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Cambridge Service Alliance

developing new understanding and approaches to complex service systems

Studying at Cambridge

 

Chara Makri

Biography:

Research interests

Chara joined the Cambridge Service Alliance team in October 2014 as a doctoral student under the supervision of Professor Andy Neely. Her research interests lie in operations management with a focus on services. The objective of her PhD research is to investigate how an understanding of Through-Life Accountability can improve safety and examine the human factors that can lead to failures and in turn, accidents within a service environment. Chara is interested in engaging with researchers and practitioners in order to understand more about why these failures happen and how they can be prevented. The ultimate goal is to equip servitized manufacturers with a unique tool in order to face such challenges and support them in the challenging path towards the services of the future. This research is conducted in partnership with BAE Systems and is sponsored by EPSRC.  

Background

Chara holds a BSc in Management Science and Technology from the Athens University of Economics and Business, Greece and an MPhil with Distinction in Industrial Systems, Manufacturing and Management from the University of Cambridge. Prior coming to Cambridge, Chara has worked in operations (mainly Demand Planning) for General Mills, Cadbury and Kraft.

Contact Details

E: cam221@cam.ac.uk

LinkedIn: Chara Makri

Twitter: @MakriChara

Keywords

  • Servitization
  • Shift to Services
  • Risk
  • Through-Life Accountability

Key Publications

Conference Publications

Makri, C. and Neely, A., 2015. “Through-life accountability: Managing complex services”. 22nd EurOMA Conference, Neuchatel, Switzerland.

Makri, C. and Neely, A. (2016), ‘Critical Incidents in Complex Service Contracts: Safety Challenges and Means of Prevention’, 23rd EurOMA Conference, Trondheim, Norway.

Makri, C. and Neely, A. (2017) 'Barriers and Facilitators to Incident Reporting in Servitized Manufacturers', 24th EurOMA Conference, Edinburgh, Scotland. 

Other Publications

Cambridge Service Alliance, March 2015 Paper, 'Through-Life Accountability: Managing Complex Services' external link by Chara Makri and Andy Neely

Cambridge Service Alliance, July 2016 Paper, ‘Critical incidents in complex service contracts: Safety challenges and means of prevention’ by Chara Makri and Andy Neely.

Cambridge Service Alliance, July 2017 Paper, 'Barriers and Facilitators to Incident Reporting in Servitized Manufacturers' by Chara Makri And Andy Neely.

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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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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