Process Intelligence in Action : Taking Process Mining to the Next Level / edited by Lars Reinkemeyer

Mitwirkende(r):

Reinkemeyer, Lars [HerausgeberIn]

Resource type: Ressourcentyp: Buch (Online)Buch (Online)Sprache: Englisch Verlag: Cham : Springer Nature Switzerland, 2024Verlag: Cham : Imprint: Springer, 2024Auflage: 1st ed. 2024Beschreibung: 1 Online-Ressource(XXIV, 248 p. 107 illus., 67 illus. in color.)ISBN:

9783031613432

Schlagwörter:

6,500,285 23
658.05 23
650.0285 23

DOI: DOI: 10.1007/978-3-031-61343-2Online-Ressourcen:

Zugang im Netz des KIT

Zusammenfassung: Part I: From Process Mining to Process Intelligence -- 1. Evolution -- 2. How to get started -- 3. Driving user adoption -- 4. Success Factors for Digital Transformation -- 5. Value as the name of the Game -- 6. Operating Models and Centers of Excellence -- 7. Process Intelligence as Transformation Enabler -- 8. Challenges, Pitfalls and Failures -- 9. Key Learnings -- Part II: Best Practice Use Cases -- 10. ABB: Driving a Sustainability Transformation with Process Intelligence -- 11. BMW: Process Intelligence for Everybody – Organizational Setup & Scaling -- 12. Bosch: Business & IT Transformation facilitated by Process Intelligence -- 13. Ingka: Inspiring a Community for Open Exchange -- 14. Kimberly-Clark: Process Intelligence as Catalyst for Supply Chain and Global Business Services Transformation -- 15. Merck, Sharp & Dohme: Digital Transformation in Shared Services and Beyond -- 16. PepsiCo: From Discovery to Dollars - Turning Process Insights into Value -- 17. Reckitt’s Digital Transformation: Unleashing the Power of Process Intelligence at Scale -- 18. Saint Gobain: The Power of Executive Sponsorship -- 19. Siemens: Acting resiliently through hybrid Process Intelligence in the Supply Chain Metaverse -- 20. thyssenkrupp Rasselstein: Connected Supply Chain across thyssenkrupp -- 21. Uniper: Empowering Business Transformation through Process Orchestration -- Part III Future of Process Intelligence -- Academic Perspective: How Object-Centric Process Mining Helps to Unleash Predictive and Generative AI -- 23. Business Perspective: The Future of Intelligent Business Process Execution.Zusammenfassung: This book provides operational guidance on how to bring process mining to the next level, with process intelligence enabling companies to improve process efficiency and realize value. Written by practitioners, it combines the editor’s 10-year experience in this field gained at Celonis and Siemens, with 12 best practice use cases from international companies representing multiple industries and domains. Part I sets the stage describing the evolution from process mining to process intelligence. The chapters guide the reader step by step, from getting started to driving adoption at scale. Success factors critical for digital transformations and a detailed path to value realization are presented. Best practices on operating models and Centers of Excellence (CoEs) are shared as accelerators for successful digital transformations. Part II presents 12 use cases written by transformation- and CoE leaders who have achieved significant impact and value with process intelligence in their respective organization. All use cases have been written independent from any particular software, with a focus on evangelizing the topic and showcasing how companies like ABB, BMW, Bosch, Merck, PepsiCo, Saint Gobin, Siemens, and others leverage the capability to drive value. Part III provides an outlook on the future of process intelligence from an academic and an operational perspective, with a special focus on the disruptive impact of GenAI with future scenarios, challenges and recommendations. The book is written by practitioners for practitioners. Readers may have responsibilities as senior executives, transformation leaders, process managers and experts, consultants, change evangelists, etc. The book provides operational, hands-on tips on how to accelerate process transformation in organizations by detailing best practices as well as possible pitfalls.PPN: PPN: 1893105385Package identifier: Produktsigel: ZDB-2-SEB | ZDB-2-SCS | ZDB-2-SXCS

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