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Titlebook: Digitization in Controlling; Forecasting Processe Andre Gro?e Kamphake Book 2020 Springer Fachmedien Wiesbaden GmbH, part of Springer Natur

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書目名稱Digitization in Controlling
副標(biāo)題Forecasting Processe
編輯Andre Gro?e Kamphake
視頻videohttp://file.papertrans.cn/281/280417/280417.mp4
叢書名稱BestMasters
圖書封面Titlebook: Digitization in Controlling; Forecasting Processe Andre Gro?e Kamphake Book 2020 Springer Fachmedien Wiesbaden GmbH, part of Springer Natur
描述.Andre Gro?e 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..
出版日期Book 2020
關(guān)鍵詞Controlling; Forecasting; Predictive Analytics; Digitization; Digitalization; Decision Tree; ARIMA
版次1
doihttps://doi.org/10.1007/978-3-658-28741-2
isbn_softcover978-3-658-28740-5
isbn_ebook978-3-658-28741-2Series ISSN 2625-3577 Series E-ISSN 2625-3615
issn_series 2625-3577
copyrightSpringer Fachmedien Wiesbaden GmbH, part of Springer Nature 2020
The information of publication is updating

書目名稱Digitization in Controlling影響因子(影響力)




書目名稱Digitization in Controlling影響因子(影響力)學(xué)科排名




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Digitization in Controlling978-3-658-28741-2Series ISSN 2625-3577 Series E-ISSN 2625-3615
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Soviet Social Scientists TalkingThe data.one platform described in paragraph 2.4 comprises almost all Bayer data. Since this forecast plays a pioneering role in Bayer’s WoC prediction, a small data set is chosen first. The data set contains information of a sales subsidiary, located in one country, which distributes the goods across two divisions.
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