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Cambridge Service Alliance

developing new understanding and approaches to complex service systems

Studying at Cambridge

 

Business

The Cambridge Service Alliance brings together businesses and the University to improve the way high-performance complex services are designed, deployed and delivered.  Working directly with our partners - BAE Systems, Caterpillar, IBM, Pearson and Zoetis - the Alliance seeks to develop and deliver the tools, education and insights needed for the complex service providers of tomorrow.

Our growing membership brings greater diversity and provides new opportunities to test the ideas and thinking that the Alliance delivers. Learning across sectors and organisations provides a rich and rewarding environment, both for researchers and our partners. We would love to hear from you if you are interested in joining us on this exciting journey.

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Cambridge Service Alliance

Welcome to the Cambridge Service Alliance…

  • a unique global alliance between leading businesses and universities;
  • bringing together the world's leading firms and academics;
  • all of whom are devoted to delivering today the tools, education and insights needed for the complex service solutions of tomorrow.

Members of the Cambridge Service Alliance include BAE Systems, Caterpillar, IBM and the University of Cambridge.

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Webinar - Customer Loyalty Predictive Model

Jan 10, 2017

9 January 2017 - The Fallacy of the Net Promoter Score: Customer Loyalty Predictive Model - Mohamed Zaki

Webinar - Feedback from the Frontline

Dec 13, 2016

12 December 2016 - Feedback from the Frontline: Engaging front-line employees in service innovation - Florian Urmetzer

Ecosystems Value Framework Paper

Dec 12, 2016

The December Paper on 'The Ecosystem Value Framework: Supporting Managers to Understand Value Exchange between Core Businesses in Service Ecosystems', by Florian Urmetzer, Veronica Martinez and Andy Neely.

December 2016 Newsletter

Dec 01, 2016

December 2016 Alliance Newsletter

Classification of Noisy Data

Nov 28, 2016

November paper on 'Classification of Noisy Data: An Approach Based on Genetic Algorithms and Voronoi Tessellation' by Abdul Rauf Khan, Henrik Schiøler, Torben Knudsen, Murat Kulahci and Mohamed Zaki

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