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文章基本信息

  • 标题:Automated business process management – in times of digital transformation using machine learning or artificial intelligence
  • 本地全文:下载
  • 作者:Daniel Paschek ; Daniel Paschek ; Caius Tudor Luminosu
  • 期刊名称:MATEC Web of Conferences
  • 电子版ISSN:2261-236X
  • 出版年度:2017
  • 卷号:121
  • 页码:1-8
  • DOI:10.1051/matecconf/201712104007
  • 语种:English
  • 出版社:EDP Sciences
  • 摘要:The continuous optimization of business processes is still a challenge for companies. In times of digital transformation, faster changing internal and external framework conditions and new customer expectations for fastest delivery and best quality of goods and many more, companies should set up their internal process at the best way. But what to do if framework conditions changed unexpectedly? The purpose of the paper is to analyse how the digital transformation will impact the Business Process Management (BPM) while using methods like machine learning or artificial intelligence. Therefore, the core components will be explained, compared and set up in relation. To identify application areas interviews and analysis will be held up with digital companies. The finding of the paper will be recommendation for action in the field of BPM and process optimization through machine learning and artificial intelligence. The Approach of optimizing and management processes via machine learning and artificial intelligence will support companies to decide which tool will be the best for automated BPM.
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