Supply Chain Localization in the Semiconductor Industry

Chapter 1. Explainable Artificial Intelligence (XAI).- Chapter 2. Artificial Intelligence (AI) Applications in Job Sequencing and Scheduling.- Chapter 3. XAI Applications to Job sequencing and Scheduling.- Chapter 4. Explaining Artificial Neural Network and Deep Learning Applications in Job Sequencing and Scheduling.- Chapter 5. Explaining Genetic Algorithm and Other Bio-inspired Algorithm Applications in Job Sequencing and Scheduling.- Chapter 6. Tailoring Scheduling Rules Using XAI.- Chapter 7. XAI-enabled Edge Computing Application in Job Sequencing and Scheduling.

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.