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On the use of machine learning in supply chain management: a systematic review
16 references·0 citing·51 second hop
On the use of machine learning in supply chain management: a systematic review (2023)On the use of machine learning in supply …M5 accuracy competition: Results, findings, and conclusions (2022)M5 accuracy competition: Resu…A review on reinforcement learning algorithms and applications in supply chain management (2022)A review on reinforcement lea…Supply chain disruptions and resilience: a major review and future research agenda (2021)Supply chain disruptions and …Artificial intelligence in supply chain management: A systematic literature review (2020)Artificial intelligence in su…The M4 Competition: 100,000 time series and 61 forecasting methods (2019)The M4 Competition: 100,000 t…From Predictive to Prescriptive Analytics (2019)From Predictive to Prescripti…The Big Data Newsvendor: Practical Insights from Machine Learning (2018)The Big Data Newsvendor: Prac…Analytics for an Online Retailer: Demand Forecasting and Price Optimization (2015)Analytics for an Online Retai…Integration of artificial neural network and MADA methods for green supplier selection (2010)Integration of artificial neu…From a literature review to a conceptual framework for sustainable supply chain management (2008)From a literature review to a…Application of machine learning techniques for supply chain demand forecasting (2007)Application of machine learni…The accuracy of intermittent demand estimates (2004)The accuracy of intermittent …On the bias of intermittent demand estimates (2001)On the bias of intermittent d…The M3-Competition: results, conclusions and implications (2000)The M3-Competition: results, …The accuracy of extrapolation (time series) methods: Results of a forecasting competition (1982)The accuracy of extrapolation…Forecasting and Stock Control for Intermittent Demands (1972)Forecasting and Stock Control…A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting (1997)Big data: the management revolution. (2012)Impact of business analytics and enterprise systems on managerial accounting (2017)Smart “Predict, then Optimize” (2021)Frameworks and Results in Distributionally Robust Optimization (2022)Information Distortion in a Supply Chain: The Bullwhip Effect (1997)The Bullwhip Effect In Supply Chains 1 (1997)Information systems in supply chain integration and management (2003)Artificial intelligence in supply chain management: theory and applications (2009)Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities (2020)Artificial intelligence applications in supply chain management (2021)Time Series Analysis: Forecasting and Control (1977)25 years of time series forecasting (2006)Automatic Time Series Forecasting: The forecast Package for R (2008)DeepAR: Probabilistic forecasting with autoregressive recurrent networks (2019)Measuring the efficiency of decision making units (1978)Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis (1984)The Analytic Hierarchy Process (1985)Supplier selection using fuzzy AHP and fuzzy multi-objective linear programming for developing low carbon supply chain (2012)Selecting green suppliers based on GSCM practices: Using fuzzy TOPSIS applied to a Brazilian electronics company (2013)An extended TODIM multi-criteria group decision making method for green supplier selection in interval type-2 fuzzy environment (2016)Case Study Research: Design and Methods (1984)Case Study Research: Design and Methods (1991)Cannibals with forks: the triple bottom line of 21st century business (1999)An organizational theoretic review of green supply chain management literature (2010)Business models for sustainable innovation: state-of-the-art and steps towards a research agenda (2012)Green supply chain management: A review and bibliometric analysis (2015)Combining forecasts: A review and annotated bibliography (1989)Statistical and Machine Learning forecasting methods: Concerns and ways forward (2018)Recurrent Neural Networks for Time Series Forecasting: Current status and future directions (2020)The Combination of Forecasts (1969)Exponential smoothing: The state of the art (1985)Combining forecasts: A review and annotated bibliography (1990)Error measures for generalizing about forecasting methods: Empirical comparisons (1992)Forecasting methods and applications (1993)Time Series Analysis Forecasting and Control. (1971)Another look at measures of forecast accuracy (2006)Strictly Proper Scoring Rules, Prediction, and Estimation (2007)Forecast evaluation for data scientists: common pitfalls and best practices (2022)Forecasting: theory and practice (2022)Fuzzy sets (1965)Qualitative Data Analysis: An Expanded Sourcebook (1994)Fuzzy sets (1996)Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis (2021)Artificial intelligence for supply chain resilience: learning from Covid-19 (2021)Building the Resilient Supply Chain (2004)Business Intelligence and Analytics: From Big Data to Big Impact (2012)Data-intensive applications, challenges, techniques and technologies: A survey on Big Data (2014)Strengthening supply chain resilience during COVID ‐19: A case study of JD .com (2021)Artificial intelligence and big data analytics for supply chain resilience: a systematic literature review (2022)Supply chain resilience in the UK during the coronavirus pandemic: A resource orchestration perspective (2022)
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References

  • 2008From a literature review to a conceptual framework for sustainable supply chain management6,372 cited
  • 2000The M3-Competition: results, conclusions and implications1,700 cited
  • 1982The accuracy of extrapolation (time series) methods: Results of a forecasting competition1,465 cited
  • 2020Artificial intelligence in supply chain management: A systematic literature review1,015 cited
  • 2019The M4 Competition: 100,000 time series and 61 forecasting methods990 cited
  • 2019From Predictive to Prescriptive Analytics781 cited
  • 1972Forecasting and Stock Control for Intermittent Demands761 cited
  • 2021Supply chain disruptions and resilience: a major review and future research agenda683 cited
  • 2007Application of machine learning techniques for supply chain demand forecasting669 cited
  • 2010Integration of artificial neural network and MADA methods for green supplier selection654 cited
  • 2022M5 accuracy competition: Results, findings, and conclusions579 cited
  • 2015Analytics for an Online Retailer: Demand Forecasting and Price Optimization542 cited
  • 2018The Big Data Newsvendor: Practical Insights from Machine Learning510 cited
  • 2004The accuracy of intermittent demand estimates498 cited
  • 2001

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On the bias of intermittent demand estimates
363 cited
  • 2022A review on reinforcement learning algorithms and applications in supply chain management359 cited
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