Buch, Englisch, 96 Seiten, Format (B × H): 178 mm x 254 mm
How to Drive Smarter Continuous Improvement
Buch, Englisch, 96 Seiten, Format (B × H): 178 mm x 254 mm
ISBN: 978-1-041-12232-6
Verlag: Taylor & Francis Ltd
This book presents a practical roadmap for integrating artificial intelligence (AI) tools into the proven methodologies of Lean thinking to enhance operational excellence and continuous improvement. Drawing from decades of experience in manufacturing, operations, and management consulting, the author explores how modern AI capabilities can extend Lean’s power by improving decision-making, identifying patterns, and eliminating waste in new ways.
The book begins by revisiting the foundational principles of Lean, such as value stream mapping, root cause analysis, and standard work. It then introduces key AI technologies -- including machine learning, natural language processing, and predictive analytics -- and explains their relevance to Lean tools and concepts. Real-world case studies and use cases are used throughout to show how AI can elevate traditional Lean practices. Examples include predictive maintenance enabled by AI-driven sensors, automated analysis of customer feedback for quality improvement, and the use of generative AI to streamline value stream analysis and documentation.
The key benefit of this work is its clarity in bridging two powerful yet often separately applied methodologies. The book serves as a hands-on guide for Lean professionals, operational leaders, and continuous improvement teams who want to apply AI effectively without losing sight of Lean’s core values.
Zielgruppe
Professional Practice & Development
Autoren/Hrsg.
Fachgebiete
- Wirtschaftswissenschaften Betriebswirtschaft Management Qualitätsmanagement, Qualitätssicherung (QS), Total Quality Management (TQM)
- Wirtschaftswissenschaften Betriebswirtschaft Management Unternehmensorganisation & Entwicklungsstrategien
- Wirtschaftswissenschaften Betriebswirtschaft Bereichsspezifisches Management Management: Führung & Motivation
- Wirtschaftswissenschaften Betriebswirtschaft Bereichsspezifisches Management Einkauf, Logistik, Supply-Chain-Management
- Wirtschaftswissenschaften Betriebswirtschaft Management Wissensmanagement
- Wirtschaftswissenschaften Betriebswirtschaft Bereichsspezifisches Management Produktionsmanagement, Qualitätskontrolle
Weitere Infos & Material
SECTION 1 — CONDITIONS: PRINCIPLES FOR LEAN + AI Chapter 1 - AI Does Not Fix Lean — It Reveals It Chapter 2 - Lean Must Lead — AI Must Follow Chapter 3 - Respect for People Is the Constraint Chapter 4 - Learning Speed — Not Efficiency — Is the Advantage Chapter 5 - Visibility Without Judgment Is Dangerous Chapter 6 - Gemba Cannot Be Digitized Chapter 7 - Small Experiments Build Trust — Big Launches Destroy It Chapter 8 - Capability, Not Tools, Determines the Outcome Chapter 9 - Lean Civilizes AI SECTION 2 — EVIDENCE: VOICES FROM THE FIELD Introduction: Why Voices Matter Expert–Question Map Chapter 10 - The Premise: Why Lean + AI, and Why Now Chapter 11 - Real-World Application: Where AI Meets Continuous Improvement Chapter 12 - Hopes, Risks, and Lessons Learned Chapter 13 – Change Management, Adoption, and Trust Chapter 14 – Practice and Process: Gemba, PDCA, and Digital Tools Chapter 15 – Foundations and Definitions Chapter 16 – Strategy, Leadership, and the Future Enterprise SECTION 3 — PRACTICE: LEADING LEAN + AI Chapter 17 - Where Leaders Go Wrong – and What to Do Instead Chapter 18 - What Must Be Stabilized Before Introducing AI Chapter 19 – How to Experiment with AI Without Damaging Learning Chapter 20 – Integrating AI in to Daily Management Without Losing Control Chapter 21 – Knowing When – and When Not – to Scale AI Chapter 22 - Sustaining Lean + AI Over Time Closing A Final Word on Co-Intelligence Expert Index




