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001 978-3-030-40172-6
003 DE-He213
005 20240423130141.0
007 cr nn 008mamaa
008 200314s2020 sz | s |||| 0|eng d
020 _a9783030401726
_9978-3-030-40172-6
024 7 _a10.1007/978-3-030-40172-6
_2doi
050 4 _aHD30.19-.29
072 7 _aUF
_2bicssc
072 7 _aCOM005000
_2bisacsh
072 7 _aUXJ
_2thema
082 0 4 _a005.3
_223
245 1 0 _aProcess Mining in Action
_h[electronic resource] :
_bPrinciples, Use Cases and Outlook /
_cedited by Lars Reinkemeyer.
250 _a1st ed. 2020.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2020.
300 _aXXII, 207 p. 87 illus., 79 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aPart I Principles and Value of Process Mining -- 1 Process Mining in a Nutshell -- 2 How to get Started -- 3 Purpose: Identifying the right Use Cases -- 4 People: The Human Factor -- 5 Processtraces: Technology -- 6 Challenges, Pitfalls and Failures -- 7 Process Mining, RPA, BPM and DTO -- 8 Key Learnings -- Part II Best Practice Use Cases -- 9 Siemens: Driving global change with the Digital Fit Rate in Order2Cash -- 10 Uber: Process Mining to optimize Customer experience and Business performance -- 11 BMW: Process Mining @ Production -- 12 Siemens: Process Mining for operational efficiency in Purchase2Pay -- 13 athenahealth: Process Mining for Service Integrity in Healthcare -- 14 EDP Comercial: Sales and Service Digitization -- 15 ABB: From Mining Processes towards Driving Processes -- 16 Bosch: Process Mining – a Corporate Consulting Perspective -- 17 Schukat: Process Mining enables Schukat electronic to reinvent itself -- 18 Siemens Healthineers: Process Mining as Innovation Driver in Product Management -- 19 Bayer: Process Mining supports Digital Transformation in Internal Audit -- 20 Telekom: Process Mining in Shared Services -- Part III Outlook: Future of Process Mining -- 21 Academic View: Development of the Process Mining Discipline -- 22 Business View: Towards a Digital Enabled Organization.
520 _aThis book describes process mining use cases and business impact along the value chain, from corporate to local applications, representing the state of the art in domain know-how. Providing a set of industrial case studies and best practices, it complements academic publications on the topic. Further the book reveals the challenges and failures in order to offer readers practical insights and guidance on how to avoid the pitfalls and ensure successful operational deployment. The book is divided into three parts: Part I provides an introduction to the topic from fundamental principles to key success factors, and an overview of operational use cases. As a holistic description of process mining in a business environment, this part is particularly useful for readers not yet familiar with the topic. Part II presents detailed use cases written by contributors from a variety of functions and industries. Lastly, Part III provides a brief overview of the future of process mining, both from academic and operational perspectives. Based on a solid academic foundation, process mining has received increasing interest from operational businesses, with many companies already reaping the benefits. As the first book to present an overview of successful industrial applications, it is of particular interest to professionals who want to learn more about the possibilities and opportunities this new technology offers. It is also a valuable resource for researchers looking for empirical results when considering requirements for enhancements and further developments. “If your organization cares about operational performance and has a culture that thrives on transparency, Process Mining is the answer to your prayers. And there is no better source than this book to learn about what Process Mining is, how it's being used in leading companies, and where it might go in the future.” Thomas H. Davenport (Distinguished Professor, Babson College and Research Fellow, MIT Initiative on the Digital Economy); Author of Process Innovation, Competing on Anal “Unlike the traditional plan-execution process improvement, Process Mining allows full transparency of actual processes and activities. Process Mining in Action describes principles, challenges and learnings from years of practice.” Seungjin Whang (Professor of Operations, Information & Technology at Stanford Graduate School of Business) “This is a timely book that presents operational experiences and brings Process Mining application problems to academic research communities. It inspires researchers to further develop frameworks and techniques to tackle broader process analytics challenges over multiple application domains in order to complement the fast growing operational community.” Jianwen Su (Professor of Computer Science at University of California, Santa Barbara) Features and Benefits First book to present an overview of successful industrial experiences of process mining Operational experts describe use cases and business impact along the whole value chain Discusses the challenges, lessons learned and failures in order to provide guidance on how to avoid pitfalls and ensure successful operational deployment.
650 0 _aInformation technology
_xManagement.
650 0 _aApplication software.
650 0 _aQuantitative research.
650 0 _aBig data.
650 0 _aData mining.
650 1 4 _aComputer Application in Administrative Data Processing.
650 2 4 _aBusiness Process Management.
650 2 4 _aComputer and Information Systems Applications.
650 2 4 _aData Analysis and Big Data.
650 2 4 _aBig Data.
650 2 4 _aData Mining and Knowledge Discovery.
700 1 _aReinkemeyer, Lars.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030401719
776 0 8 _iPrinted edition:
_z9783030401733
776 0 8 _iPrinted edition:
_z9783030401740
856 4 0 _uhttps://doi.org/10.1007/978-3-030-40172-6
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c185598
_d185598