Supply Chain Collaboration: Roles of Interorganizational Systems, Trust, and Collaborative Culture - Hardcover

Cao, Mei; Zhang, Qingyu

Hardcover

ISBN 10: 1447145909 ISBN 13: 9781447145905

Publisher: Springer, 2012

Synopsis

Describing novel mathematical concepts for recommendation engines, Realtime Data Mining: Self-Learning Techniques for Recommendation Engines features a sound mathematical framework unifying approaches based on control and learning theories, tensor factorization, and hierarchical methods. Furthermore, it presents promising results of numerous experiments on real-world data. The area of realtime data mining is currently developing at an exceptionally dynamic pace, and realtime data mining systems are the counterpart of today's "classic" data mining systems. Whereas the latter learn from historical data and then use it to deduce necessary actions, realtime analytics systems learn and act continuously and autonomously. In the vanguard of these new analytics systems are recommendation engines. They are principally found on the Internet, where all information is available in realtime and an immediate feedback is guaranteed. This monograph appeals to computer scientists and specialists in machine learning, especially from the area of recommender systems, because it conveys a new way of realtime thinking by considering recommendation tasks as control-theoretic problems. Realtime Data Mining: Self-Learning Techniques for Recommendation Engines will also interest application-oriented mathematicians because it consistently combines some of the most promising mathematical areas, namely control theory, multilevel approximation, and tensor factorization.

About this title

About the Author:

Dr. Mei Cao is an associate professor at the University of Wisconsin-Superior. She is certified in Transportation and Logistics (CTL) by AST&L. She has publications in various academic journals such as the Journal of Operations Management, the European Journal of Operational Research, the International Journal of Production Research, the International Journal of Production Economics, the International Journal of Operations and Production Management, the Journal of Systems Science and Systems Engineering, Information & Management, Industrial Management & Data Systems, the International Journal of Product Development, and the International Journal of Services Technology and Management. She serves on the editorial boards of the International Journal of Operations Research and Information Systems, and the International Journal of Information Technology Project Management. Her research interests include supply chain management, transportation and logistics, flexibility, and inter-organizational information systems. Her research has been funded by the National Center for Freight and Infrastructure Research and Education under the sponsorship of the U.S. Department of Transportation.

Dr. Qingyu Zhang is an associate professor at Arkansas State University. He is an APICS Certified Fellow in Production and Inventory Management. He is also certified MCSD, MCSE, and MCDBA by Microsoft. He has published in the European Journal of Operational Research, the International Journal of Production Research, the Journal of Operations Management, the International Journal of Production Economics, the International Journal of Operations and Production Management, the International Journal of Logistics Management, the Journal of Systems Science and Systems Engineering, Kybernetes: International Journal of Systems and Cybernetics, Industrial Management & Data Systems, the International Journal of Product Development, the International Journal of Quality and Reliability Management, the European Journal of Innovation Management, and the International Journal of Information Technology and Decision Making. Dr. Zhang’s research interests are supply chain management, value chain flexibility, e-commerce, product development, and data mining. He serves on the editorial boards of the Journal of Computer Information Systems, the Information Resource Management Journal, the International Journal of Integrated Supply Management, the International Journal of Data Analysis Techniques and Strategy, and the International Journal of Information Technology Project Management.

 

.

From the Back Cover:

To survive and thrive in the competition, firms have strived to achieve greater supply chain collaboration to leverage the resources and knowledge of suppliers and customers.  Internet based technologies, particularly interorganizational systems, further extend the firms’ opportunities to strengthen their supply chain partnerships and share real-time information to optimize their operations.  Supply Chain Collaboration: Roles of Interorganizational Systems, Trust, and Collaborative Culture explores the nature and characteristics, antecedents, and consequences of supply chain collaboration from multiple theoretical perspectives. 

Supply Chain Collaboration: Roles of Interorganizational Systems, Trust, and Collaborative Culture conceptualizes supply chain collaboration as seven interconnecting elements including information sharing, incentive alignment, goal congruence, decision synchronization, resource sharing, as well as communication and joint knowledge creation. These seven components define the occurrence of collaborative efforts and allow us to explain supply chain collaboration more precisely. Collaborative advantages are also divided into five components to capture the joint competitive advantages and benefits among supply chain partners.

The definitions and measures developed here examine some central issue surrounding supply chain development but this is also followed up with real-life managerial practicalities. This balance of theory and practical application makes Supply Chain Collaboration: Roles of Interorganizational Systems, Trust, and Collaborative Culture a strong resource for industry practitioners and researchers alike.

About this edition

• Publisher: Springer
• Publication date: 2012
• ISBN 10: 1447145909
• ISBN 13: 9781447145905
• Binding: Hardcover
• Number of pages: 212