Dr Sarah Marshall

profile image


Phone: +64 9 921 9999 ext 5414

Email: sarah.marshall@aut.ac.nz

Physical Address:
School of Computer & Mathematical Sciences
AUT Tower, Level 1
2-14 Wakefield St
Auckland 1010
Postal Address:

AUT School of Computer & Mathematical Sciences
Private Bag 92006
Auckland 1142
New Zealand

ORCID: ORCID logo  https://orcid.org/http://orcid.org/0000-0002-6220-2105


Postgraduate Diploma in Advanced Academic Studies (Academic Practice), University of Strathclyde, Glasgow, United Kingdom. 2011 - 2014.
PhD in Management Science, University of Edinburgh, United Kingdom. 2012
MSc in Statistics and Operations Research, Victoria University of Wellington, New Zealand. 2008
BSc in Operations Research and Psychology, Victoria University of Wellington, New Zealand.  2005
BCA in Economics, Victoria University of Wellington, New Zealand.  2005

Memberships and Affiliations:

Operational Research Society (United Kingdom)

Operations Research Society of New Zealand, Council Member, 2014 - present

Member of the Royal Society of New Zealand 

New Zealand Analytics Forum 


Dr Sarah Marshall joined AUT as a Lecturer in February 2014. Sarah completed a Bachelor of Commerce and Administration in Economics and a Bachelor of Science in Psychology and Operations Research at Victoria University of Wellington. After graduating, she worked in the Australian stockbroking industry before returning to New Zealand to complete a Master of Science in Statistics and Operations Research at Victoria University of Wellington. Sarah completed her PhD in Management Science on the application of deterministic and stochastic models to product recovery systems at the University of Edinburgh in 2012. Sarah taught in the Department of Management Science at the University of Strathclyde in Glasgow for three years before beginning her current position at AUT.  

Sarah is a member of the Mathematical Sciences Research Group and the Data Science Research Group.  Her research focuses on the use of stochastic modelling to address problems of interest to business and industry. Examples of application areas include remanufacturing systems, inventory management and warranty costs analysis.

Sarah has a keen interest in analytics and regularly attends the NZ Analytics Forum.  Sarah is the Industry Liaison for the Master of Analytics and welcomes enquiries from organisations interested in engaging with AUT through student projects. Sarah is a member of the ORSNZ Council and was co-chair of the 2016 Joint NZSA+ORSNZ Conference.

Teaching Areas:

  • STAT500 Applied Statistics
  • STAT600 Probability
  • STAT700 Applied Stochastic Models
  • STAT800 Stochastic Modelling
  • STAT804 Optimization and Operations Research

Research Areas:

  • Stochastic Modelling
  • Markov Decision Processes
  • Renewal Processes
  • Product Recovery Systems
  • Inventory Management
  • Remanufacturing
  • Warranty Cost Analysis

Current Research Projects:

Warranty Cost Analysis (with Stefanka Chukova, Richard Arnold, Yu Hayakawa)
Quality in Product Recovery Systems (with Thomas Archibald)
Traceability in the Dairy Industry (with PhD student Melissa Welsh)


Google Scholar: https://scholar.google.com.au/citations?user=0-4vXgoAAAAJ&hl=en

Journal Articles

Marshall, S., & Archibald, TW. (2015). Substitution in hybrid remanufacturing. Procedia CIRP, 26, 583-588. doi:10.1016/j.procir.2014.07.073

Marshall, S. E., & Chukova, S. (2010). On analysing warranty data from repairable items. Quality and Reliability Engineering International, 26(1), 43-52. doi:10.1002/qre.1032



Marshall, S. (2012). Refuse or Reuse: Managing the Quality of Returns in Product Recovery Systems. (Doctoral Thesis, University of Edinburgh, Edinburgh, United Kingdom). Retrieved from http://hdl.handle.net/1842/6415

Marshall, S. (2008). On the analysis of reliability data. (Master's Thesis/Postgraduate Dissertation, Victoria University of Wellington, Wellington, New Zealand). Available online: Available online


Working Papers

Welsh, M., Marshall, S., & Noy, I (2016) Modelling New Zealand milk: From the farm to the factory. School of Economics and Finance Working Paper Series, Victoria University of Wellington. Available online: http://www.victoria.ac.nz/sef/research/pdf/2016-papers/SEF-Working-Paper18-2016.pdf


Marshall, S.E. & Archibald T.W. (2016) Lot-sizing for a Product Recovery System with Quality-dependent Recovery Channels. Computers and Industrial Engineering (under review)