Technology Innovation Research Cluster

The Technology Innovation Research Cluster combines multidisciplinary and impactful research in technology and digital innovation in global operations and supply chains.

Our areas of expertise are: 

We collaborate with national and international organisations such as the NHS, Rolls Royce, Siemens and Pierlite Australia. We also work with colleagues from national and international institutions such as the University of Technology Sydney and Pennsylvania State University.

Our aims

This research cluster aims to bring together industry, policymakers, academia and the community to create efficient, inclusive, resilient and sustainable operations and supply chains. We are developing high-quality, multidisciplinary and impactful research in the area of technology and digital innovation in global operations and supply chains.

Research Cluster Team

Our research

We are currently working on a research project in collaboration with Derby-based Alertive Company and the NHS entitled “Evaluating the impact of implementing a new digital communication platform on the performance of healthcare professionals (HCPs) and communication effectiveness within the hospital." This project is part of a digital health transformation project for hospitals and NHS trusts in the UK. The University and the European Regional Development Fund (ERDF) launched this initiative to provide fully and partially funded support to organisations to improve their productivity and market competitiveness.

We also promote significant technology innovation in students, staff development, research productivity, regional economic development, and international collaboration from five continents. For instance, we collaborate on Knowledge Exchange Partnerships (KTPs.) An example includes a project with international company Gerald Lighting (based in Sydney, Australia) to improve their supply chain through digital technology innovation.

Join us

Whether you would like to join the research cluster team, apply for a PhD or collaborate on research, please contact cluster lead Dr Jay Daniel at


  • Daniel, J., Maroun, E., (2022), Exploring Blockchain technology and sustainability in supply chain, 12th European Decision Sciences Conference. European Decision Sciences Institute (EDSI), Dublin, Ireland, 29 May - 1 June 2022 
  • González-Aleu, F., Hernandez, J. V., Ramirez, R., Linares, C. M., Peinado, J. A., & Daniel, J. (2022). Strategic planning for repurposing kitchen equipment production operations during COVID-19 pandemic. Operations Management Research, 15(3-4), 1241-1256. 
  • Daniel, J., (2022), Blockchain Technology: How to Unblock the Chain! China-Britain Business Council,  
  • Stapleton, D., Daniel, J., Nandialath, MA., Franklin, D., (2021) The Reciprocal Relationship between Containers Overboard and Climate Change, Journal of Transportation Law, Logistics, and Policy, 88(1), 51-65 
  • Rajeev, Rohit and Daniel, Jay (2021) Supply Chain Mapping and Visualisation of UK Rail Sector, British Academy of Management, UK. 
  • Daniel, J., & Merigó, J. M. (2021). Developing a new multidimensional model for selecting strategic plans in balanced scorecard. Journal of Intelligent & Fuzzy Systems, 40(2), 1817-1826. 
  • Maroun, A.E., Daniel, J., Zowghi, D., Talaei-Khoei, A., (2019) Blockchain in Supply Chain Management: Australian Manufacturer Case Study. In: Beheshti A., Dong H., Zhang W. (eds) Service Research and Innovation. ASSRI 2017. Lecture Notes in Business Information Processing, Springer, Cham 
  • Hanson-New, C., & Daniel, J. (2019) The application of big data and AI in the upstream supply chain, Logistics Research Network, The Chartered Institute of Logistics and Transport in the UK. 
  • Ho, T. H. D., Daniel, J., Nadeem, S. P., Garza-Reyes, J. A., & Kumar, V. (2018, December). Improving the reliability of warehouse operations in the 3PL Industry: An Australian 3PL case study. In 2018 International Conference on Production and Operations Management Society (POMS) (pp. 1-7). IEEE. 
  • Daniel J, Naderpour M, Lin C. (2018) A Fuzzy Multilayer Assessment Method for EFQM. IEEE Transactions on Fuzzy Systems. 2019;27(6):1252-62.