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A predictive analytics approach for demand forecasting in the process industry
16 references·7 citing·29 second hop
A predictive analytics approach for demand forecasting in the process industry (2014)A predictive analytics approach for deman…Effective supply chain management (2020)Effective supply chain manage…“Supply Chain 2.0”: managing supply chains in the era of turbulence (2011)“Supply Chain 2.0”: managing …Effective forecasting and judgmental adjustments: an empirical evaluation and strategies for improvement in supply-chain planning (2009)Effective forecasting and jud…Exponential smoothing: The state of the art—Part II (2006)Exponential smoothing: The st…25 years of time series forecasting (2006)25 years of time series forec…Forecasting seasonals and trends by exponentially weighted moving averages (2004)Forecasting seasonals and tre…Identifying sources of uncertainty to generate supply chain redesign strategies (2002)Identifying sources of uncert…A state space framework for automatic forecasting using exponential smoothing methods (2002)A state space framework for a…Comparing Predictive Accuracy (1994)Comparing Predictive AccuracyTime-based competition: the next battleground in American manufacturing (1991)Time-based competition: the n…Forecasting Trends in Time Series (1985)Forecasting Trends in Time Se…Exponential smoothing: The state of the art (1985)Exponential smoothing: The st…Designing Complex Organizations (1973)Designing Complex Organizatio…Time Series Analysis Forecasting and Control (1971)Time Series Analysis Forecast…Forecasting Sales by Exponentially Weighted Moving Averages (1960)Forecasting Sales by Exponent…Economic Control of Quality of Manufactured Product. (1932)Economic Control of Quality o…Not So Averse After All: Behavioral Mechanisms Underlying the Use of Algorithms in Managerial Forecasting (2025)Not So Averse After All: Beha…Forecasting supply chain disruptions in the textile industry using machine learning: A case study (2024)Forecasting supply chain disr…Controllership effectiveness and digitalization: Shedding light on the importance of business analytics capabilities and the business partner role (2024)Controllership effectiveness …A novel attLSTM framework combining the attention mechanism and bidirectional LSTM for demand forecasting (2024)A novel attLSTM framework com…Big Data Analytics-Based Approach for Robust, Flexible and Sustainable Collaborative Networked Enterprises (2022)Big Data Analytics-Based Appr…A Systematic Investigation of the Integration of Machine Learning into Supply Chain Risk Management (2021)A Systematic Investigation of…A new method for measuring process flexibility of product design (2016)A new method for measuring pr…The M3-Competition: results, conclusions and implications (2000)A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting (2019)Artificial intelligence applications in supply chain management (2021)Forecasting: theory and practice (2022)Time Series Analysis: Forecasting and Control (1977)Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation (1982)Co-Integration and Error Correction: Representation, Estimation, and Testing (1987)On the use of cross-validation for time series predictor evaluation (2012)Stock price prediction using LSTM, RNN and CNN-sliding window model (2017)Time series forecasting of petroleum production using deep LSTM recurrent networks (2018)Time Series Analysis: Forecasting and Control (1977)Forecasting Economic Time Series (2000)Forecasting Time Series With Complex Seasonal Patterns Using Exponential Smoothing (2011)Statistical and Machine Learning forecasting methods: Concerns and ways forward (2018)The accuracy of extrapolation (time series) methods: Results of a forecasting competition (1982)Forecasting methods and applications (1993)The M4 Competition: 100,000 time series and 61 forecasting methods (2019)Political Parties and Macroeconomic Policy (1977)Seasonal integration and cointegration (1990)A new metric of absolute percentage error for intermittent demand forecasts (2016)Procedures for Reacting to a Change in Distribution (1971)Statistical process control as a tool for research and healthcare improvement (2003)Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications (2014)Answering the Skeptics: Yes, Standard Volatility Models do Provide Accurate Forecasts (1998)A Comprehensive Look at The Empirical Performance of Equity Premium Prediction (2007)Strictly Proper Scoring Rules, Prediction, and Estimation (2007)Organizational Information Requirements, Media Richness and Structural Design (1986)Toward a knowledge‐based theory of the firm (1996)Social Capital, Intellectual Capital, and the Organizational Advantage (1998)
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References

  • 1994Comparing Predictive Accuracy5,495 cited
  • 1973Designing Complex Organizations5,427 cited
  • 1932Economic Control of Quality of Manufactured Product.3,069 cited
  • 1960Forecasting Sales by Exponentially Weighted Moving Averages2,117 cited
  • 1971Time Series Analysis Forecasting and Control1,792 cited
  • 2004Forecasting seasonals and trends by exponentially weighted moving averages1,732 cited
  • 200625 years of time series forecasting1,589 cited
  • 1985Exponential smoothing: The state of the art1,461 cited
  • 2002A state space framework for automatic forecasting using exponential smoothing methods1,074 cited
  • 2006Exponential smoothing: The state of the art—Part II953 cited
  • 2020Effective supply chain management898 cited
  • 2011“Supply Chain 2.0”: managing supply chains in the era of turbulence764 cited
  • 2002Identifying sources of uncertainty to generate supply chain redesign strategies600 cited
  • 2009Effective forecasting and judgmental adjustments: an empirical evaluation and strategies for improvement in supply-chain planning447 cited
  • 1985Forecasting Trends in Time Series399 cited

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1991
Time-based competition: the next battleground in American manufacturing
359 cited

Cited by

  • 2021A Systematic Investigation of the Integration of Machine Learning into Supply Chain Risk Management82 cited
  • 2024A novel attLSTM framework combining the attention mechanism and bidirectional LSTM for demand forecasting44 cited
  • 2024Forecasting supply chain disruptions in the textile industry using machine learning: A case study40 cited
  • 2022Big Data Analytics-Based Approach for Robust, Flexible and Sustainable Collaborative Networked Enterprises30 cited
  • 2024Controllership effectiveness and digitalization: Shedding light on the importance of business analytics capabilities and the business partner role26 cited
  • 2016A new method for measuring process flexibility of product design14 cited
  • 2025Not So Averse After All: Behavioral Mechanisms Underlying the Use of Algorithms in Managerial Forecasting1 cited