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Ajouter au panierEtat : New. pp. XV, 70 17 illus. 1st ed. 2020 edition NO-PA16APR2015-KAP.
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Ajouter au panierPaperback. Etat : Brand New. 85 pages. German language. 8.27x5.83x0.28 inches. In Stock.
Langue: anglais
Edité par Springer Fachmedien Wiesbaden, 2020
ISBN 10 : 3658287403 ISBN 13 : 9783658287405
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Ajouter au panierTaschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - Andre Große Kamphake deals with the digitization in controlling and focuses in this context on the analysis of automated forecasting processes within a chemical company. He aims at outlining to what extent and how accurate forecasting processes can be automated in the age of digitization and big data. Therefore, the forecast of the working capital is put at the center since it plays a leading role for the cash collection process. Based on data from 2015 to 2018, two different forecasting models are combined to optimally predict the different components contained in the working capital. The author manages to prove that both a trained forecasting algorithm achieves a prediction accuracy of 92.49 % and statistical methods in machine learning lead to a significant increase in forecasts compared to naive forecasting models.
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Ajouter au panierEtat : new. Questo è un articolo print on demand.
Langue: anglais
Edité par Springer Fachmedien Wiesbaden Jan 2020, 2020
ISBN 10 : 3658287403 ISBN 13 : 9783658287405
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
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Ajouter au panierTaschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Andre Große Kamphake deals with the digitization in controlling and focuses in this context on the analysis of automated forecasting processes within a chemical company. He aims at outlining to what extent and how accurate forecasting processes can be automated in the age of digitization and big data. Therefore, the forecast of the working capital is put at the center since it plays a leading role for the cash collection process. Based on data from 2015 to 2018, two different forecasting models are combined to optimally predict the different components contained in the working capital. The author manages to prove that both a trained forecasting algorithm achieves a prediction accuracy of 92.49 % and statistical methods in machine learning lead to a significant increase in forecasts compared to naive forecasting models. 88 pp. Englisch.
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Ajouter au panierEtat : New. Print on Demand pp. XV, 70 17 illus.
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Ajouter au panierEtat : New. PRINT ON DEMAND pp. XV, 70 17 illus.
Langue: anglais
Edité par Springer Fachmedien Wiesbaden, 2020
ISBN 10 : 3658287403 ISBN 13 : 9783658287405
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Ajouter au panierEtat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Electronic  Supplementary Material AvailableThe Challenge of Digitalization Projects.- Optimization of Working Capital Management.- Proceeding for Data-Driven Data Mining Forecasts.- Application of the Decision Tree Algorithm C 5.0.- Implem.
Langue: anglais
Edité par Springer Vieweg Jan 2020, 2020
ISBN 10 : 3658287403 ISBN 13 : 9783658287405
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
EUR 53,49
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Ajouter au panierTaschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -Andre Große Kamphake deals with the digitization in controlling and focuses in this context on the analysis of automated forecasting processes within a chemical company. He aims at outlining to what extent and how accurate forecasting processes can be automated in the age of digitization and big data. Therefore, the forecast of the working capital is put at the center since it plays a leading role for the cash collection process. Based on data from 2015 to 2018, two different forecasting models are combined to optimally predict the different components contained in the working capital. The author manages to prove that both a trained forecasting algorithm achieves a prediction accuracy of 92.49 % and statistical methods in machine learning lead to a significant increase in forecasts compared to naive forecasting models.Springer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 88 pp. Englisch.