SZodixStudyAcademic Library
ShelvesOpen access
Study · an open academic library·Metadata from OpenAlex (CC0)·Shelves·OpenAlex metadata, de-duplicated
Reducing demand uncertainty in the platelet supply chain through artificial neural networks and ARIMA models
15 references·16 citing·31 second hop
Reducing demand uncertainty in the platelet supply chain through artificial neural networks and ARIMA models (2019)Reducing demand uncertainty in the platel…The top ten global health supply chain issues: Perspectives from the field (2014)The top ten global health sup…Blood Inventory Management: Hospital Best Practice (2012)Blood Inventory Management: H…Analysis of Financial Time Series (2010)Analysis of Financial Time Se…Introduction to time series analysis and forecasting (2008)Introduction to time series a…Understanding supply chain dynamics: A chaos perspective (2006)Understanding supply chain dy…Analysis of Financial Time Series (2006)Analysis of Financial Time Se…Supply Chain Management: Strategy, Planning, and Operation (2003)Supply Chain Management: Stra…Simulation of supply chain behaviour and performance in an uncertain environment (2001)Simulation of supply chain be…An investigation of model selection criteria for neural network time series forecasting (2001)An investigation of model sel…Supply Chain Management: Strategy, Planning and Operations (2000)Supply Chain Management: Stra…Forecasting: Methods and Applications (1999)Forecasting: Methods and Appl…Forecasting methods and applications (1993)Forecasting methods and appli…On the Theory of Testing for Unit Roots in Observed Time Series (1986)On the Theory of Testing for …Time Series Analysis: Forecasting and Control. (1978)Time Series Analysis: Forecas…Time Series Analysis, Forecasting and Control. (1971)Time Series Analysis, Forecas…A data-driven approach to optimize a blood supply chain within the Industry 5.0 framework: A stochastic optimization model (2025)A data-driven approach to opt…Demand Forecasting in Supply Chain Using Uni-Regression Deep Approximate Forecasting Model (2024)Demand Forecasting in Supply …Demand forecasting for platelet usage: From univariate time series to multivariable models (2024)Demand forecasting for platel…A robust optimization model for multi-objective blood supply chain network considering scenario analysis under uncertainty: a multi-objective approach (2024)A robust optimization model f…Smart Platform for Data Blood Bank Management: Forecasting Demand in Blood Supply Chain Using Machine Learning (2023)Smart Platform for Data Blood…Artificial neural networks in supply chain management, a review (2023)Artificial neural networks in…Artificial intelligence techniques for enhancing supply chain resilience: A systematic literature review, holistic framework, and future research (2023)Artificial intelligence techn…A Systematic Literature Review of the Blood Supply Chain through Bibliometric Analysis and Taxonomy (2023)A Systematic Literature Revie…Forecasting blood demand for different blood groups in Shiraz using auto regressive integrated moving average (ARIMA) and artificial neural network (ANN) and a hybrid approaches (2022)Forecasting blood demand for …Efficient customer segmentation in digital marketing using deep learning with swarm intelligence approach (2022)Efficient customer segmentati…A robust autonomous method for blood demand forecasting (2022)A robust autonomous method fo…A Comparative Study of Demand Forecasting Models for a Multi-Channel Retail Company: A Novel Hybrid Machine Learning Approach (2022)A Comparative Study of Demand…Reduction of Platelet Outdating and Shortage by Forecasting Demand With Statistical Learning and Deep Neural Networks: Modeling Study (2021)Reduction of Platelet Outdati…Enablers of resilience in the healthcare supply chain: A case study of U.S healthcare industry during COVID-19 pandemic (2021)Enablers of resilience in the…Comparative analysis of short-term demand predicting models using ARIMA and deep learning (2021)Comparative analysis of short…An Advanced Data-Driven Hybrid Model of SARIMA-NNNAR for Tuberculosis Incidence Time Series Forecasting in Qinghai Province, China (2020)An Advanced Data-Driven Hybri…Time Series Analysis: Forecasting and Control (1977)Distribution of the Estimators for Autoregressive Time Series with a Unit Root (1979)Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root (1981)Explosive Rational Bubbles in Stock Prices (1988)Pitfalls in Testing for Explosive Bubbles in Asset Prices (1991)TESTING FOR MULTIPLE BUBBLES: HISTORICAL EPISODES OF EXUBERANCE AND COLLAPSE IN THE S&P 500 (2015)Multivariate GARCH models: a surveyThe Pricing of Options and Corporate Liabilities (1973)Co-Integration and Error Correction: Representation, Estimation, and Testing (1987)Modeling Ordered Choices (2010)THE EVOLUTION OF STOCK MARKET EFFICIENCY OVER TIME: A SURVEY OF THE EMPIRICAL LITERATURE (2010)Managing Disruption Risks in Supply Chains (2005)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)A comparison of three different approaches to tourist arrival forecasting (2003)Evaluation of statistical and machine learning models for time series prediction: Identifying the state-of-the-art and the best conditions for the use of each model (2019)Bayesian optimization based dynamic ensemble for time series forecasting (2022)Business models and supply chains for the circular economy (2018)The accuracy of extrapolation (time series) methods: Results of a forecasting competition (1982)25 years of time series forecasting (2006)Statistical and Machine Learning forecasting methods: Concerns and ways forward (2018)The theta model: a decomposition approach to forecasting (2000)Improved supply chain management based on hybrid demand forecasts (2005)Can Google data improve the forecasting performance of tourist arrivals? Mixed-data sampling approach (2014)A model of carbon price interactions with macroeconomic and energy dynamics (2011)STATISTICAL ARBITRAGE PAIRS TRADING STRATEGIES: REVIEW AND OUTLOOK (2016)Combining official and Google Trends data to forecast the Italian youth unemployment rate (2017)Principles of forecasting : a handbook for researchers and practitioners (2001)The politics of attention: how government prioritizes problems (2006)Long memory relationships and the aggregation of dynamic models (1980)Understanding spurious regressions in econometrics (1986)
this work references cited by second hop

References

  • 1978Time Series Analysis: Forecasting and Control.5,299 cited
  • 1971Time Series Analysis, Forecasting and Control.2,771 cited
  • 2000Supply Chain Management: Strategy, Planning and Operations2,583 cited
  • 1993Forecasting methods and applications2,393 cited
  • 2006Analysis of Financial Time Series2,348 cited
  • 2003Supply Chain Management: Strategy, Planning, and Operation2,283 cited
  • 2008Introduction to time series analysis and forecasting1,405 cited
  • 2010Analysis of Financial Time Series1,035 cited
  • 1999Forecasting: Methods and Applications626 cited
  • 1986On the Theory of Testing for Unit Roots in Observed Time Series612 cited
  • 2001An investigation of model selection criteria for neural network time series forecasting257 cited
  • 2001Simulation of supply chain behaviour and performance in an uncertain environment184 cited
  • 2012Blood Inventory Management: Hospital Best Practice167 cited
  • 2006Understanding supply chain dynamics: A chaos perspective161 cited
  • 2014The top ten global health supply chain issues: Perspectives from the field137 cited

Cited by

Only edges whose both ends are imported exist here. Widen the year window, or show the whole map. Back to the work

  • 2021Enablers of resilience in the healthcare supply chain: A case study of U.S healthcare industry during COVID-19 pandemic129 cited
  • 2022Efficient customer segmentation in digital marketing using deep learning with swarm intelligence approach110 cited
  • 2022A Comparative Study of Demand Forecasting Models for a Multi-Channel Retail Company: A Novel Hybrid Machine Learning Approach103 cited
  • 2023Artificial neural networks in supply chain management, a review96 cited
  • 2023Artificial intelligence techniques for enhancing supply chain resilience: A systematic literature review, holistic framework, and future research92 cited
  • 2023Smart Platform for Data Blood Bank Management: Forecasting Demand in Blood Supply Chain Using Machine Learning66 cited
  • 2024Demand Forecasting in Supply Chain Using Uni-Regression Deep Approximate Forecasting Model48 cited
  • 2021Comparative analysis of short-term demand predicting models using ARIMA and deep learning45 cited
  • 2020An Advanced Data-Driven Hybrid Model of SARIMA-NNNAR for Tuberculosis Incidence Time Series Forecasting in Qinghai Province, China40 cited
  • 2024A robust optimization model for multi-objective blood supply chain network considering scenario analysis under uncertainty: a multi-objective approach27 cited
  • 2024Demand forecasting for platelet usage: From univariate time series to multivariable models27 cited
  • 2021Reduction of Platelet Outdating and Shortage by Forecasting Demand With Statistical Learning and Deep Neural Networks: Modeling Study26 cited
  • 2022Forecasting blood demand for different blood groups in Shiraz using auto regressive integrated moving average (ARIMA) and artificial neural network (ANN) and a hybrid approaches21 cited
  • 2023A Systematic Literature Review of the Blood Supply Chain through Bibliometric Analysis and Taxonomy21 cited
  • 2025A data-driven approach to optimize a blood supply chain within the Industry 5.0 framework: A stochastic optimization model14 cited
  • 2022A robust autonomous method for blood demand forecasting12 cited