decision support system for demand forecasting with artificial neural networks and neuro-fuzzy models: A comparative analysis (2008)A decision support system for…The accuracy of intermittent demand estimates (2004)The accuracy of intermittent …Supply Chain Management: Strategy, Planning, and Operation (2003)Supply Chain Management: Stra…A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting (1997)A Decision-Theoretic Generali…Forecasting intermittent demand in manufacturing: a comparative evaluation of Croston's method (1994)Forecasting intermittent dema…Exponential smoothing: The state of the art (1985)Exponential smoothing: The st…Forecasting and Stock Control for Intermittent Demands (1972)Forecasting and Stock Control…Forecasting principles and practiceForecasting principles and pr…MCDFN: supply chain demand forecasting via an explainable multi-channel data fusion network model (2025)MCDFN: supply chain demand fo…Machine learning and artificial intelligence methods and applications for post-crisis supply chain resiliency and recovery (2025)Machine learning and artifici…Supply Chain Inventory Management from the Perspective of “Cloud Supply Chain”—A Data Driven Approach (2024)Supply Chain Inventory Manage…Demand Forecasting in Supply Chain Using Uni-Regression Deep Approximate Forecasting Model (2024)Demand Forecasting in Supply …Big Data—Supply Chain Management Framework for Forecasting: Data Preprocessing and Machine Learning Techniques (2024)Big Data—Supply Chain Managem…A novel attLSTM framework combining the attention mechanism and bidirectional LSTM for demand forecasting (2024)A novel attLSTM framework com…A deep reinforcement learning system for the allocation of epidemic prevention materials based on DDPG (2023)A deep reinforcement learning…Studying key antecedents of disruptive technology adoption in the digital supply chain: an Indian perspective (2022)Studying key antecedents of d…Approaching sales forecasting using recurrent neural networks and transformers (2022)Approaching sales forecasting…Applications of deep learning into supply chain management: a systematic literature review and a framework for future research (2022)Applications of deep learning…A hybrid deep learning framework with CNN and Bi-directional LSTM for store item demand forecasting (2022)A hybrid deep learning framew…Understanding the influential and mediating role of cultural enablers of AI integration to supply chain (2021)Understanding the influential…Sales Prediction and Product Recommendation Model Through User Behavior Analytics (2021)Sales Prediction and Product …Explainability in supply chain operational risk management: A systematic literature review (2021)Explainability in supply chai…Application of stacking ensemble machine learning algorithm in predicting the cost of highway construction projects (2021)Application of stacking ensem…Time Series Analysis: Forecasting and Control (1977)25 years of time series forecasting (2006)DeepAR: Probabilistic forecasting with autoregressive recurrent networks (2019)Production and inventory management (1983)The M3-Competition: results, conclusions and implications (2000)Automatic Time Series Forecasting: The forecast Package for R (2008)Supply chain forecasting: Theory, practice, their gap and the future (2015)Optimal Forecast Reconciliation for Hierarchical and Grouped Time Series Through Trace Minimization (2018)Retail forecasting: Research and practice (2019)Forecasting methods and applications (1993)Supply Chain Management: Strategy, Planning and Operations (2000)Multi-objective optimization of closed-loop supply chains in uncertain environment (2012)A big data driven framework for demand-driven forecasting with effects of marketing-mix variables (2019)Machine-Learning Models for Sales Time Series Forecasting (2019)The accuracy of extrapolation (time series) methods: Results of a forecasting competition (1982)A Survey of Forecast Error Measures (2013)Forecasting Sales by Exponentially Weighted Moving Averages (1960)Time Series Analysis: Forecasting and Control (1977)Forecasting Economic Time Series (2000)A state space framework for automatic forecasting using exponential smoothing methods (2002)Forecasting Time Series With Complex Seasonal Patterns Using Exponential Smoothing (2011)Statistical and Machine Learning forecasting methods: Concerns and ways forward (2018)Customer Experience Creation: Determinants, 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(2012)Big Data : The Management Revolution (2013)Marketing Analytics for Data-Rich Environments (2016)The value of online surveys: a look back and a look ahead (2018)Supply chain collaboration: Impact on collaborative advantage and firm performance (2010)Reverse logistics and closed-loop supply chain: A comprehensive review to explore the future (2014)Business models and supply chains for the circular economy (2018)The Nonstochastic Multiarmed Bandit Problem (2002)Prediction, Learning, and Games (2006)Survey on speech emotion recognition: Features, classification schemes, and databases (2010)A Comparison of ARIMA and LSTM in Forecasting Time Series (2018)The Performance of LSTM and BiLSTM in Forecasting Time Series (2019)Recurrent Neural Networks for Time Series Forecasting: Current status and future directions (2020)
this work references cited by second hop

References

  • 1997A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting20,469 cited
  • n.d.Forecasting principles and practice4,364 cited
  • 2003Supply Chain Management: Strategy, Planning, and Operation2,283 cited
  • 1985Exponential smoothing: The state of the art1,461 cited
  • 2017The Future of Retailing1,192 cited
  • 1972Forecasting and Stock Control for Intermittent Demands761 cited
  • 2017The Role of Big Data and Predictive Analytics in Retailing516 cited
  • 2004The accuracy of intermittent demand estimates498 cited
  • 2011Optimal combination forecasts for hierarchical time series471 cited
  • 2008A decision support system for demand forecasting with artificial neural networks and neuro-fuzzy models: A comparative analysis263 cited
  • 2011Intermittent demand: Linking forecasting to inventory obsolescence255 cited
  • 1994Forecasting intermittent demand in manufacturing: a comparative evaluation of Croston's method236 cited

Cited by

  • 2022A hybrid deep learning framework with CNN and Bi-directional LSTM for store item demand forecasting105 cited
  • 2021Understanding the influential and mediating role of cultural enablers of AI integration to supply chain99 cited

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2022
Applications of deep learning into supply chain management: a systematic literature review and a framework for future research
98 cited
  • 2021Explainability in supply chain operational risk management: A systematic literature review84 cited
  • 2021Application of stacking ensemble machine learning algorithm in predicting the cost of highway construction projects82 cited
  • 2022Approaching sales forecasting using recurrent neural networks and transformers73 cited
  • 2025MCDFN: supply chain demand forecasting via an explainable multi-channel data fusion network model67 cited
  • 2024Big Data—Supply Chain Management Framework for Forecasting: Data Preprocessing and Machine Learning Techniques50 cited
  • 2024Demand Forecasting in Supply Chain Using Uni-Regression Deep Approximate Forecasting Model48 cited
  • 2024A novel attLSTM framework combining the attention mechanism and bidirectional LSTM for demand forecasting44 cited
  • 2022Studying key antecedents of disruptive technology adoption in the digital supply chain: an Indian perspective40 cited
  • 2021Sales Prediction and Product Recommendation Model Through User Behavior Analytics40 cited
  • 2025Machine learning and artificial intelligence methods and applications for post-crisis supply chain resiliency and recovery40 cited
  • 2024Supply Chain Inventory Management from the Perspective of “Cloud Supply Chain”—A Data Driven Approach25 cited
  • 2023A deep reinforcement learning system for the allocation of epidemic prevention materials based on DDPG19 cited