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Reproducibility
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Reproducibility (2016)ReproducibilityAnnouncement: Reducing our irreproducibility (2013)Announcement: Reducing our ir…Joint maintenance and spare-parts inventory models: a review and discussion of practical stock-keeping rules (2023)Joint maintenance and spare-p…Forecasting of lead-time demand variance: Implications for safety stock calculations (2021)Forecasting of lead-time dema…On the empirical performance of some new neural network methods for forecasting intermittent demand (2020)On the empirical performance …Anticipating special events in Emergency Department forecasting (2020)Anticipating special events i…The inventory performance of forecasting methods: Evidence from the M3 competition data (2018)The inventory performance of …Forecasting with a hybrid method utilizing data smoothing, a variation of the Theta method and shrinkage of seasonal factors (2018)Forecasting with a hybrid met…An investigation on bootstrapping forecasting methods for intermittent demands (2018)An investigation on bootstrap…A new method to forecast intermittent demand in the presence of inventory obsolescence (2018)A new method to forecast inte…Enriching demand forecasts with managerial information to improve inventory replenishment decisions: Exploiting judgment and fostering learning (2017)Enriching demand forecasts wi…Reducing the Cost of Demand Uncertainty Through Accurate Response to Early Sales (1996)Decision Bias in the Newsvendor Problem with a Known Demand Distribution: Experimental Evidence (2000)Tests of equal forecast accuracy and encompassing for nested models (2001)Control of inventory dynamics: A survey of special cases for products with low demand (2020)Superforecasting reality check: Evidence from a small pool of experts and expedited identification (2020)Demand Forecasting Application with Regression and IoT Based Inventory Management System: A Case Study of a Semiconductor Manufacturing Company (2022)Information Distortion in a Supply Chain: The Bullwhip Effect (1997)Another look at measures of forecast accuracy (2006)Automatic Time Series Forecasting: The forecast Package for R (2008)Are forecasting competitions data representative of the reality? (2019)The M4 Competition: 100,000 time series and 61 forecasting methods (2019)Intermittent demand forecasting for spare parts in the heavy-duty vehicle industry: a support vector machine model (2020)Forecasting methods and applications (1993)The M3-Competition: results, conclusions and implications (2000)A combination-based forecasting method for the M4-competition (2019)N-BEATS: Neural basis expansion analysis for interpretable time series forecasting (2019)Forecasting: theory and practice (2022)Forecasting and Stock Control for Intermittent Demands (1972)On the categorization of demand patterns (2004)The accuracy of intermittent demand estimates (2004)On the order-up-to policy with intermittent integer demand and logically consistent forecasts (2023)Time Series Analysis: Forecasting and Control. (1978)Strictly Proper Scoring Rules, Prediction, and Estimation (2007)Forecasting with artificial neural networks: (1998)Exponential smoothing: The state of the art—Part II (2006)Basket data-driven approach for omnichannel demand forecasting (2022)On the use of machine learning in supply chain management: a systematic review (2023)Book Review: Time Series Analysis: Forecasting and Control (1977)The Value of Information Sharing in a Two-Level Supply Chain (2000)Supply Chain Management: Strategy, Planning, and Operation (2003)A beautiful shock? Exploring the impact of pandemic shocks on the accuracy of AI forecasting in the beauty care industry (2023)Is the online-offline buy-online-pickup-in-store retail strategy best among other product delivery strategies under variable lead time? (2023)Order-up-to-level inventory optimization model using time-series demand forecasting with ensemble deep learning (2023)Piecewise-Deterministic Markov Processes: A General Class of Non-Diffusion Stochastic Models (1984)Maintenance spare parts logistics: Special characteristics and strategic choices (2001)An overview of recent literature on spare parts inventories (2002)The use of continuous action representations to scale deep reinforcement learning for inventory control (2023)Fifty years of inventory research from a forecasting perspective (2025)Using supervised machine learning for B2B sales forecasting: A case study of spare parts sales forecasting at an after-sales service provider (2021)Policy: NIH plans to enhance reproducibility (2014)Reproducibility in Science (2014)Risk of Bias in Reports of In Vivo Research: A Focus for Improvement (2015)
this work references cited by second hop

References

  • 2013Announcement: Reducing our irreproducibility243 cited

Cited by

  • 2018A new method to forecast intermittent demand in the presence of inventory obsolescence72 cited
  • 2018The inventory performance of forecasting methods: Evidence from the M3 competition data70 cited
  • 2021Forecasting of lead-time demand variance: Implications for safety stock calculations41 cited
  • 2020On the empirical performance of some new neural network methods for forecasting intermittent demand41 cited
  • 2023Joint maintenance and spare-parts inventory models: a review and discussion of practical stock-keeping rules35 cited
  • 2018Forecasting with a hybrid method utilizing data smoothing, a variation of the Theta method and shrinkage of seasonal factors33 cited
  • 2018An investigation on bootstrapping forecasting methods for intermittent demands31 cited
  • 2020Anticipating special events in Emergency Department forecasting24 cited
  • 2017Enriching demand forecasts with managerial information to improve inventory replenishment decisions: Exploiting judgment and fostering learning22 cited

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