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Cambridge Service Alliance

developing new understanding and approaches to complex service systems

Studying at Cambridge

 

What We Do

The Cambridge Service Alliance seeks to be the world’s premier industry-University consortium devoted to understanding complex services.

 For partners the Alliance offers:

  • Access to leading service research and best practice that helps partners advance their own business models internally and externally
  • Ability to influence and commission new relevant research for the Alliance
  • Access to education derived from the Alliance
  • Knowledge sharing with an exclusive club and associated networking opportunities
  • Brand association opportunities
  • Practical tools and techniques that will allow partner organisations to exploit commercially the intellectual property developed through the Alliance.

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.

RSS Feed Latest news

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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