- 426 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
About This Book
Research efforts in the past ten years have led to considerable advances in the concepts and methods of smart manufacturing. Smart Manufacturing: Concepts and Methods puts these advances in perspective, showing how process industries can benefit from these new techniques. The book consolidates results developed by leading academic and industrial groups in the area, providing a systematic, comprehensive coverage of conceptual and methodological advances made to date.
Written by leaders in the field from around the world, Smart Manufacturing: Concepts and Methods is essential reading for graduate students, researchers, process engineers, and managers. It is complemented by a companion book titled Smart Manufacturing: Applications and Case Studies, which covers the applications of smart manufacturing concepts and methods in process industries and beyond.
- Takes a process-systems engineering approach to design, monitoring, and control of smart manufacturing systems
- Brings together the key concepts and methods of smart manufacturing, including the advances made in the past decade
- Includes coverage of computation methods for process optimization, control, and safety, as well as advanced modelling techniques
Frequently asked questions
Information
Chapter 1: Smart manufacturing: It's a journey, not a destination
Abstract
Keywords
1: Introduction
- ā¢ There will be an increase in automation, and a reduction of hands-on workforce [4]. This trend has already been progressing over several decades and will continue for decades to come. The need for manual labor and hands on activities will continue to decline.
- ā¢ Robotics and autonomous equipment will replace people for needed transport, process monitoring, and inspection. This will reduce exposure to safety risks and also help reduce the industry and manufacturing size footprint [5].
- ā¢ Factories will become modular and mobile, making relocation much simpler and less costly. Commodity industries will be closer to supply chain and sources of energy. Manufacturing will be done closer to the consumer reducing time to market and the cost of transportation and logistics [6].
- ā¢ Similar to the trend in automation, artificial intelligence will also start to become a prevalent capability in our production and operations facilities. AI will begin to replace traditional professional roles in industry and manufacturing [5].
- ā¢ With improved operational intelligence, virtual and/or physical centers of excellences (CoEs) will be established to monitor or control like assets. This will more effectively leverage critical subject matter experts (SMEs), enable improved knowledge sharing across teams, and help offset risk associated with the loss of key resources [7].
- ā¢ Sustainability and conservation will go beyond the walls of individual companies and start to optimize at both a community and global enterprise level.
- ā¢ Data and information will continue to become a more valuable asset in support of continuous performance improvement and effective decisions [8].
- ā¢ Digital technologies and related skillsets will be a key enabler for the future. Knowledgeable resources will be in high demand across all business sectors.
- ā¢ Navigation systems are now available on your mobile device. This not only gives the driver step by step instructions, but also automatically reroutes when unexpected events occur. These scenarios could include accidents, weather, construction delays, law enforcement, etc. Real-time intelligence is now helping drivers optimize their routes and arrival times.
- ā¢ Although not widely adopted as of yet, autonomous vehicles are available and driving alongside humans on shared roadways. Autonomous capabilities are also starting to impact transportation and logistics, and related distribution trends.
- ā¢ Companies such as Uber and Lyft have turned information into a business, mapping available drivers to those who need rides. This not only helps reduce traffic and parking needs, but can also help improve safety on the roads.
- ā¢ Fleet-wide artificial intelligence is helping to optimize fuel efficiency and vehicle wear by better understanding driving habits and equipment condition.
2: The āSmart Manufacturingā north star vision
- ā¢ Environmental impactāminimize impact on the communities where we operate and in the markets we serve.
- ā¢ Health and safetyāzero harm to our employees and the markets we serve.
- ā¢ Risk managementāensure availability and integrity of supply.
- ā¢ Cost managementāmanagement of supply pricing/impact on customer pricing.
- ā¢ Resource managementāoptimization of supply resources, including energy, water, raw materials, people.
- ā¢ Productivityāincreasing production volume against baseline schedule/costs.
- ā¢ Asset healthāimproving critical asset uptime, reducing related maintenance costs, and extending asset life.
- ā¢ Transportation/logisticsāensuring ontime delivery and cost control.
- ā¢ Product qualityāzero returns/zero complaints.
- ā¢ Product demandāagility to meet evolving customer wants and needs.
- ā¢ Profitabilityāeffective decisions on when to enter/exit markets.
2.1: People and process culture enablers
- ā¢ Reboot of operational excellence/lean manufacturin...
Table of contents
- Cover image
- Title page
- Table of Contents
- Copyright
- Contributors
- Preface
- Chapter 1: Smart manufacturing: It's a journey, not a destination
- Chapter 2: Implementing smart manufacturing across an industrial organization
- Chapter 3: Industrie 4.0 and international perspective
- Chapter 4: Cyberinfrastructure for the democratization of smart manufacturing
- Chapter 5: The role of hardware and software in smart manufacturing
- Chapter 6: Measuring, managing, and transforming data for operational insights
- Chapter 7: The role of advanced process modeling in smart manufacturing
- Chapter 8: Industrial AI and predictive analytics for smart manufacturing systems
- Chapter 9: Computational framework for smart manufacturing via parametric optimization and control (PAROC)
- Chapter 10: A systems engineering-driven decomposition approach for large-scale industrial decision-making processes
- Chapter 11: Model-predictive safety: A new evolution in functional safety
- Chapter 12: Inferential modeling and soft sensors
- Chapter 13: A decision support framework for sustainable and smart manufacturing
- Chapter 14: Smart manufacturing pedagogy for the anthropocene
- Index