Our research focuses on a central theme: how to design, analyse and improve processes.

This includes processes within the traditional manufacturing and service contexts, but also within health care, the office and museums. Other areas of interest include operations strategy, supply chain management, reputation management, cyber security and major programme management.

The group undertakes field work in the US, Europe and Asia, exploring everything from the manufacture of toys in China through to health care process improvements in the UK.

Members hold editorial positions with leading journals and their work has been published in the Journal of Operations Management, Journal of Service Research, Journal of Management Studies, and Social Science and Medicine.

Our research has won a number of awards, including the Shingo Research and Professional Publication Award, best paper and most cited awards at the Journal of Operations Management and the International Journal of Operations and Production Management, and an Advanced Institute of Management (AIM) Fellowship.


Mind the gap: Towards performance measurement beyond a plan-execute logic(opens in new window)

  • Journal article
  • International Journal of Project Management
  • Harvey Maylor,
  • Joana Geraldi,
  • Alexander Budzier,
  • Neil Turner,
  • Mark Johnson
Major Programme Management
Technology and Operations Management
  • Anette Mikes,
  • Steve New
Professional Service Firms
Health Care
Major Programme Management
Technology and Operations Management
  • S Dutta,
  • L Yang,
  • SY Liu,
  • CM Liu,
  • LJ Liaw,
  • S Som,
  • A Mohapatra,
  • R Sankar,
  • WC Lin,
  • YC Chao
Technology and Operations Management
  • Bent Flyvbjerg,
  • Alexander Budzier,
  • Jong Soek Lee,
  • Mark Keil,
  • Daniel Lunn,
  • Dw Bester
Major Programme Management
Technology and Operations Management
  • Bent Flyvbjerg,
  • Alexander Budzier,
  • Ricky LAU Chun-kit,
  • Karlene Agard,
  • Andreas Leed
Major Programme Management
Technology and Operations Management
See more publications

Data-Driven Decisions Lab (3DL)

With more data available and computational performance always improving, the number of machine learning models has increased in the past ten years. However, we need to better understand how these models affect real-world decision making.

The Data-Driven Decisions Lab (3DL) aims to address this challenge.


The intellectual diversity of our group is built upon a shared commitment that research should be based on a broad, multi-disciplinary intellectual base. 

Read profiles for our academics and researchers