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This book systematically introduces the principles and latest developments in cycle time management. With the rise of artificial intelligence, numerous advanced information technologies, such as Industry 4.0, big data, edge computing, and explainable artificial intelligence, have emerged. Factory engineers are exploring opportunities to apply these technologies to enhance the efficiency and effectiveness of cycle time management. To address this, the book conducts a preliminary investigation and outlines several practical actions that can be implemented.
Factories worldwide are striving to reduce cycle times to increase their competitiveness and sustainability. Cycle time modeling and factor analysis are essential prerequisites, followed by accurate cycle time prediction and stringent control measures. Reducing cycle times can offer a competitive advantage in managing customer relationships. The successful strategies for shortening cycle times can also be applied to other product types or factories, a concept known as cycle time learning. All these efforts rely on comprehensive cycle time management activities. This book will be of use to professionals in industry as well as researchers and graduate students.
List of contents
Cycle time management.- Cycle time prediction.- Cycle time modelling and analysis.- Management support.- Cycle time reduction.
About the author
Dr. Chen has published a few papers on cycle time prediction and reduction in prestigious journals such as Applied Soft Computing, Journal of Intelligent Manufacturing, IEEE Transactions on Systems, Man, and Cybernetics, Fuzzy Sets and Systems, Computers & Industrial Engineering, etc. He has guest edited special issues on advanced manufacturing management topics in journals such as Journal of Intelligent Manufacturing, Robotics and Computer-Integrated Manufacturing, International Journal of Advanced Manufacturing Technology, International Journal of Intelligent Systems, etc. Dr. Chen is an IET fellow.
Summary
This book systematically introduces the principles and latest developments in cycle time management. With the rise of artificial intelligence, numerous advanced information technologies, such as Industry 4.0, big data, edge computing, and explainable artificial intelligence, have emerged. Factory engineers are exploring opportunities to apply these technologies to enhance the efficiency and effectiveness of cycle time management. To address this, the book conducts a preliminary investigation and outlines several practical actions that can be implemented.
Factories worldwide are striving to reduce cycle times to increase their competitiveness and sustainability. Cycle time modeling and factor analysis are essential prerequisites, followed by accurate cycle time prediction and stringent control measures. Reducing cycle times can offer a competitive advantage in managing customer relationships. The successful strategies for shortening cycle times can also be applied to other product types or factories, a concept known as cycle time learning. All these efforts rely on comprehensive cycle time management activities. This book will be of use to professionals in industry as well as researchers and graduate students.
