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Cambridge Service Alliance

developing new understanding and approaches to complex service systems

Studying at Cambridge

 

Dr Jinchen Hou

Dr Jinchen Hou

Research Associate


Biography:

Research interests

  • Servitization
  • Outcome-based contracts / Performance-based contracts
  • Social capital and supplier-customer relationship

Background

Jingchen got her Bachelor's Degree of Civil Engineering in Tongji University, China, 2010. After that, she completed an MSc study in Imperial College London in 2011, majoring in Concrete Structures and Business Management. She joined the Cambridge Service Alliance in January 2012 for the pursuit of a PhD. Her research interests include servitization, outcome-based contracts, risks and social capital. Her PhD research examined the effects of social capital on risks of outcome-based contracts from the provider’s perspective. After she obtained the PhD degree in 2016, she has been working as a research associate in Cambridge Service Alliance, and is involved in a serial of research projects such as the Manufacturing Metrics project.

Keywords

  • Servitization
  • Supplier-Customer Relationship
  • Outcome-Based Contracts
  • Risk

Key Publications

Publications:

  • Investigating risks of outcome-based service contracts from a provider’s perspective. Jingchen Hou and Andy Neely. Submitted to International Journal of Production Research. Under review.
  • Supplying innovation: unlocking innovation in the supply chain. Jingchen Hou, Keith Wishart, Jonathan Baker-Brian, Claire Vine. A white paper in collaboration with IBM.

  • Case studies: analysing the effects of social capital on risks taken by suppliers in outcome-based contracts, Jingchen Hou and Andy Neely, The 21st EurOMA Conference, Palermo, Italy, June 2014.
  • Effects of social capital on risks of performance-based contracts from suppliers’ perspective, Jingchen Hou and Andy Neely, The 27th annual British Academy of Management conference, Liverpool, UK, September 2013.
  • Barriers of servitization: results of a systematic literature review, Jingchen Hou and Andy Neely, The Spring Servitization Conference 2013, Birmingham, UK, May 2013.

Webinars and Podcasts:

Supplying Innovation: Unlocking Innovative Behaviours in the Supply Chain. 

https://www.youtube.com/watch?v=C4LGupjnJ14&feature=youtu.be

https://cambridgeservicealliance.eng.cam.ac.uk/resources/Podcasts

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 - High-Quality Prediction Intervals for Deep Learning

Jun 11, 2018

11 June 2018 - High-Quality Prediction Intervals for Deep Learning: A Distribution-Free, Ensembled Approach - by Tim Pearce, Mohamed Zaki, Alexandra Brintrup, Andy Neely

Theme Update - Blockchains in Smart Services

Jun 06, 2018

In May, Veronica and her team worked very hard on the first practical phase of the project – the Codification of the Blockchain Prototype for the CAT/Perkins’ Supply Chain.

Project Update - OMMS: the Wireless Micro-Factory that will Treat People with Cancer

Jun 06, 2018

The OMMS micro-factory project lead by Dr Veronica Martinez made a great progress this month.

June 2018 Newsletter

Jun 04, 2018

Find out what the Cambridge Service Alliance have been doing and how you can get involved.

Rare-events classification: An approach based on Genetic Algorithm and Voronoi Tessellation

May 31, 2018

by Abdul Rauf Khan, Mohamed Zaki, Henrik Schiøler, and Murat Kulahci. The paper was accepted at 'The 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)', a leading international conference in the areas of knowledge discovery and data mining (KDD). In this paper, Mohamed and co-authors propose a novel strategy for data mining based on partitioning of the feature space through Voronoi tessellation and Genetic Algorithm, where the latter is applied to solve a combinatorial optimization problem.

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