Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions.
Konstantinos Nikolopoulos,Sushil Punia,Andreas Schäfers,Christos Tsinopoulos,Chrysovalantis Vasilakis +4 more
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TLDR
Predictive analytics tools for forecasting and planning during a pandemic using statistical, epidemiological, machine- and deep-learning models, and a new hybrid forecasting method based on nearest neighbors and clustering are provided.About:
This article is published in European Journal of Operational Research.The article was published on 2021-04-01 and is currently open access. It has received 304 citations till now. The article focuses on the topics: Predictive analytics.read more
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COVID-19 pandemic related supply chain studies: a systematic review
TL;DR: In this article, the authors systematically reviewed existing research on the COVID-19 pandemic in supply chain disciplines and identified 74 relevant articles published on or before 28 September 2020, and the synthesis of the findings reveals that four broad themes recur in the published work: namely, impacts of the CO VID-2019 pandemic, resilience strategies for managing impacts and recovery, the role of technology in implementing resilience strategies, and supply chain sustainability in the light of the pandemic.
Repository
Forecasting: theory and practice
Fotios Petropoulos,Daniele Apiletti,Vassilios Assimakopoulos,Mohamed Zied Babai,Devon K. Barrow,Souhaib Ben Taieb,Christoph Bergmeir,Ricardo J. Bessa,Jakub Bijak,John E. Boylan,Jethro Browell,Claudio Carnevale,Jennifer L. Castle,Pasquale Cirillo,Michael P. Clements,Clara Cordeiro,Clara Cordeiro,Fernando Luiz Cyrino Oliveira,Shari De Baets,Alexander Dokumentov,Joanne Ellison,Piotr Fiszeder,Philip Hans Franses,David T. Frazier,Michael Gilliland,M. Sinan Gönül,Paul Goodwin,Luigi Grossi,Yael Grushka-Cockayne,Mariangela Guidolin,Massimo Guidolin,Ulrich Gunter,Xiaojia Guo,Renato Guseo,Nigel Harvey,David F. Hendry,Ross Hollyman,Tim Januschowski,Jooyoung Jeon,Victor Richmond R. Jose,Yanfei Kang,Anne B. Koehler,Stephan Kolassa,Nikolaos Kourentzes,Nikolaos Kourentzes,Sonia Leva,Feng Li,Konstantia Litsiou,Spyros Makridakis,Gael M. Martin,Andrew B. Martinez,Andrew B. Martinez,Sheik Meeran,Theodore Modis,Konstantinos Nikolopoulos,Dilek Önkal,Alessia Paccagnini,Alessia Paccagnini,Anastasios Panagiotelis,Ioannis P. Panapakidis,Jose M. Pavía,Manuela Pedio,Manuela Pedio,Diego J. Pedregal,Pierre Pinson,Patrícia Ramos,David E. Rapach,J. James Reade,Bahman Rostami-Tabar,Michał Rubaszek,Georgios Sermpinis,Han Lin Shang,Evangelos Spiliotis,Aris A. Syntetos,Priyanga Dilini Talagala,Thiyanga S. Talagala,Len Tashman,Dimitrios D. Thomakos,Thordis L. Thorarinsdottir,Ezio Todini,Juan Ramón Trapero Arenas,Xiaoqian Wang,Robert L. Winkler,Alisa Yusupova,Florian Ziel +84 more
TL;DR: A non-systematic review of the theory and the practice of forecasting, offering a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts.
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Improving supply chain resilience through industry 4.0: a systematic literature review under the impressions of the COVID-19 pandemic
Alexander Spieske,Hendrik Birkel +1 more
TL;DR: The results reveal that big data analytics is particularly suitable for improving supply chain resilience, while other industry 4.0 enabler technologies, including additive manufacturing and cyber-physical systems, still lack proof of effectiveness.
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Supply Chain Recovery Challenges in the Wake of COVID-19 Pandemic
TL;DR: In this article, a Delphi-based grey decision-making trial and evaluation laboratory (DEMATEL) methodology was used to analyze the data and identify the major supply chain recovery challenges from the impacts of the COVID-19 pandemic, the grey DEMATEL approach helped categorize the causal relationships among these challenges.
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Supply chain disruption during the COVID-19 pandemic: Recognizing potential disruption management strategies
TL;DR: In this article , the authors present a set of up-to-date bibliometric, network, and thematic analyses to identify the influential contributors, main research streams, and disruption management strategies related to the SC performance under the COVID-19 settings.
References
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Random Forests
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
Journal ArticleDOI
Information distortion in a supply chain: the bullwhip effect
TL;DR: The authors analyzes four sources of the bullwhip effect: demand signal processing, rationing game, order batching, and price variations, and shows that the distortion tends to increase as one moves upstream.
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Another look at measures of forecast accuracy
Rob J. Hyndman,Anne B. Koehler +1 more
TL;DR: In this paper, the mean absolute scaled error (MESEME) was proposed as the standard measure for comparing forecast accuracy across multiple time series across different time series types, and was used in the M-competition as well as the M3competition.
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Applied Nonparametric Regression
TL;DR: This chapter discusses smoothing in high Dimensions, Investigating multiple regression by additive models, and incorporating parametric components and alternatives.
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Quantifying the Bullwhip Effect in a Simple Supply Chain: The Impact of Forecasting, Lead Times, and Information
TL;DR: In this article, the authors quantify the effect of the bullwhip effect on simple two-stage supply chains consisting of a single retailer and a single manufacturer and demonstrate that the effect can be reduced by centralizing demand information.
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